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Sampling Fraction. In probability sampling, the sampling fraction is the (known) probability with which cases in the population are selected into the sample. For example, if we were to take a simple random sample with a sampling fraction of 1/10,000 from a population of 1,000,000 cases, each case would have a 1/10,000 probability of being selected into the sample, which will consist of approximately 1/10,000 * 1,000,000 = 100 observations.
Scalable Software Systems. Software (e.g., a data base management system, such as MS SQL Server or Oracle) that can be expanded to meet future requirements without the need to restructure its operation (e.g., split data into smaller segments) to avoid a degradation of its performance. For example, a scalable network allows the network administrator to add many additional nodes without the need to redesign the basic system. An example of a non-scalable architecture is the DOS directory structure (adding files will eventually require splitting them into subdirectories). See also Enterprise-Wide Systems.
Scaling. Altering original variable values (according to a specific function or an algorithm) into a range that meet particular criteria (e.g., positive numbers, fractions, numbers less than 10E12, numbers with a large relative variance).
Scatterplot, 2D. The scatterplot visualizes a relation (correlation) between two variables X and Y (e.g., weight and height). Individual data points are represented in two-dimensional space (see below), where axes represent the variables (X on the horizontal axis and Y on the vertical axis).
The two coordinates (X and Y) that determine the location of each point correspond to its specific values on the two variables. See also, Data Reduction.
Scatterplot, 2D - Categorized Ternary Graph. The points representing the proportions of the component variables (X, Y, and Z) in a ternary graph are plotted in a 2-dimensional display for each level of the grouping variable (or user-defined subset of data). One component graph is produced for each level of the grouping variable (or user-defined subset of data) and all the component graphs are arranged in one display to allow for comparisons between the subsets of data (categories). See also, Data Reduction.
Scatterplot, 2D - Double-Y. This type of scatterplot can be considered to be a combination of two multiple scatterplots for one X-variable and two different sets (lists) of Y-variables. A scatterplot for the X-variable and each of the selected Y-variables will be plotted, but the variables entered into the first list (called Left-Y) will be plotted against the left-Y axis, whereas the variables entered into the second list (called Right-Y) will be plotted against the right-Y axis. The names of all Y-variables from the two lists will be included in the legend followed either by the letter (L) or (R), denoting the left-Y and right-Y axis, respectively.
The Double-Y scatterplot can be used to compare images of several correlations by overlaying them in a single graph. However, due to the independent scaling used for the two list of variables, it can facilitate comparisons between variables with values in different ranges. See also, Data Reduction.
Scatterplot, 2D - Frequency. Frequency scatterplots display the frequencies of overlapping points between two variables in order to visually represent data point weight or other measurable characteristics of individual data points.
See also, Data Reduction.
Scatterplot, 2D - Multiple. Unlike the regular scatterplot in which one variable is represented by the horizontal axis and one by the vertical axis, the multiple scatterplot consists of multiple plots and represents multiple correlations: one variable (X) is represented by the horizontal axis, and several variables (Y's) are plotted against the vertical axis. A different point marker and color is used for each of the multiple Y-variables and referenced in the legend so that individual plots representing different variables can be discriminated in the graph.
The Multiple scatterplot is used to compare images of several correlations by overlaying them in a single graph that uses one common set of scales (e.g., to reveal the underlying structure of factors or dimensions in Discriminant Function Analysis). See also, Data Reduction.
Scatterplot, 2D - Regular. The regular scatterplot visualizes a relation between two variables X and Y ( e.g., weight and height). Individual data points are represented by point markers in two-dimensional space, where axes represent the variables. The two coordinates (X and Y) that determine the location of each point correspond to its specific values on the two variables. If the two variables are strongly related, then the data points form a systematic shape (e.g., a straight line or a clear curve). If the variables are not related, then the points form an irregular "cloud" (see the categorized scatterplot below for examples of both types of data sets).
Fitting functions to scatterplot data helps identify the patterns of relations between variables (see example below).
For more examples of how scatterplot data helps identify the patterns of relations between variables, see Outliers and Brushing. See also, Data Reduction.
Scatterplot, 3D. 3D Scatterplots visualize a relationship between three or more variables, representing the X, Y, and one or more Z (vertical) coordinates of each point in 3-dimensional space (see graph below).
See also, 3D Scatterplot - Custom Ternary Graph, Data Reduction and Data Rotation (in 3D space).
Scatterplot, 3D - Raw Data. An unsmoothed surface (no smoothing function is applied) is drawn through the points in the 3D scatterplot. See also, Data Reduction.
Scatterplot, 3D - Ternary Graph. In this type of ternary graph, the triangular coordinate systems are used to plot four (or more) variables (the components X, Y, and Z, and the responses V1, V2, etc.) in three dimensions (ternary 3D scatterplots or surface plots). Here, the responses (V1, V2, etc.) associated with the proportions of the component variables (X, Y, and Z) in a ternary graph are plotted as the heights of the points. See also, Data Reduction.
Scatterplot Smoothers. In 2D scatterplots, various smoothing methods are available to fit a function through the points to best represent (summarize) the relationship between the variables.
Scheffe's Test. This post hoc test can be used to determine the significant differences between group means in an analysis of variance setting. Scheffe's test is considered to be one of the most conservative post hoc tests (for a detailed discussion of different post hoc tests, see Winer, Michels, & Brown (1991). For more details, see General Linear Models. See also, Post Hoc Comparisons. For a discussion of statistical significance, see Elementary Concepts.
Score Statistic. This statistic is used to evaluate the statistical significance of parameter estimates computed via maximum likelihood methods. It is also sometimes called the efficient score statistic. The test is based on the behavior of the log-likelihood function at the point where the respective parameter estimate is equal to 0.0 (zero); specifically, it uses the derivative (slope) of the log-likelihood function evaluated at the null hypothesis value of the parameter (parameter = 0.0). While this test is not as accurate as explicit likelihood-ratio test statistics based on the ratio of the likelihoods of the model that includes the parameter of interest, over the likelihood of the model that does not, its computation is usually much faster. It is therefore the preferred method for evaluating the statistical significance of parameter estimates in stepwise or best-subset model building methods. An alternative statistic is the Wald statistic.
Scree Plot, Scree Test. The eigenvalues for successive factors can be displayed in a simple line plot. Cattell (1966) proposed that this scree plot can be used to graphically determine the optimal number of factors to retain.
The scree test involves finding the place where the smooth decrease of eigenvalues appears to level off to the right of the plot. To the right of this point, presumably, we find only "factorial scree" – "scree" is the geological term referring to the debris that collects on the lower part of a rocky slope. Thus, no more than the number of factors to the left of this point should be retained.
For more information on procedures for determining the optimal number of factors to retain, see the section on Reviewing the Results of a Principal Components Analysis in Factor Analysis and How Many Dimensions to Specify in Multi-dimensional Scaling.
S.D. Ratio. In a regression problem, the ratio of the prediction error standard deviation to the original output data standard deviation. A lower S.D. ratio indicates a better prediction. This is equivalent to one minus the explained variance of the model. See Multiple Regression, Neural Networks.
Semi-Partial (or Part) Correlation. The semi-partial or part correlation is similar to the partial correlation statistic. Like the, partial correlation, it is a measure of the correlation between two variables that remains after controlling for (i.e., "partialling" out) the effects of one or more other predictor variables. However, while the squared partial correlation between a predictor X1 and a response variable Y can be interpreted as the proportion of (unique) variance accounted for by X1, in the presence of other predictors X2, ... , Xk, relative to the residual or unexplained variance that cannot be accounted for by X2, ... , Xk, the squared semi-partial or part correlation is the proportion of (unique) variance accounted for by the predictor X1, relative to the total variance of Y. Thus, the semi-partial or part correlation is a better indicator of the "practical relevance" of a predictor, because it is scaled to (i.e., relative to) the total variability in the dependent (response) variable.
See also Correlation, Spurious Correlations, partial correlation, Basic Statistics, Multiple Regression, General Linear Models, General Stepwise Regression, Structural Equation Modeling (SEPATH).
SEMMA. See Models for Data Mining. See also, Data Mining Techniques.
Sensitivity Analysis (in Neural Networks). A sensitivity analysis indicates which input variables are considered most important by that particular neural network. Sensitivity analysis can be used purely for informative purposes, or to perform input pruning.
Sensitivity analysis can give important insights into the usefulness of individual variables. It often identifies variables that can be safely ignored in subsequent analyses, and key variables that must always be retained. However, it must be deployed with some care, for reasons that are explained below.
Input variables are not, in general, independent; that is, there are interdependencies between variables. Sensitivity analysis rates variables according to the deterioration in modeling performance that occurs if that variable is no longer available to the model. In so doing, it assigns a single rating value to each variable. However, the interdependence between variables means that no scheme of single ratings per variable can ever reflect the subtlety of the true situation.
