Transcript Document

One-Way and Two-way Analyses of Variance
PSBE Chapters 14, 15 and Guan Chapter 9
Research Questions
Income and allocation?
Do people in different counties earn same income?
Obviously it is not, how can we test?
Research Questions
Income and gender?
Do men and women earn same income?
Do industrial and service industries offer same salary
Obviously it is not, how can we test?
38,467 /月
28,156 /月
42,006 /月
36,998 /月
One-Way Analysis of Variance
The idea of ANOVA (變異數分析)
A factor (因子) is a variable that can take one of several levels used to
differentiate one group from another.
An experiment has a one-way or completely randomized design (完
全隨機設計) if several levels of one factor are being studied and the
individuals are randomly assigned to its levels. (There is only one way
to group the data.)
Example: Which of four advertising offers mailed to sample households
produces the highest sales?
Will a lower price in a plain mailing draw more sales on average than a
higher price in a fancy brochure? Analyzing the effect of price and layout
together requires two-way ANOVA.
Analysis of variance (ANOVA) is the technique used to determine
whether more than two population means are equal.
One-way ANOVA is used for completely randomized, one-way designs.
The ANOVA setting: comparing means
We want to know if the observed differences in sample means are likely
to have occurred by chance just because of the random sampling.
This will likely depend on both the difference between the sample
means and how much variability there is within each sample.
The two-sample t statistic
A two sample t test assuming equal variance and an ANOVA comparing only
two groups will give you the exact same p-value (for a two-sided hypothesis).
H0: m1 = m2
Ha: m1 ≠ m2
H0: m1 = m2
Ha: m1 ≠ m2
One-way ANOVA
t test assuming equal variance
t statistic
F = t2 and both p-values are the same.
But the t test is more flexible: You may choose a one-sided alternative instead,
or you may want to run a t test assuming unequal variance if you are not sure
that your two populations have the same standard deviation s.
The ANOVA model
Random sampling always produces chance variations. Any “factor
effect” would thus show up in our data as the factor-driven differences
plus chance variations (“error”):
Data = fit (“factor/groups”) + residual (“error”)
The one-way ANOVA model analyses
situations where chance variations are
normally distributed N(0,σ) so that:
Testing hypotheses in one-way ANOVA
We have I independent SRSs, from I populations or treatments.
The ith population has a normal distribution with unknown mean µi.
All I populations have the same standard deviation σ, unknown.
The ANOVA F statistic tests:
SSG ( I  1)
SSE ( N  I )
H0: m1 = m2 = … = mI
Ha: not all the mi are equal
When H0 is true, F has the F
distribution with I − 1 (numerator)
and N − I (denominator) degrees of
The ANOVA F-test
The ANOVA F-statistic compares variation due to specific sources
(levels of the factor) to variation among individuals who should be
similar (individuals in the same sample).
variation among sample means
variation among individual s in same sample
Difference in
means large
relative to
overall variability
Difference in
means small
relative to
overall variability
 F tends to be small
 F tends to be large
Larger F-values typically yield more significant results. How large depends on
the degrees of freedom (I − 1 and N − I).
Checking our assumptions
Each of the #I populations must be normally distributed (histograms
or normal quantile plots). But the test is robust to normality deviations
for large enough sample sizes, thanks to the central limit theorem.
The ANOVA F-test requires that all populations have the same
standard deviation s. Since s is unknown, this can be hard to check.
Practically: The results of the ANOVA F-test are approximately
correct when the largest sample standard deviation is no more than
twice as large as the smallest sample standard deviation.
(Equal sample sizes also make ANOVA more robust to deviations from the equal σ rule)
The ANOVA table
Source of variation
Sum of squares
Mean square
F crit
Among or between
i i
I -1
Tail area
above F
Value of
F for a
Within groups or
 (ni  1)si
 x )2
Coefficient of determination
√MSE = sp
Pooled standard deviation
The sum of squares represents variation in the data: SST = SSG + SSE.
The degrees of freedom likewise reflect the ANOVA model: DFT = DFG + DFE.
Data (“Total”) = fit (“Groups”) + residual (“Error”)
Case: Do eyes affect ad response?
A study investigated the affect of the color of a model’s eyes in a viewer’s
response to an ad. In three groups of students, viewers see a picture of the
model looking directly at the camera, with only the eye color being changed.
A fourth group of students saw a picture of the model looking down.
The table below summarizes the responses :
Verifying conditions for ANOVA
Side-by-side boxplots of the scores for the four groups.
