{'fraction_non_alphanumeric': 0.016366612111292964, 'fraction_numerical': 0.0, 'mean_word_length': 5.1457286432160805, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Regression and correlation analyses.

Abstract
 This chapter focuses on regression and correlation analyses. Correlation and regression analyses are used to test whether, and to what degree, variation in one continuous variable is related to variation in another continuous variable. In correlation analysis, there are no control over either variable, they are just data collected, and indeed, even if two variables are strongly correlated, they may not be influencing one another but simply both being affected by a third which perhaps was not measured. The initial assumption of the analysis is that the values of both variables are drawn from a normal distribution. In regression analysis one of the variables are being controlled seeing whether changing its value affects the other. The variable being controlled is the explanatory variable (sometimes called the treatment) and the other is the response variable. As the explanatory variables are being controlled, they are probably going to be set at specified values or set increments and are therefore not normally distributed. There may be more than one explanatory variable. If all the explanatory variables are categorical then the regression is called an ANOVA.