Consider, for example, the case where two input variables encode the same information (they might even be copies of the same variable). A particular model might depend wholly on one, wholly on the other, or on some arbitrary combination of them. Then sensitivity analysis produces an arbitrary relative sensitivity to them. Moreover, if either is eliminated the model may compensate adequately because the other still provides the key information. It may therefore rate the variables as of low sensitivity, even though they might encode key information. Similarly, a variable that encodes relatively unimportant information, but is the only variable to do so, may have higher sensitivity than any number of variables that mutually encode more important information.
There may be interdependent variables that are useful only if included as a set. If the entire set is included in a model, they can be accorded significant sensitivity, but this does not reveal the interdependency. Worse, if only part of the interdependent set is included, their sensitivity will be zero, as they carry no discernable information.
In summary, sensitivity analysis does not rate the "usefulness" of variables in modeling in a reliable or absolute manner. We must be cautious in the conclusions we draw about the importance of variables. Nonetheless, in practice it is extremely useful. If a number of models are studied, it is often possible to identify key variables that are always of high sensitivity, others that are always of low sensitivity, and "ambiguous" variables that change ratings and probably carry mutually redundant information.
How does sensitivity analysis work? Each input variable is treated in turn as if it were "unavailable" (Hunter, 2000). There is a missing value substitution procedure, which is used to allow predictions to be made in the absence of values for one or more inputs. To define the sensitivity of a particular variable, v, we first run the network on a set of test cases, and accumulate the network error. We then run the network again using the same cases, but this time replacing the observed values of v with the value estimated by the missing value procedure, and again accumulate the network error.
Given that we have effectively removed some information that presumably the network uses (i.e. one of its input variables), we would reasonably expect some deterioration in error to occur. The basic measure of sensitivity is the ratio of the error with missing value substitution to the original error. The more sensitive the network is to a particular input, the greater the deterioration we can expect, and therefore the greater the ratio.
If the ratio is one or lower, making the variable "unavailable" either has no effect on the performance of the network, or actually enhances it. Once sensitivities have been calculated for all variables, they may be ranked in order.
Sequential Contour Plot, 3D. This contour plot presents a 2-dimensional projection of the spline-smoothed surface fit to the data (see 3D Sequential Surface Plot. Successive values of each series are plotted along the X-axis, with each successive series represented along the Y-axis.
Sequential/Stacked Plots. In this type of graph, the sequence of values from each selected variable is stacked on one another.
Sequential/Stacked Plots, 2D - Area. The sequence of values from each selected variable will be represented by consecutive areas stacked on one another in this type of graph.
Sequential/Stacked Plots, 2D - Column. The sequence of values from each selected variable will be represented by consecutive segments of vertical columns stacked on one another in this type of graph.
Sequential/Stacked Plots, 2D - Lines. The sequence of values from each selected variable will be represented by consecutive lines stacked on one another in this type of graph.
Sequential/Stacked Plots, 2D - Mixed Line. In this type of graph, the sequences of values of variables selected in the first list will be represented by consecutive areas stacked on one another while the sequences of values of variables selected in the second list will be represented by consecutive lines stacked on one another (over the area representing the last variable from the first list).
Sequential/Stacked Plots, 2D - Mixed Step. In this type of graph, the sequences of values of variables selected in the first list will be represented by consecutive step areas stacked on one another while the sequences of values of variables selected in the second list will be represented by consecutive step lines stacked on one another (over the step area representing the last variable from the first list).
Sequential/Stacked Plots, 2D - Step. The sequence of values from each selected variable will be represented by consecutive step lines stacked on one another in this type of graph.
Sequential/Stacked Plots, 2D - Step Area. The sequence of values from each selected variable will be represented by consecutive step areas stacked on one another in this type of graph.
Sequential Surface Plot, 3D. In this sequential plot, a spline-smoothed surface is fit to each data point. Successive values of each series are plotted along the X-axis, with each successive series represented along the Y-axis.
Sets of Samples in Quality Control Charts. While monitoring an ongoing process, it often becomes necessary to adjust the center line values or control limits, as those values are being refined over time. Also, we may want to compute the control limits and center line values from a set of samples that are known to be in control, and apply those values to all subsequent samples. Thus, each set is defined by a set of computation samples (from which various statistics are computed, e.g., sigma, means, etc.) and a set of application samples (to which the respective statistics, etc. are applied). Of course, the computation samples and application samples can be (and often are) not the same. To reiterate, we may want to estimate sigma from a set of samples that are known to be in control (the computation set), and use that estimate for establishing control limits for all remaining and new samples (the application set).
Note that each sample must be uniquely assigned to one application set; in other words, each sample has control limits based on statistics (e.g., sigma) computed for one particular set. The assignment of application samples to sets proceeds in a hierarchical manner, i.e., each sample is assigned to the first set where it "fits" (where the definition of the application sample set would include the respective sample). This hierarchical search always begins at the last set that the user specified, and not with the all-samples set. Hence, if the user-specified sets encompass all valid samples, the default all-samples set will actually become empty (since all samples will be assigned to one of the user-defined sets).
Shapiro-Wilk W Test. The Shapiro-Wilk W test is used in testing for normality. If the W statistic is significant, then the hypothesis that the respective distribution is normal should be rejected. The Shapiro-Wilk W test is the preferred test of normality because of its good power properties as compared to a wide range of alternative tests (Shapiro, Wilk, & Chen, 1968). Some software programs implement an extension to the test described by Royston (1982), which allows it to be applied to large samples (with up to 5000 observations). See also Kolmogorov-Smirnov test and Lilliefors test.
Shewhart Control Charts. This is a standard graphical tool widely used in statistical Quality Control. The general approach to quality control charting is straightforward: We extract samples of a certain size from the ongoing production process. We then produce line charts of the variability in those samples, and consider their closeness to target specifications. If a trend emerges in those lines, or if samples fall outside pre-specified limits, then the process is declared to be out of control and the operator will take action to find the cause of the problem. These types of charts are sometimes also referred to as Shewhart control charts (named after W. A. Shewhart who is generally credited as being the first to introduce these methods; see Shewhart, 1931). For additional information, see also Quality Control charts; Assignable causes and actions.
Short Run Control Charts. The short run quality control chart , for short production runs, plots transformations of the observations of variables or attributes for multiple parts, each of which constitutes a distinct "run," on the same chart. The transformations rescale the variable values of interest such that they are of comparable magnitudes across the different short production runs (or parts). The control limits computed for those transformed values can then be applied to determine if the production process is in control, to monitor continuing production, and to establish procedures for continuous quality improvement.
Shuffle, Back Propagation (in Neural Networks). Presenting training cases in a random order on each epoch to prevent various undesirable effects that can otherwise occur (such as oscillation and convergence to local minima). See, Neural Networks.
Shuffle Data (in Neural Networks). Randomly assigning cases to the training and verification sets, so that these are (as far as possible) statistically unbiased. See, Neural Networks.
Sigma Restricted Model. A sigma restricted model uses the sigma-restricted coding to represent effects for categorical predictor variables in general linear models and generalized linear models. To illustrate the sigma-restricted coding, suppose that a categorical predictor variable called Gender has two levels (i.e., male and female). Cases in the two groups would be assigned values of 1 or -1, respectively, on the coded predictor variable, so that if the regression coefficient for the variable is positive, the group coded as 1 on the predictor variable will have a higher predicted value (i.e., a higher group mean) on the dependent variable, and if the regression coefficient is negative, the group coded as -1 on the predictor variable will have a higher predicted value on the dependent variable. This coding strategy is aptly called the sigma-restricted parameterization, because the values used to represent group membership (1 and -1) sum to zero. See also, categorical predictor variables, design matrix; or General Linear Models.
Sigmoid Function. An S-shaped curve, with a near-linear central response and saturating limits. See also, logistic function and hyperbolic tangent function.
Signal Detection Theory (SDT). Signal detection theory (SDT) is an application of statistical decision theory used to detect a signal embedded in noise. SDT is used in psychophysical studies of detection, recognition, and discrimination, and in other areas such as medical research, weather forecasting, survey research, and marketing research.
A general approach to estimating the parameters of the signal detection model is via the use of the generalized linear model. For example, DeCarlo (1998) shows how signal detection models based on different underlying distributions can easily be considered by using the generalized linear model with different link functions.
For discussion of the generalized linear model and the link functions it uses, see the Generalized Linear Models topic.
Simple Random Sampling (SRS). Simple random sampling is a type of probability sampling where observations are randomly selected from a population with a known probability or sampling fraction. Typically, we begin with a list of N observations that comprises the entire population from which we wish to extract a simple random sample (e.g., a list of registered voters); we can then generate k random case numbers (without replacement) in the range from 1 to N, and select the respective cases into the final sample (with a sampling fraction or known selection probability of k/N). Refer to Kish (1965) for a detailed discussion of the advantages and characteristics of probability samples and EPSEM samples.