The data appear relatively symmetric with some skewness.
Verifying conditions for ANOVA
Normal quantile plots of the scores for the four groups.
The data looks reasonably Normal.
ANOVA results
The pooled standard deviation sp is reported as 1.677. The value of F is
2.89, with a P-value of 0.036. We have evidence at the 5% significance
level to reject the null hypotheses that the four populations have equal
means. There is evidence that the four groups of students do not all
have the same mean attitude score.
Using Table E
The F distribution is asymmetrical and has two distinct degrees of
freedom. This was discovered by Fisher, hence the label “F.”
Once again, what we do is calculate the value of F for our sample data
and then look up the corresponding area under the curve in Table E.
Yogurt preparation and taste
Yogurt can be made using three distinct commercial preparation
methods: traditional, ultra filtration, and reverse osmosis.
To study the effect of these methods on taste, an experiment was
designed in which three batches of yogurt were prepared for each of
the three methods. A trained expert tasted each of the nine samples
(presented in random order), and judged them on a scale of 1 to 10.
Variables, hypotheses, assumptions, calculations?
ANOVA table
Source of variation
Between groups
Within groups
17.3 I-1=2
4.6 N-I=6
F crit
dfnum = I − 1
Computation details
MSG SSG ( I  1)
MSE SSE ( N  I )
MSG, the mean square for groups, measures how different the individual
means are from the overall mean (~ weighted average of square distances of
sample averages to the overall mean). SSG is the sum of squares for groups.
MSE, the mean square for error, is the pooled sample variance sp2 and
estimates the common variance σ2 of the I populations (~ weighted average of
the variances from each of the I samples). SSE is the sum of squares for error.
Comparing Group Means and the Power of
the ANOVA Test
You have calculated a p-value for your ANOVA test. Now what?
If you found a significant result, you still need to determine which
treatments were different from which.
You can gain insight by looking back at your plots (boxplot, mean ± s).
There are several tests of statistical significance designed specifically for
multiple tests. You can choose, apriori, contrasts or, aposteriori,
multiple comparisons.
You can find the confidence interval for each mean mi shown to be
significantly different from the others.
Contrasts can be used only when there are clear expectations
BEFORE starting an experiment, and these are reflected in the
experimental design. Contrasts are planned comparisons.
Patients are given either drug A, drug B, or a placebo. The three
treatments are not symmetrical. The placebo is meant to provide a
baseline against which the other drugs can be compared.
Multiple comparisons should be used when there are no justified
expectations. Those are aposteriori, pair-wise tests of significance.
We compare gas mileage for eight brands of SUVs. We have no prior
knowledge to expect any brand to perform differently from the rest. Pairwise comparisons should be performed here, but only if an ANOVA test
on all eight brands reached statistical significance first.
It is NOT appropriate to use a contrast test when suggested
comparisons appear only after the data is collected.
Contrasts: planned comparisons
When an experiment is designed to test a specific hypothesis that
some treatments are different from other treatments, we can use
contrasts to test for significant differences between these specific
Contrasts are more powerful than multiple comparisons because they
are more specific. They are better able to pick up a significant difference.
You can use a t test on the contrasts or calculate a t confidence interval.
The results are valid regardless of the results of your multiple sample
ANOVA test (you are still testing a valid hypothesis).
A contrast is a combination of
population means of the form:
   ai mi
where the coefficients ai have sum 0.
To test the null hypothesis
H0:  = 0 use the t statistic:
t  c SEc
with degrees of freedom DFE that is
associated with sp. The alternative
hypothesis can be one or two sided.
The corresponding sample contrast is :
c   ai xi
The standard error of c is:
SEc  s p
 n  MSE  n
A level C confidence interval for
the difference  is:
c  t * SEc
where t* is the critical value defining
the middle C% of the t distribution
with DFE degrees of freedom.
Contrasts are not always readily available in statistical software
packages (when they are, you need to assign the coefficients “ai”), or
they may be limited to comparing each sample to a control.
If your software doesn’t provide an option for contrasts, you can test
your contrast hypothesis with a regular t test using the formulas we just
highlighted. Remember to use the pooled variance and degrees of
freedom as they reflect your better estimate of the population variance.
Then you can look up your p-value in a table of t distribution.