Simplex Algorithm. A nonlinear estimation algorithm that does not rely on the computation or estimation of the derivatives of the loss function. Instead, at each iteration the function will be evaluated at m+1 points in the m dimensional parameter space. For example, in two dimensions (i.e., when there are two parameters to be estimated), the program will evaluate the function at three points around the current optimum. These three points would define a triangle; in more than two dimensions, the "figure" produced by these points is called a Simplex.
Single and Multiple Censoring. There are situations in which censoring can occur at different times (multiple censoring), or only at a particular point in time (single censoring). Consider an example experiment where we start with 100 light bulbs, and terminate the experiment after a certain amount of time. If the experiment is terminated at a particular point in time, then a single point of censoring exists, and the data set is said to be single-censored. However, in biomedical research multiple censoring often exists, for example, when patients are discharged from a hospital after different amounts (times) of treatment, and the researcher knows that the patient survived up to those (differential) points of censoring.
Data sets with censored observations can be analyzed via Survival Analysis or Weibull and Reliability/Failure Time Analysis. See also, Type I and II Censoring and Left and Right Censoring.
Singular Value Decomposition. An efficient algorithm for optimizing a linear model. See also, pseudo-inverse.
Six Sigma (DMAIC). Six Sigma is a well-structured, data-driven methodology for eliminating defects, waste, or quality control problems of all kinds in manufacturing, service delivery, management, and other business activities. Six Sigma methodology is based on the combination of well-established statistical quality control techniques, simple and advanced data analysis methods, and the systematic training of all personnel at every level in the organization involved in the activity or process targeted by Six Sigma.
Six Sigma methodology and management strategies provide an overall framework for organizing company wide quality control efforts. These methods have recently become very popular, due to numerous success stories from major US-based as well as international corporations. For reviews of Six Sigma strategies, refer to Harry and Schroeder (2000), or Pyzdek (2001).
These are organized into the categories of activities that make up the Six Sigma effort: Define (D), Measure (M), Analyze (A), Improve (I), Control (C); or DMAIC for short.
Define. The Define phase is concerned with the definition of project goals and boundaries, and the identification of issues that need to be addressed to achieve the higher sigma level.
Measure. The goal of the Measure phase is to gather information about the current situation, to obtain baseline data on current process performance, and to identify problem areas.
Analyze. The goal of the Analyze phase is to identify the root cause(s) of quality problems, and to confirm those causes using the appropriate data analysis tools.
Improve. The goal of the Improve phase is to implement solutions that address the problems (root causes) identified during the previous (Analyze) phase.
Control. The goal of the Control phase is to evaluate and monitor the results of the previous phase (Improve).
Six Sigma Process. A six sigma process is one that can be expected to produce only 3.4 defects per one million opportunities. The concept of the six sigma process is important in Six Sigma quality improvement programs. The idea can best be summarized with the following graphs.
The term Six Sigma derives from the goal to achieve a process variation, so that ± 6 * sigma (the estimate of the population standard deviation) will "fit" inside the lower and upper specification limits for the process. In that case, even if the process mean shifts by 1.5 * sigma in one direction (e.g., to +1.5 sigma in the direction of the upper specification limit), then the process will still produce very few defects.
For example, suppose we expressed the area above the upper specification limit in terms of one million opportunities to produce defects. The 6 * sigma process shifted upwards by 1.5 * sigma will only produce 3.4 defects (i.e., "parts" or "cases" greater than the upper specification limit) per one million opportunities.
Shift. An ongoing process that at some point was centered will shift over time. Motorola, in their implementation of Six Sigma strategies, determined that it is reasonable to assume that a process will shift over time by approximately 1.5 * sigma (see, for example, Harry and Schroeder, 2000). Hence, most standard Six Sigma calculators will be based on a 1.5 * sigma shift.
One-sided vs. two-sided limits. In the illustration shown above the area outside the upper specification limit (greater than USL) is defined as one million opportunities to produce defects. Of course, in many cases any "outcomes" (e.g., parts) that are produced that fall below the specification limit can be equally defective. In that case, we may want to consider the lower tail of the respective (shifted) normal distribution as well. However, in practice, we usually ignore the lower tail of the normal curve because (1) in many cases, the process "naturally" has one-sided specification limits (e.g., very low delay times are not really a defect, only very long times; very few customer complaints are not a problem, only very many, etc.), and (2) when a 6 * sigma process has been achieved, the area under the normal curve below the lower specification limit is negligible.
Yield. The illustration shown above focuses on the number of defects that a process produces. The number of non-defects can be considered the Yield of the process. Six Sigma calculators will compute the number of defects per million opportunities (DPMO) as well as the yield, expressed as the percent of the area under the normal curve that falls below the upper specification limit (in the illustration above).
Skewness. Skewness (this term was first used by Pearson, 1895) measures the deviation of the distribution from symmetry. If the skewness is clearly different from 0, then that distribution is asymmetrical, while normal distributions are perfectly symmetrical.
Skewness = n*M3/[(n-1)*(n-2)*3]
M3 is equal to: (xi-Meanx)3
3 is the standard deviation (sigma) raised to the third power
n is the valid number of cases.
See also, Descriptive Statistics Overview.
Smoothing. Smoothing techniques can be used in two different situations. Smoothing techniques for 3D Bivariate Histograms allow us to fit surfaces to 3D representations of bivariate frequency data. Thus, every 3D histogram can be turned into a smoothed surface providing a sensitive method for revealing non-salient overall patterns of data and/or identifying patterns to use in developing quantitative models of the investigated phenomenon.
In Time Series analysis, the general purpose of smoothing techniques is to "bring out" the major patterns or trends in a time series, while de-emphasizing minor fluctuations (random noise). Visually, as a result of smoothing, a jagged line pattern should be transformed into a smooth curve.
See also, Exploratory Data Analysis and Data Mining Techniques and Smoothing Bivariate Distributions.
SOFMs (Self-Organizing Feature Maps; Kohonen Networks). Neural networks based on the topological properties of the human brain, also known as Kohonen Networks (Kohonen, 1982; Fausett, 1994,; Haykin, 1994; Patterson, 1996).
Softmax. A specialized activation function for one-of-N encoded classification networks. Performs a normalized exponential (i.e. the outputs add up to 1). In combination with the cross entropy error function, allows multilayer perceptron networks to be modified for class probability estimation (Bishop, 1995; Bridle, 1990). See, Neural Networks.
Space Plots. This type of graph offers a distinctive means of representing 3D Scatterplot data through the use of a separate X-Y plane positioned at a user-selectable level of the vertical Z-axis (which "sticks up" through the middle of the plane).
The Space Plots specific layout may facilitate exploratory examination of specific types of three-dimensional data. It is recommended to assign variables to axes such that the variable that is most likely to discriminate between patterns of relation among the other two is specified as Z. See also, Data Rotation (in 3D space) in the Graphical Techniques topic.
Spearman R. Spearman R can be thought of as the regular Pearson product-moment correlation coefficient (Pearson r); that is, in terms of the proportion of variability accounted for, except that Spearman R is computed from ranks. As mentioned above, Spearman R assumes that the variables under consideration were measured on at least an ordinal (rank order) scale; that is, the individual observations (cases) can be ranked into two ordered series. Detailed discussions of the Spearman R statistic, its power and efficiency can be found in Gibbons (1985), Hays (1981), McNemar (1969), Siegel (1956), Siegel and Castellan (1988), Kendall (1948), Olds (1949), or Hotelling and Pabst (1936).
Spectral Plot. The original application of this type of plot was in the context of spectral analysis in order to investigate the behavior of non-stationary time series. On the horizontal axes, we can plot the frequency of the spectrum against consecutive time intervals, and indicate on the Z-axis the spectral densities at each interval (see for example, Shumway, 1988, page 82).
Spectral plots have clear advantages over the regular 3D Scatterplots when we are interested in examining how a relationship between two variables changes across the levels of a third variable, as is shown in the next illustration. The advantage of Spectral Plots over regular 3D Scatterplots is well-illustrated in the comparison of the two displays of the same data set shown below.
The Spectral Plot makes it easier to see that the relationship between Pressure and Yield changes from an "inverted U" to a "U". See also, Data Rotation (in 3D space) in Graphical Techniques.
Spikes (3D Graphs). In this type of graph, individual values of one or more series of data are represented along the X-axis as a series of "spikes" (point symbols with lines descending to the base plane). Each series to be plotted is spaced along the Y-axis. The "height" of each spike is determined by the respective value of each series.
Spline (2D Graphs). A curve is fitted to the XY coordinate data using the bicubic spline smoothing procedure.
Spline (3D Graphs). A surface is fitted to the XYZ coordinate data using the bicubic spline smoothing procedure.
Split Selection (for Classification Trees). Split selection for classification trees refers to the process of selecting the splits on the predictor variables that are used to predict membership in the classes of the dependent variable for the cases or objects in the analysis. Given the hierarchical nature of classification trees, these splits are selected one at time, starting with the split at the root node, and continuing with splits of resulting child nodes until splitting stops, and the child nodes that have not been split become terminal nodes. The split selection process is described in the Computational Methods section of Classification Trees.
Spurious Correlations. Correlations that are due mostly to the influences of one or more "other" variables. For example, there is a correlation between the total amount of losses in a fire and the number of firemen that were putting out the fire; however, what this correlation does not indicate is that if we call fewer firemen, we would lower the losses. There is a third variable (the initial size of the fire) that influences both the amount of losses and the number of firemen. If we "control" for this variable (e.g., consider only fires of a fixed size), the correlation will either disappear or perhaps even change its sign. The main problem with spurious correlations is that we typically do not know what the "hidden" agent is. However, in cases when we know where to look, we can use partial correlations that control for (i.e., partial out) the influence of specified variables. See also, Correlation, Partial Correlation, Basic Statistics, Multiple Regression, Structural Equation Modeling (SEPATH).
SQL. SQL (Structured Query Language) enables us to query an outside data source about the data it contains. We can use a SQL statement in order to specify the desired tables, fields, rows, etc. to return as data. For information on SQL syntax, consult an SQL manual.
Square Root of the Signal to Noise Ratio (f). This standardized measure of effect size is used in the Analysis of Variance to characterize the overall level of population effects, and is very similar to the RMSSE. It is the square root of the sum of squared standardized effects divided by the number of effects. For example, in a 1-Way ANOVA, with J groups, f is calculated as
For more information, see Power Analysis.
Stacked Generalization. See Stacking.
Stacking (Stacked Generalization). The concept of stacking (short for Stacked Generalization) applies to the area of predictive data mining, to combine the predictions from multiple models. It is particularly useful when the types of models included in the project are very different.
Suppose your data mining project includes tree classifiers, such as C&RT and CHAID, linear discriminant analysis (e.g., see GDA), and Neural Networks. Each computes predicted classifications for a crossvalidation sample, from which overall goodness-of-fit statistics (e.g., misclassification rates) can be computed. Experience has shown that combining the predictions from multiple methods often yields more accurate predictions than can be derived from any one method (e.g., see Witten and Frank, 2000).
In stacking, the predictions from different classifiers are used as input into a meta-learner, which attempts to combine the predictions to create a final best predicted classification. So, for example, the predicted classifications from the tree classifiers, linear model, and the neural network classifier(s) can be used as input variables into a neural network meta-classifier, which will attempt to "learn" from the data how to combine the predictions from the different models to yield maximum classification accuracy.
Other methods for combining the prediction from multiple models or methods (e.g., from multiple datasets used for learning) are Boosting and Bagging (Voting).
Standard Deviation. The standard deviation (this term was first used by Pearson, 1894) is a commonly-used measure of variation. The standard deviation of a population of values is computed as:
= [(xi-µ)2/N]1/2
µ is the population mean
N is the population size.
The sample estimate of the population standard deviation is computed as:
s = [(xi-x-bar)2/n-1]1/2
xbar is the sample mean
n is the sample size.
Standard Error. The standard error (this term was first used by Yule, 1897) is the standard deviation of a mean and is computed as:
std.err. = Ö(s2/n)
s2 is the sample variance
n is the sample size.
Standard Error of the Mean. The standard error of the mean (first used by Yule, 1897) is the theoretical standard deviation of all sample means of size n drawn from a population and depends on both the population variance (sigma) and the sample size (n) as indicated below:
= (2/n)1/2 where
2 is the population variance and
n is the sample size.
Since the population variance is typically unknown, the best estimate for the standard error of the mean is then calculated as:
= (s2/n)1/2
s2 is the sample variance (our best estimate of the population variance) and
n is the sample size.
Standard Error of the Proportion. This is the standard deviation of the distribution of the sample proportion over repeated samples. If the population proportion is , and the sample size is N, the standard error of the proportion when sampling from an infinite population is
sp = (p(1-p)/N)**1/2
Standardization.While in the everyday language, the term "standardization" means converting to a common standard or making something conform to a standard (i.e., its meaning is similar to the term normalization in data analysis), in statistics, this term has a very specific meaning and refers to the transformation of data by subtracting each value from some reference value (typically a sample mean) and diving it by the standard deviation (typically a sample SD). This important transformation will bring all values (regardless of their distributions and original units of measurement) to compatible units from a distribution with a mean of 0 and a standard deviation of 1.
This transformation has a wide variety of applications because it makes the distributions of values easy to compare across variables and/or subsets. If applied to the input data, standardization also makes the results of a variety of statistical techniques entirely independent of the ranges of values or the units of measurements (see the discussion of these issues in Elementary Concepts, Basic Statistics, Multiple Regression, Factor Analysis, and others).
Standardized DFFITS. This is another measure of impact of the respective case on the regression equation. The formula for standardized DFFITS is
SDFITi = DFFITi/(si(i)1/2) where hi is the leverage for the ith case
i = 1/N + hi See also, DFFITS, studentized residuals, and studentized deleted residuals. For more information see Hocking (1996) and Ryan (1997).
Standardized Effect (Es). A statistical effect expressed in convenient standardized units. For example, the standardized effect in a 2 Sample t-test is the difference between the two means, divided by the standard deviation, i.e.,
Es = (µ1 - µ2)/s
Standard Residual Value. This is the standardized residual value (observed minus predicted divided by the square root of the residual mean square). See also, Mahalanobis distance, deleted residual and Cook's distance.
Stationary Series (in Time Series). In Time Series analysis, a stationary series has a constant mean, variance, and autocorrelation through time (i.e., seasonal dependencies have been removed via Differencing).
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STATISTICA Monitoring and Alerting Server (MAS).StatSoft's STATISTICA Monitoring and Alerting Server (MAS) is a system that enables users to automate the continual monitoring of hundreds or thousands of critical process and product parameters. The ongoing monitoring is an automated and efficient method for:
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STATISTICA Multivariate Statistical Process Control (MSPC).StatSoft's STATISTICA Multivariate Statistical Process Control (MSPC) is a complete solution for multivariate statistical process control, deployed within a scalable, secure analytics software platform.
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Statistical Power. The probability of rejecting a false statistical null hypothesis. For more information, see Power Analysis.
Statistical Process Control (SPC).The term Statistical Process Control (SPC) is typically used in context of manufacturing processes (although it may also pertain to services and other activities), and it denotes statistical methods used to monitor and improve the quality of the respective operations. By gathering information about the various stages of the process and performing statistical analysis on that information, the SPC engineer is able to take necessary action (often preventive) to ensure that the overall process stays in-control and to allow the product to meet all desired specifications.
SPC involves monitoring processes, identifying problem areas, recommending methods to reduce variation and verifying that they work, optimizing the process, assessing the reliability of parts, and other analytic operations. SPC uses such basic statistical quality control methods as quality control charts (Sheward, Pareto, and others), capability analysis, gage repeatability/reproducibility analysis, and reliability analysis. However, also specialized experimental methods (DOE) and other advanced statistical techniques are often part of global SPC systems. Important components of effective, modern SPC systems are real-time access to data and facilities to document and respond to incoming QC data on-line, efficient central QC data warehousing, and groupware facilities allowing QC engineers to share data and reports (see also, Enterprise SPC).
See also, Quality Control and Process Analysis. For more information on process control systems, see the ASQC/AIAG's Fundamental statistical process control reference manual (1991).
Statistical Significance (p-value). The statistical significance of a result is an estimated measure of the degree to which it is "true" (in the sense of "representative of the population"). More technically, the value of the p-value represents a decreasing index of the reliability of a result. The higher the p-value, the less we can believe that the observed relation between variables in the sample is a reliable indicator of the relation between the respective variables in the population. Specifically, the p-value represents the probability of error that is involved in accepting our observed result as valid, that is, as "representative of the population." For example, the p-value of .05 (i.e.,1/20) indicates that there is a 5% probability that the relation between the variables found in our sample is a "fluke." In other words, assuming that in the population there was no relation between those variables whatsoever, and we were repeating experiments like ours one after another, we could expect that approximately in every 20 replications of the experiment there would be one in which the relation between the variables in question would be equal or stronger than in ours. In many areas of research, the p-value of .05 is customarily treated as a "border-line acceptable" error level. See also, Elementary Concepts.
Steepest Descent Iterations. When initial values for the parameters are far from the ultimate minimum, the approximate Hessian used in the Gauss-Newton procedure may fail to yield a proper step direction during iteration. In this case, the program may iterate into a region of the parameter space from which recovery (i.e., successful iteration to the true minimum point) is not possible. One option offered by Structural Equation Modeling is to precede the Gauss-Newton procedure with a few iterations utilizing the "method of steepest descent." In the steepest descent approach, values of the parameter vector q on each iteration are obtained as
k+1 = k + kgk
In simple terms, what this means is that the Hessian is not used to help find the direction for the next step. Instead, only the first derivative information in the gradient is used.
Hint for beginners. Inserting a few Steepest Descent Iterations may help in situations where the iterative routine "gets lost" after only a few iterations.