[SAS code]
proc glm data = ….;
class b;
model y = b;
means b /deponly;
contrast 'Compare 3rd & 4th grp' b 0 0 1 -1;
contrast 'Compare 1st & 2nd with 3rd & 4th grp' b 1 1 -1 -1; contrast 'Compare 1st,
2nd & 3rd grps with 4th grp' b 1 1 1 -3;
Evaluation of the new product
A study compares the reading
comprehension (“COMP,” a test
score) of children randomly
assigned to one of three teaching
methods: basal, DRTA, and
We test:
H0: µBasal = µDRTA = µStrat
Ha: H0 not true
The ANOVA test is significant (α = 5%): we have found evidence that the three
methods do not all yield the same population mean reading comprehension score.
Evaluation of the new product (cont.)
The two new methods are based on the same idea. Are they
superior to the standard method? We can formulate this question as:
H01: ½(µDRTA + µStrat) = µBasal
Ha1: ½(µDRTA + µStrat) > µBasal
Are the DRTA and Strat methods equally effective? We formulate
this as:
H02: µDRTA = µStrat
Ha2: µDRTA ≠ µStrat
Evaluation of the new product (cont.)
Output for contrasts for the comprehension scores in the new-product
evaluation study:
The P-values are correct for two-sided alternative hypothesis. To convert
the results to apply to our one-sided alternative Ha1, simply divide the
reported P-value by two after checking that the sample contrast is positive.
There is strong evidence against H01, i.e., the new methods produce higher
mean scores. However, there is not sufficient evidence against H02, i.e.,
the DRTA and Strat methods appear equally effective.
Multiple comparisons*
Multiple comparison tests are variants on the two-sample t test.
They use the pooled standard deviation sp = √MSE.
The pooled degrees of freedom DFE.
And they compensate for the multiple comparisons.
We compute the t statistic
for all pairs of means:
A given test is significant (µi and µj significantly different), when
|tij| ≥ t** (df = DFE).
The value of t** depends on which procedure you choose to use.
The Bonferroni procedure*
The Bonferroni procedure performs a number of pair-wise
comparisons with t tests and then multiplies each p-value by the
number of comparisons made. This ensures that the probability of
making any false rejection among all comparisons made is no greater
than the chosen significance level α.
As a consequence, the higher the number of pair-wise comparisons you
make, the more difficult it will be to show statistical significance for each
test. But the chance of committing a type I error also increases with the
number of tests made. The Bonferroni procedure lowers the working
significance level of each test to compensate for the increased chance of
type I errors among all tests performed.
Simultaneous confidence intervals*
We can also calculate simultaneous level C confidence intervals for
all pair-wise differences (µi − µj) between population means:
CI : ( xi  x j )  t * *s p
1 1
ni n j
sp is the pooled variance, MSE.
t** is the t critical with degrees of freedom DFE = N – I, adjusted for
multiple, simultaneous comparisons (e.g., Bonferroni procedure).
What do you conclude?
The three methods do not yield the same results: We found evidence of a
significant difference between DRTA and basal methods (DRTA gave better
results on average), but the data gathered does not support the claim of a
difference between the other methods (DRTA vs. strategies or basal vs.
The power, or sensitivity, of a one-way ANOVA is the probability that
the test will be able to detect a difference among the groups (i.e., reach
statistical significance) when there really is a difference.
Estimate the power of your test while designing your experiment to
select sample sizes appropriate to detect an amount of difference
between means that you deem important.
Too small a sample is a waste of experiment, but too large a sample is
also a waste of resources.
A power of at least 80% is often suggested.
Power computations*
ANOVA power is affected by:
the significance level a
the sample sizes and number of groups being compared
the differences between group means µi
the guessed population standard deviation
You need to decide what alternative Ha you would consider important to
detect statistically for the means µi and to guess the common standard
deviation σ (from similar studies or preliminary work).
The power computations then require calculating a noncentrality
parameter λ, which follows the F distribution with DFG and DFE
degrees of freedom to arrive at the power of the test.
Two-way ANOVA
The Two-Way ANOVA Model and Inference
for Two-Way ANOVA
Two-way designs
In a two-way design, two factors (independent variables) are studied in
conjunction with the response (dependent) variable. Thus, there are
two ways of organizing the data, as shown in a two-way table:
two-way table (3 by 4
design to test the
design of a magazine)
When the dependent variable is quantitative, the data is analyzed with
a two-way ANOVA procedure. A chi-square test is used instead if the
dependent variable is categorical.
Advantages of a two-way ANOVA model
It is more efficient to study two factors at once than separately.