Stemming. An important pre-processing step before indexing input documents for text mining is the stemming of words. The term stemming refers to the reduction of words to their roots so that, for example, different grammatical forms or declinations of verbs are identified and indexed (counted) as the same word. For example, stemming will ensure that both "travel" and "traveled" will be recognized by the program as the same word. For more information, see Manning and Schütze (2002).
Steps. Repetitions of a particular analytic or computational operation or procedure. For example in the neural network time series analysis, the number of consecutive time steps from which input variable values should be drawn to be fed into the neural network input units.
Stepwise Regression. A model-building technique that finds subsets of predictor variables that most adequately predict responses on a dependent variable by linear (or nonlinear) regression, given the specified criteria for adequacy of model fit.
For an overview of stepwise regression and model fit criteria see General Stepwise Regression or Multiple Regression; for nonlinear stepwise and best subset regression, see Generalized Linear Models.
Stiffness Parameter (in Fitting Options). The function that controls the weight is determined by the Stiffness parameter, which can be modified. Thus, the stiffness parameter determines the degree to which the fitted curve depends on local configurations of the analyzed values.
The lower the coefficient, the more the shape of the curve is influenced by individual data points (i.e., the curve "bends" more to accommodate individual values and subsets of values). The range of the stiffness parameters is 0 < s < 1. Large values of the parameter produce smoother curves that adequately represent the overall pattern in the data set at the expense of local details. See also, McLain, 1974.
Stopping Conditions. During an iterative process (e.g., fitting, searching, training), the conditions that must be true for the process to stop. (For example, in neural networks, the stopping conditions include the maximum number of epochs, target error performance and the minimum error improvement thresholds.
Stopping Conditions (in Neural Networks). The iterative gradient-descent training algorithms (back propagation, Quasi-Newton, conjugate gradient descent, Levenberg-Marquardt, quick propagation, Delta-bar-Delta, and Kohonen) all attempt to reduce the training error on each epoch.
We specify a maximum number of epochs for these iterative algorithms. However, we can also define stopping conditions that may cause training to determine earlier.
Specifically, training may be stopped when:
the error drops below a given level;
the error fails to improve by a given amount over a given number of epochs.
The conditions are cumulative; i.e., if several stopping conditions are specified, training ceases when any one of them is satisfied. In particular, a maximum number of epochs must always be specified. The error-based stopping conditions can also be specified independently for the error on the training set and the error on the selection set (if any).
Target Error. We can specify a target error level, for the training subset, the selection subset, or both. If the RMS falls below this level, training ceases.
Minimum Improvement. Specifies that the RMS error on the training subset, the selection subset, or both must improve by at least this amount, or training will cease (if the Window parameter is non-zero).
Sometimes error improvement may slow down for a while or even rise temporarily (particularly if the shuffle option is used with back propagation, or non-zero noise is specified, as these both introduce an element of noise into the training process). To prevent this option from aborting the run prematurely, specify a longer Window. It is particularly recommended to monitor the selection error for minimum improvement, as this helps to prevent over-learning.
Specify a negative improvement threshold if you want to stop training only when a significant deterioration in the error is detected. The algorithm will stop when a number of generations pass during which the error is always the given amount worse than the best it ever achieved.
Window. The window factor is the number of epochs across which the error must fail to improve by the specified amount, before the algorithm is deemed to have slowed down too much and is stopped. By default the window is zero, which means that the minimum improvement stopping condition is not used at all.
Stopping Rule (in Classification Trees). The stopping rule for a classification tree refers to the criteria that are used for determining the "right-sized" classification tree, that is, a classification tree with an appropriate number of splits and optimal predictive accuracy. The process of determining the "right-sized" classification tree is described in the Computational Methods section of Classification Trees.
Stratified Random Sampling. In general, random sampling is the process of randomly selecting observations from a population, to create a subsample that "represents" the observations in that population (see Kish, 1965; see also Probability Sampling, Simple Random Sampling, EPSEM Samples; see also Representative Sample for a brief exploration of this often misunderstood notion). In stratified sampling, we usually apply specific (identical or different) sampling fractions to different groups (strata) in the population to draw the sample.
Over-sampling particular strata to over-represent rare events. In some predictive data mining applications, it is often necessary to apply stratified sampling to systematically over-sample (apply a greater sampling fraction) to particular "rare events" of interest. For example, in catalog retailing the response rate to particular catalog offers can be below 1%, and when analyzing historical data (from prior campaigns) to build a model for targeting potential customers more successfully, it is desirable to over-sample past respondents (i.e., the "rare" respondents who ordered from the catalog); we can then apply the various model building techniques for classification (see Data Mining) to a sample consisting of approximately 50% responders and 50% non-responders. Otherwise, if we were to draw a simple random sample for the analysis (with 1% of responders), then practically all model building techniques would likely predict a simple "no-response" for all cases and would be (trivially) correct in 99% of the cases.
Stub and Banner Tables (Banner Tables). Stub-and-banner tables are essentially two-way tables, except that two lists of categorical variables (instead of just two individual variables) are crosstabulated. In the Stub-and-banner table, one list will be tabulated in the columns (horizontally) and the second list will be tabulated in the rows (vertically) of the Scrollsheet. For more information, see the Stub and Banner Tables section of Basic Statistics.
Studentized Deleted Residuals. In addition to standardized residuals several methods (including studentized residuals, studentized deleted residuals, DFFITS, and standardized DFFITS) are available for detecting outlying values (observations with extreme values on the set of predictor variables or the dependent variable). The formula for studentized deleted residuals is given by:
e = residual
SSE = sum of squared errors
N = sample size
p = rank of design matrix X
H = leverage
For more information see Hocking (1996) and Ryan (1997).
Studentized Residuals. In addition to standardized residuals, several methods (including studentized residuals, studentized deleted residuals, DFFITS, and standardized DFFITS) are available for detecting outlying values (observations with extreme values on the set of predictor variables or the dependent variable). The formula for studentized residuals is
SRESi = (ei/s)/(1-i)1/2 where
ei is the error for the ith case
hi is the leverage for the ith case
and i = 1/N + hi For more information see Hocking (1996) and Ryan (1997).
Student's t Distribution. The Student's t distribution has density function (for = 1, 2, ...):
is the degrees of freedom
(gamma) is the Gamma function
is the constant Pi (3.14...)
The animation above shows various tail areas (p-values) for a Student's t distribution with 15 degrees of freedom.
Sum-Squared Error Function. An error function composed by squaring the difference between sets of target and actual values, and adding these together. See also, loss function.
Supervised and Unsupervised Learning. An important distinction in machine learning, and also applicable to data mining, is that between supervised and unsupervised learning algorithms. The term "supervised" learning is usually applied to cases in which a particular classification is already observed and recorded in a training sample, and you want to build a model to predict those classifications (in a new testing sample). For example, you may have a data set that contains information about who from among a list of customers targeted for a special promotion responded to that offer. The purpose of the classification analysis would be to build a model to predict who (from a different list of new potential customers) is likely to respond to the same (or a similar) offer in the future. You may want to review the methods discussed in General Classification and Regression Trees (GC&RT), General CHAID Models (GCHAID), Discriminant Function Analysis and General Discriminant Analysis (GDA), MARSplines (Multivariate Adaptive Regression Splines), and neural networks to learn about different techniques that can be used to build or fit models to data where the outcome variable of interest (e.g., customer did or did not respond to an offer) was observed. These methods are called supervised learning algorithms because the learning (fitting of models) is "guided" or "supervised" by the observed classifications recorded in the data file.
In unsupervised learning, the situation is different. Here the outcome variable of interest is not (and perhaps cannot be) directly observed. Instead, we want to detect some "structure" or clusters in the data that may not be trivially observable. For example, you may have a database of customers with various demographic indicators and variables potentially relevant to future purchasing behavior. Your goal would be to find market segments, i.e., groups of observations that are relatively similar to each other on certain variables; once identified, you could then determine how best to reach one or more clusters by providing certain goods or services you think may have some special utility or appeal to individuals in that segment (cluster). This type of task calls for an unsupervised learning algorithm, because learning (fitting of models) in this case cannot be guided by previously known classifications. Only after identifying certain clusters can you begin to assign labels, for example, based on subsequent research (e.g., after identifying one group of customers as "young risk takers").
There are several methods available for unsupervised learning, including Principal Components and Classification Analysis, Factor Analysis, Multidimensional Scaling, Correspondence Analysis, Neural Networks, Self-Organizing Feature Maps (SOFM, Kohonen networks); particularly powerful algorithms for pattern recognition and clustering are the EM and k-Means clustering algorithms.
Support Value (Association Rules). When applying (in data or text mining) algorithms for deriving association rules of the general form If Body then Head (e.g., If (Car=Porsche and Age<20) then (Risk=High and Insurance=High)), the Support value is computed as the joint probability (relative frequency of co-occurrence) of the Body and Head of each association rule.
Support Vector. A set of points in the feature space that determines the boundary between objects of different class memberships.
Support Vector Machine (SVM) Support Vector Machine (SVM) A classification method based on the maximum margin hyperplane.
Suppressor Variable. A suppressor variable (in Multiple Regression ) has zero (or close to zero) correlation with the criterion but is correlated with one or more of the predictor variables, and therefore, it will suppress irrelevant variance of independent variables. For example, you are trying to predict the times of runners in a 40 meter dash. Your predictors are Height and Weight of the runner. Now, assume that Height is not correlated with Time, but Weight is. Also assume that Weight and Height are correlated. If Height is a suppressor variable, then it will suppress, or control for, irrelevant variance (i.e., variance that is shared with the predictor and not the criterion), thus increasing the partial correlation. This can be viewed as ridding the analysis of noise.
Let t = Time, h = Height, w - Weight, rth = 0.0, rtw = 0.5, and rhw = 0.6.
Weight in this instance accounts for 25% (Rtw**2 = 0.5**2) of the variability of Time. However, if Height is included in the model, then an additional 14% of the variability of Time is accounted for even though Height is not correlated with Time (see below):
Rt.hw**2 = 0.5**2/(1 - 0.6**2) = 0.39
For more information, please refer to Pedhazur, 1982.
Surface Plot (from Raw Data). This sequential plot fits a spline-smoothed surface to each data point. Successive values of each series are plotted along the X-axis, with each successive series represented along the Y-axis.
Survival Analysis. Survival analysis (exploratory and hypothesis testing) techniques include descriptive methods for estimating the distribution of survival times from a sample, methods for comparing survival in two or more groups, and techniques for fitting linear or non-linear regression models to survival data. A defining characteristic of survival time data is that they usually include so-called censored observations, e.g., observations that "survived" to a certain point in time, and then dropped out from the study (e.g., patients who are discharged from a hospital). Instead of discarding such observations from the data analysis all together (i.e., unnecessarily loose potentially useful information) survival analysis techniques can accommodate censored observations, and "use" them in statistical significance testing and model fitting.
Typical survival analysis methods include life table, survival distribution, and Kaplan-Meier survival function estimation, and additional techniques for comparing the survival in two or more groups. Finally, Survival analysis includes the use of regression models for estimating the relationship of (multiple) continuous variables to survival times. For more information, see Survival Analysis.
Survivorship Function. The survivorship function (commonly denoted as R(t)) is the complement to the cumulative distribution function (i.e., R(t)=1-F(t)); the survivorship function is also referred to as the reliability or survival function (since it describes the probability of not failing or of surviving until a certain time t; e.g., see Lee, 1992).
For additional information see Survival Analysis or the Weibull and Reliability/Failure Time Analysis section of Process Analysis.
Sweeping. The sweeping transformation of matrices is commonly used to efficiently perform stepwise multiple regression (see Dempster, 1969, Jennrich, 1977) or similar analyses; a modified version of this transformation is also used to compute the g2 generalized inverse. The forward sweeping transformation for a column k can be summarized in the following four steps (where the e's refer to the elements of a symmetric matrix):
The reverse sweeping operation reverses the changes effected by these transformations. The sweeping operator is used extensively in General Linear Models, Multiple Regression, and similar techniques.
Symmetrical Distribution. If you split the distribution in half at its mean (or median), then the distribution of values would be a "mirror image" about this central point. See also, Descriptive Statistics Overview.
Symmetric Matrix. A matrix is symmetric if the transpose of the matrix is itself (i.e., A = A'). In other words, the lower triangle of the square matrix is a "mirror image" of the upper triangle with 1's on the diagonal (see below).
Synaptic Functions (in Neural Networks).
Dot product. Dot product units perform a weighted sum of their inputs, minus the threshold value. In vector terminology, this is the dot product of the weight vector with the input vector, plus a bias value. Dot product units have equal output values along hyperplanes in pattern space. They attempt to perform classification by dividing pattern space into sections using intersecting hyperplanes.
Radial. Radial units calculate the square of the distance between the two points in N dimensional space (where N is the number of inputs) represented by the input pattern vector and the unit's weight vector. Radial units have equal output values lying on hyperspheres in pattern space. They attempt to perform classification by measuring the distance of normalized cases from exemplar points in pattern space (the exemplars being stored by the units). The squared distance is multiplied by the threshold (which is, therefore, actually a deviation value in radial units) to produce the post synaptic value of the unit (which is then passed to the unit's activation function).
Dot product units are used in multilayer perceptron and linear networks, and in the final layers of radial basis function, PNN, and GRNN networks.
Radial units are used in the second layer of Kohonen, radial basis function, Clustering, and probabilistic and generalized regression networks. They are not used in any other layers of any standard network architecture.
Division. This is specially designed for use in generalized regression networks, and should not be employed elsewhere. It expects one incoming weight to equal +1, one to equal -1, and the others to equal zero. The post-synaptic value is the +1 input divided by the -1 input.


text:
Why Halloween can be truly scary for pets
A dog's-eye view of Halloween can get pretty scary.
On the face of it, it should be a perfect day to be a dog. After all, your humans dress you up in funny stuff and take you to your neighbors' houses and they smile and give you things to eat.
You don't even have to do any tricks. Cool!
But then you get home and your humans won't let you eat it. It's all so confusing. It's my food, isn't it?
That brown chewy stuff — they call it chocolate — looks so good. I could bite it all down in two gulps.
But the humans say chocolate is toxic for pets. I think that means it makes you puke. They say that the Pet Poison Helpline warns about giving pets chocolate. Why did the neighbors hand it out, then?

subset: commoncrawl


text:
In the post debate commentary of the third – and last – presidential debate on foreign policy, on Monday, Chris Matthews just could not help himself spewing another diatribe about the racial hatred that he insists is ingrained into the soul of America's conservatives.
In fact, clucked Matthews, they're so hateful of Obama that they want him out of the White House more than they want al-Qaeda's complete destruction. Below is the transcript and video of this hyperbolic and unruly episode. (video after the jump)
From MSNBC's October 22, 2012 post debate coverage:
CHRIS MATTHEWS: I think they're more political than either you or I – I think they hate Obama. They want him out of the White House more than they want to destroy al Qaeda. Their number one enemy in the world right now, on the right, is their hatred – hatred for Obama. We can go into that about the white working class in the south and looking at these numbers we're getting about racial hatred in many cases. This isn't about being a better president. They want to get rid of this president. That's their number one goal and they're willing to let Romney go to the hard center, even if it's to the left on issues, as long as they get rid of this guy. Romney went in there tonight with sixteen ounce gloves. He didn't want to look too ferocious he just wanted to win and the way he wanted to win was not make himself into the right-winger that the right wing that's supporting him really are.


text:
photo 1 of 87
UFC®147 Weigh-in Photo Gallery
June 22, 2012
87 Photos
UFC 147 weigh in at Estadio Jornalista Felipe Drummond on June 22, 2012 in Belo Horizonte, Brazil.
Getty Images reserves the right to pursue unauthorized users of this image or clip. If you violate our intellectual property you may be liable for: actual damages, loss of income, and profits you derive from the use of this image or clip, and, where appropriate, the costs of collection and/or statutory damages up to $150,000 (USD).


text:
Every week, Food52's Senior Editor Kristen Miglore is unearthing recipes that are nothing short of genius.
Today: New life for the herbs and greens that linger in the crisper, thanks to Paula Wolfert (and Food52er fiveandspice).
- KristenBundles of herbs -- the very things that bring life and color to so many recipes -- are too often the victims of crisper drawer abandonment. A week after that bánh mì or minestrone we were so excited about, we unearth the remains, wilted and weeping, entombed in their own plastic baggy coffins. So we throw them away, faces all squinched up. And we hate ourselves, and sulk about missed opportunity.There is a way to treat them a little better, and ease this self-loathing. You can store them something like a bouquet of flowers: upright in a jar in the fridge, tips dangling into fresh water, and tops protected by a plastic bag secured by a rubber band around the jar -- a trick for keeping them hydrated that I learned from a family friend and wonderful cook known to all as Jagu. If you remember to change out the water every few days, your herbs will last for a freakishly long time.But I usually don't remember to do this. And even when I do -- those herbs can't defy their fate forever.
Our friend fiveandspice tipped me off to the solution, and it's an herb jam by Paula Wolfert (pictured above). "It's completely changed the way I think about cilantro and parsley," fiveandspice says. "I used to never want to get a bunch of either for a recipe that called for just a tablespoon or two for garnish -- which is many recipes. I never waste any, anymore!"In Wolfert's book The Slow Mediterranean Kitchen, she smartly adapts a traditional Moroccan spread (typically made with a native green called baqqula) to the modern kitchen (note that you can still use either a heavy-bottomed skillet or a "medium cazuela set over a flame-tamer" -- I'll leave that up to you.)
In a couple of nifty strokes of genius, Wolfert transforms your sad-looking herbs and greens into a spunky new animal. It doesn't matter if they're starting to lose their edge, that their garnishing days are over -- because you're about to cook the daylights out of them (see a full slideshow of the proceedings on the recipe page here).