A two-way design requires smaller sample sizes per condition than a
series of one-way designs would, because the samples for all levels of
factor B contribute to sampling for factor A.
Including a second factor thought to influence the response variable
helps reduce the residual variation in a model of the data.
In a one-way ANOVA for factor A, any effect of factor B is assigned to
the residual (“error” term). In a two-way ANOVA, both factors contribute
to the fit part of the model.
Interactions between factors can be investigated.
The two-way ANOVA breaks down the fit part of the model between
each of the main components (the two factors) and an interaction effect.
The interaction cannot be tested with a series of one-way ANOVAs.
Two variables interact if a particular combination of variables leads to
results that would not be anticipated on the basis of the main effects of
those variables.
People of white descent have a higher per capita income than those of
Asian descent. People from the Northeast have a higher per capita
income than those from the Midwest. These are main effects.
An interaction implies that the effect of one variable differs depending
on the level of another variable.
The ethnicity earnings gap is different in the two regions, so ethnicity
and region interact.
An interaction implies that the effect of one variable differs depending
on the level of another variable.
Plot of per capita
incomes of whites
and Asians in two
U.S. regions
The main effect is seen in the different lines.
Interaction is shown by the lack of parallelism.
An interaction implies that the effect of one variable differs depending
on the level of another variable.
Plot of per capita
incomes of whites
and Asians in two
U.S. regions
The main effect is seen in the different lines.
Lack of interaction is shown by the parallel lines.
The two-way ANOVA model
We record a quantitative variable in a two-way design with I levels
of the first factor and J levels of the second factor.
We have independent SRSs from each of I  J Normal populations.
Sample sizes do not have to be identical (although many software
only carry out the computations when sample sizes are equal 
“balanced design”).
All parameters are unknown. The population means may be
different but all populations have the same standard deviation σ.
Assumptions for a two-way ANOVA
Main effects and interaction effect
Each factor is represented by a main effect: this is the impact on the
response (dependent variable) of varying levels of that factor,
regardless of the other factor (i.e., pooling together the levels of the
other factor). There are two main effects, one for each factor.
The interaction of both factors is also studied and is described by the
interaction effect.
When there is no clear interaction, the main effects are enough to
describe the data. In the presence of interaction, the main effects
could mask what is really going on with the data.
Major types of two-way ANOVA outcomes
In a two-way design, statistical significance can be found for each
factor, for the interaction effect, or for any combination of these.
Neither factor is
Only one factor is
Both factors are
No interaction
Interaction effect is
No interaction
With or without
significant interaction
Dependent var.
Neither factor is
Levels of factor B:
Levels of factor A
Dependent var.
Levels of factor A
Levels of factor A
Levels of factor A
Inference for two-way ANOVA
A one-way ANOVA tests the following model of your data:
Data (“total”) = fit (“groups”) + residual (“error”)
So that the sum of squares and degrees of freedom are:
A two-way design breaks down the “fit” part of the model into more
specific subcomponents, so that:
Where A and B are the two main effects from each of the two factors,
and AB represents the interaction of factors A and B.
The two-way ANOVA table
Source of
Sum of squares
square MS
Factor A
DFA = I -1
for FA
Factor B
DFB = J - 1
for FB
DFAB = (I-1)(J-1)
for FAB
DFE = N - IJ
DFT = N – 1
Main effects: P-value for factor A, P-value for factor B.
Interaction: P-value for the interaction effect of A and B.
Error: It represents the variability in the measurements within the groups.
MSE is an unbiased estimate of the population variance s2.
Significance tests
Example: Discounts and expected prices
Does the frequency with which a supermarket product is offered at a
discount affect the price that customers expect to pay for the product? Does
the percent reduction also affect this expectation? We examine the data for
two levels of promotion (1 and 3) and two levels of discount (40% and 20%).
Thus, we have a two-way ANOVA with each factor having two levels, and
10 observations in each of the four treatment combinations.
When promotions are
increased from 1 to 3,
expected price drops
from $4.56 to $4.40.
When the discount is
increased from 20% to
40%, expected price
drops from $4.61 to
Example: Discounts and expected prices
Plot of the means for the promotions and discount example: the
two lines are approximately parallel, which suggests that there is
little interaction between promotion and discount.
Example: Discounts and expected prices
Two-way ANOVA output from Minitab for the promotions and discount
example: as expected, the interaction is not statistically significant (P
= 0.856). However, the main effects of discount (P = 0.001) and
promotion (P = 0.04) are significant.