First you gather up all your orphaned semi-bunches of herbs -- Wolfert has a specific cocktail of parsley, cilantro and celery leaves, but fiveandspice says she uses whatever she has on hand that needs using up. Throw in a whole lot of spinach or chard (or other not-too-sturdy greens). My favorite step: you tuck a few cloves of garlic in there to steam with the gang, then pull them out and mash them with a fork for the next stage.You'll wring your herbs and greens dry and chop them, then bring them back to life, by sauteing them with smoked paprika, cumin, cayenne, oil-cured olives, and that garlic, smashing it all around until it turns into a heady, delicious jam. Fresh lemon juice and olive oil perk it up at the end. It's rich and voluptuous, and surprisingly meaty for something with this much green packed into it (Can you hear me vegans? This one's for you!).
This jam isn't here to win beauty pageants -- it's glorified mulch, really. If this concerns you, serve it in your prettiest little bowl, with dainty crackers. Ours looked like doilies and were very good. Pull out a butter knife from your finest silver and polish it up, if that's what it takes. Just don't let the jam's scraggly appearance keep you from its charms. Imagine what your relationship with your fridge could be, if instead of the unfortunate discoveries lurking in the corner of the crisper drawer, you always had a wee pot of this to greet you.
Paula Wolfert's Herb Jam with Olives and Lemon
Adapted slightly from The Slow Mediterranean Kitchen (Wiley, 2003)
Serves 6; Makes about 1 1/2 cups
See the full recipe (and save and print it) here.
Want more genius? Try Martha Stewart's perfectly creamy baked Macaroni and Cheese.
Got a genius recipe to share -- from a classic cookbook, an online source, or anywhere, really? Please send it my way (and tell me what's so smart about it) at [email protected].
Photos by James Ransom
The Genius Desserts cookbook is here! With more than 100 of the most beloved and talked-about desserts of our time (and the hidden gems soon to join their ranks) this book will make you a local legend, and a smarter baker to boot.
See what other Food52 readers are saying.


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Saving Memories Forever Announces First Community Outreach: American Widow Project
Saving Memories Forever proudly announces its first community effort. Starting February 11, 2013 Saving Memories Forever will sponsor a fundraising campaign on behalf of the American Widow Project. The fundraiser will run through the end of March 2013.
The American Widow Project (AWP) is a non-profit organization that provides military widows with peer-to-peer support as they rebuild their lives. AWP places an emphasis on healing through sharing stories, tears and laughter. They host a Hotline service and write a monthly newsletter. They also hold multiples events each year where widows come together to enjoy life the way they did when their spouse was still alive.
The program is available to all military wives whose spouses have been killed in Iraq and Afghanistan, an estimated 3,200 widows. The American Widow Project was created in 2007 by Taryn Davis whose husband was killed in Iraq. Taryn has received numerous awards. She was named a Top 10 CNN Hero Honoree in 2011. She has also been honored as one of Newsweek Magazine's "150 Women Who Shake the World", Diller-Von Furstenburg Foundation's "People's Voice" Award Winner, and L'Oreal Woman of Worth Honoree in 2010.
The American Widow Project and Saving Memories Forever share a common goal: keeping alive the memories and stories of American soldiers, husbands, and oftentimes fathers. Saving Memories Forever honors the service of the men and applauds the life-affirming efforts of their wives. Saving Memories Forever welcomes the wives and looks forward to the years ahead as our service helps give them comfort and joy.
As part of this campaign, SMF will donate 20 Premium Subscriptions. In addition, AWP will receive 40% of the profits from people who subscribe to Saving Memories Forever using the special fundraiser Promo Code AWP213. You can visit the American Widow Project at: http://www.americanwidowproject.org.
Saving Memories Forever is committed to helping non-profit organizations whose mission ties in with telling and saving stories. If you know of an organization whose mission ties in with ours, please contact us.


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Related Media: What To Eat When You Want to Lose Weight
What is the best diet to help you lose weight and improve heart health in the short-term?
The Atkins Nutritional Approach
Atkins Nutritional Approach stresses dramatically reducing your intake of carbohydrates and replacing them with foods rich in protein and fat. The theory goes like this: "Your body burns carbohydrates and fat for energy. With Atkins unique approach, you limit carbs so your body burns fat."
What's Involved
The Atkinsdiet has 4 phases. During the induction phase, you eat 20-25 grams of carbohydrates per day, mostly from salad greens and other acceptable vegetables. This means no bread, pasta, rice, fruit, milk, yogurt, cereal, juice, cookies, sweets, or potatoes, among other foods. However, you can eat as much as you like from foods that are pure protein and fat, such as meats, eggs, oils, and butter. The induction phase lasts at least 2 weeks, and usually results in rapid weight loss.
The next 3 phases are ongoing weight loss, pre-maintenance, and lifetime maintenance. During these phases, you will slowly reintroduce carbs until your weight loss slows and, when you reach your goal, stays at that level. Then, you will monitor your weight, and if you gain, you take away carbs again until you lose the weight again. This means that your calorie intake and your weight is moderated by adjusting your carbohydrate intake. Protein and fat are not restricted. You are also advised to take a multivitamin and drink plenty of water each day.
The Research
In 2003, a study reported that people following the Atkins' diet lost more weight than a control group after 3 and 6 months, but that both groups had lost approximately the same amount of weight after 1 year. Since then, studies have continued to show that compared to other diets, a low-carbohydrate diet leads to greater weight loss at 6 months, but similar amounts of weight loss at 1 year. A 2009 study looked at 13 different studies and concluded that after 1 year on a low-carbohydrate diet, dieters tend to lose as much weight—if not more—as dieters on a traditional high-carbohydrate diet.
The Concerns
The Atkins diet is rich in high-protein and high-fat foods, such as bacon, steak, and cheese. While this eating pattern has not been shown to be harmful when followed for 1 year, many nutrition experts worry that the high levels of saturated and trans fats in these foods will increase your risk for heart disease if you follow the diet for an extended period of time. Information about the long-term effects of low-carbohydrate diets is still being collected. A 2009 study found that after 1 year, an Atkins-type diet increased both good and bad cholesterol. However, a 2010 study found that after 2 years, a low-carbohydrate diet improved cardiovascular disease risk factors more than a low-fat diet. There is concern that the high levels of protein can overload and damage the kidneys, which is especially dangerous for people with diabetes or kidney disease.
Weight Watchers PointsPlus
Weight Watchers PointsPlus program stresses better food choices and portion control for long-term weight loss.
What's Involved
On the PointsPlus program, you are allotted a daily PointsPlus target, which is based on a number of factors including your age and body weight. Every food has a PointsPlus value. You can eat whatever foods you want as long as you stay within your daily range. Weight Watchers encourages eating what it calls Power Foods—foods that are particularly nutritious and satisfying, such as lean protein, whole grains, and low- or fat-free dairy products. PointsPlus values are based on the amount of fat, carbohydrates, protein, and fiber in a food. The way your body processes these major nutrients is also taken into consideration. Fruits and most vegetables have zero PointsPlus values to encourage consumption of these nutrient-dense and satisfying foods.
The PointsPlus program also emphasizes exercise. When you exercise, you earn activity PointsPlus values, which may be swapped for food.
Weight Watchers recommends an average loss of up to 2 pounds (0.9 kilograms) a week for safe and sustained weight loss.
The Research
A 2003 study found that people following the Weight Watchers program lost significantly more than those on a self-help weight-loss program. At the end of 1 year, the people on the Weight Watchers program lost an average of 9.5 pounds (4.3 kilograms), while the people in the self-help program lost an average of 2.9 pounds (1.3 kilograms). After 2 years, the Weight Watchers group lost an average of 6.4 pounds (2.9 kilograms) and the self-help group lost an average of 0.4 pounds (0.2 kilograms). Again, losing weight, followed by some weight gain, is typical. However, a net weight loss after 2 years—no matter how much—is desirable.
The Concerns
Overall, the Weight Watchers PointsPlus program is a healthy one. In addition to cutting the amount of calories you consume and encouraging you to exercise, the diet encourages taking a daily multivitamin, drinking lots of water, and eating plenty of fruits, vegetables, and low-fat dairy products. It takes effort to log all the food you eat and exercise that you do. Fortunately, computer- and smart phone-based programs make it easier to calculate the PointsPlus value of foods and log activities. While the traditional version of the Weight Watchers program requires attendance at a weekly meeting, these days you can also choose an online-based program.
The South Beach Diet
The South Beach Diet, developed by cardiologist Arthur Agatston, MD, attempts to find a happy medium between low-carb and low-fat diets.
What's Involved
The South Beach Diet has 3 phases. During the 2 weeks of Phase 1, breads, rice, potatoes, pasta, baked goods, and even fruit are restricted. You can eat enough meat, chicken, turkey, fish, vegetables, eggs, cheese, nuts, and salads with olive oil dressing to make you feel full. The goal of phase 1 is to eliminate cravings and kick-start weight loss.
Phase 2, which you will stay on until you reach your weight-loss goal, is less restrictive. You can add carbs back into your diet, but in moderation. The focus is on whole-grain carbs, but you can now splurge on refined carbs every once in a while.
After you have reached your weight-loss goal, you will enter into Phase 3, which is the maintenance stage. In this phase, you are encouraged to focus on monounsaturated and polyunsaturated fats instead of saturated and trans fats, and whole-grain carbs instead of refined ones.
Although exercise is not a main focus of this diet, Dr. Agatston recommends that you take a brisk, 20-minute walk each day. Dr. Agatston claims that you will lose 8-13 pounds (3.6-5.9 kilograms) in Phase 1, and 1-2 pounds per week in Phase 2.
The Research
The South Beach Diet relies on Dr. Agatston's own research. A randomized, clinical trial published in 2004 compared the South Beach Diet with the National Cholesterol Education Program diet. Sixty participants were randomized and the study lasted for 12 weeks. Participants in the South Beach Diet had significantly greater weight loss over 12 weeks. However, since the study was small and, more importantly, since Dr. Agatston himself conducted the study, more research is needed to determine whether the South Beach Diet is effective.
A 2010 study compared the effectiveness of different types of diets and found that a diet similar in make-up to the South Beach Diet led to an improved diet adherence and weight-loss maintenance compared to other diets.
The Concerns
While the strengths of this diet focuses on healthful fats and whole grains, Phase 1 is extremely restrictive and may cause many enthusiastic dieters to give up. Also, the promise that you will lose 8-13 pounds (3.6-5.9 kilograms) during the first 2 weeks is questionable. Even if it were true, most health professionals recommend no more than a couple of pounds a week for safe, long-term weight loss.
Bottom Line
So it seems that while there are many effective diets out there, the one that is best for you is the one that you will stick with. That means looking at the components of all these diets and finding the diet that best meets your needs. For example, if you are a bread lover, the Atkins diet probably is not going to work for you, and if you do not like to be bothered with logging your food intake, than the Weight Watchers program probably is not your best fit. If you are feeling overwhelmed or confused by all the options out there, consider working with a registered dietitian to develop a customized plan that is your best fit.
Revision Information
- Reviewer: Michael Woods, MD, FAAP
- Review Date: 04/2017 -
- Update Date: 05/01/2017 -


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Since writer/director John Hughes passed away recently, I thought I'd use some quotes from some of his best films (as a writer and director) to help me preview this year's Michigan Wolverine football team. Earlier I previewed the offense and defense. Today, special teams and the overall outlook:
Punt Team/Kickoff Teams
"Breakfast Club"
Richard Vernon: You ought to spend a little more time trying to make something of yourself and a little less time trying to impress people.
Well, we all know about the awesomely named Zoltan Mesko. He is already on the watch list for the Ray Guy Award, given annually to the best collegiate punter. Suffice to say, I think this is the only single position on the entire team that I have complete confidence in - rightly or wrongly. Zoltan will boot the ball downfield. I think RichRod should be able to find enough athletes to sufficiently cover his massive mortar shots into enemy territory. The return game is another matter. As Michigan fans, we have been pretty spoiled the last few years. Steve Breaston was steady, and at times spectacular. He was always a threat to take a punt for a TD. We've also had the likes of Charles Woodson, Leon Hall, and Desmond Howard back there in the recent past, so I think our expectations are pretty high. Last year shattered those expectations to the point that I was happy if the return man held onto the ball. I don't want to look at the exact numbers because it's too depressing, but we fumbled lots of punts last hear. Martavious Odoms (he did return one for a TD) probably was the most dangerous, but also had butterfingers. So RR put Greg Matthews, our most sure-handed receiver, back there. And then he fumbled. So did Donovan Warren when he gave it a shot. No one distinguished themselves as a reliable option as a return specialist, so my hopes for this year is for someone to catch the ball and hold onto it like it's his last dollar. The potential return men this year are Odoms, Warren, Matthews, Kelvin Grady, Cissoko, and all of the shifty little slot receivers and RBs (V. Smith, Gallon, T. Robinson, Roundtree, T. Jones, etc.). Just hold onto the damn ball.
For the kickoff team, Bryan Wright is the incumbent, and will probably hold onto the kickoff specialist job he performed pretty well last year. As for return men, see above. Hold onto the damn ball. It ain't gonna bite ya.
Quick Zoltan facts:
- He graduated this spring with a degree in finance and marketing
- Is currently working on his master's degree in sports management
- Was a Playboy All-American last season
"Ferris Bueller's Day Off"
Ferris: Don't worry about it, I don't even have a piece of shit. I have to envy yours.
This is a real mystery position. After KC Lopata (who made 10 of 15 tries) used up the last of his eligibility, we have a severe case of inexperience in the placekicking duties. There are seven (SEVEN!) kickers listed on the roster, and that is one position where you can realistically expect that a walk-on might be able to win a job. With that being said, I still expect Brendan Gibbons or Wright to win the job. However, for some of you the name Eddie Murray might ring a bell. The Wolverines have a "preferred walk-on" kicker by that name this year, but he is no relation to the former Detroit Lions kicker. He has a strong leg and might manage to take the kickoff duties from Wright. I would be satisfied to have a kicker with the success rate of Lopata last year. Worst case: the 2002 season (post-Epstein, pre-Rivas) was the year that started with Phil Brabbs (3-for-9) and Troy Neinberg (2-for-5), and ended with punter Adam Finley (7-for-10) taking over. If that is in the cards for this year, just shoot me now and get it over with. That Washington game almost gave me a heart attack.
"Ferris Bueller's Day Off"
Ed Rooney: I did not achieve this position in life by having some snot-nosed punk leave my cheese out in the wind.
Wow. Where to begin. Personally, I have a great deal of confidence in the head coach, Rich Rodriguez. He has a proven track record of turning around programs. The big leap typically occurs in year two. So, here we are in year two of the RichRod era. I expect big things. However, I understand why others have not embraced him: he's not a "Michigan man" (although his mentor, Don Nehlen, was); he's too nice to the media (many of the same people complained that Carr was too surly toward the media); etc. Some people are now pointing to the ridiculous "non-scandal" regarding offseason workouts - which will ultimately amount to nothing but a footnote at the end of a successful season. After a year in which the defense was a huge disappointment, RichRod made a bold move and hired Greg Robinson to coordinate the defense. Robinson has a proven track record of his own as a D-coordinator, despite his failings as the head man at Syracuse. I think Michigan has a coaching staff that will help them win games, this year and in the future.
Overall Outlook
"Breakfast Club"
John Bender: You know what I got for Christmas this year? It was a banner fuckin' year at the old Bender family. I got a carton of cigarettes. The old man grabbed me and said "Hey. Smoke up Johnny."
Okay, now for my prediction. I am pretty confident in the offense. With a new QB who is able to throw a reasonably accurate pass, an experienced offensive line that should have a grasp of the blocking schemes, and a deep stable of running backs, this offense could really be something to behold by midseason. The defense worries me, but bolstered by the addition of new defensive coordinator Greg Robinson, should show improvement in most areas, despite being somewhat young and inexperienced. The punting game is solid. The return game is questionable at best. The kicking is a mystery. The Big Ten conference is weak. They'll probably lose a couple they shouldn't, and win a couple they shouldn't. The team will take these new allegations of NCAA violations and band together and adopt an "us-against-the-world" mentality that has served so many teams so well over the years. I see a 9-3 season.
And just one last quote from "Planes,Trains, and Automobiles," because I couldn't fit it in anywhere else ("that's what she said"):
Del: If they told you wolverines would make good house pets, would you believe them?


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AnythingIT has been awarded a multi-year IT asset recovery/e-waste disposal contract with a major public utility. "As we enter this milestone 20th year of business, we continue to demonstrate our best practices are still among the highest standards in this emerging IT disposal/recovery industry." stated Vlad Stelmak COO of AnythingIT Inc. "We continue to demonstrate our unparalleled asset recovery services make a major impact in lowering customer total cost of ownership in IT while also maximizing their return on investment."
As the company reaches this milestone year, many exciting growth initiatives are set to be launched as the company experiences its impresive growth.


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First launched in 1995, the Corruption Perceptions Index has been widely credited with putting the issue of corruption on the international policy agenda.
What does a number mean to you? Each year we score countries on how corrupt their public sectors are seen to be. Our Corruption Perceptions Index sends a powerful message and governments have been forced to take notice and act.
Behind these numbers is the daily reality for people living in these countries. The index cannot capture the individual frustration of this reality, but it does capture the informed views of analysts, businesspeople and experts in countries around the world. How does your country score?


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Placebo Effect
The placebo effect is a beneficial health outcome resulting from a person's anticipation that an intervention—pill, procedure, or injection, for example—will help them. A clinician's style in interacting with patients also may bring about a positive response that is independent of any specific treatment.
For Consumers
General Information
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Systematic Reviews/Reviews/Meta-analyses (PubMed®)
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This page last modified September 24, 2017