This is the main methodology and first-results chapter. It opens with an introduction to the lexeme-based approach used for the investigation, contrasting this to previous, variationist approaches. The chapter proceeds to explain the data retrieval and screening processes and presents an overview of the data, the nearly 65,000 intensifier tokens found in the corpus, across the three main categories (maximizers, boosters, downtoners), and the descriptive results across time for the most frequent items. The word counts of the different sociopragmatic groups of speakers (divided by speakers’ role in the courtroom, gender and social class) are introduced, as well as the diachronic distribution of intensifiers across the genders and social classes. Results are presented within the descriptive statistics framework, but the chapter also briefly introduces the regression model, or the inferential, multivariate statistical method to be used in Chapters 8–11 to disentangle the complex interplay of the sociopragmatic variables of speakers on the use of intensifiers.

This chapter covers the two topics of descriptive statistics and the normal distribution. We first discuss the role of descriptive statistics and the measures of central tendency, variance, and standard deviation. We also provide examples of the kinds of graphs often used in descriptive statistics. We next discuss the normal distribution, its properties and its role in descriptive and inferential statistical analysis.

The chapter ’#StatsWithCats’ shows some statistical methods to interpret and visualise the cat-related online data. The selected sociolinguistic variables are the social media platforms and the cat account types. The chapter takes frequencies and crosstabs to describe linguistic variation across four social media platforms and four cat account types. The selected linguistic variables refer to the choices of non-meowlogisms and meowlogisms on Facebook, Instagram, Twitter, and Youtube as well as in collective, for-profit celebrity, working-for-cause, and individual cat accounts. Additionally, the chapter uses social network analysis to illustrate the networks in cat-related digital spaces.

This chapter discusses two types of descriptive statistics: models of central tendency and models of variability. Models of central tendency describe the location of the middle of the distribution, and models of variability describe the degree that scores are spread out from one another. There are four models of central tendency in this chapter. Listed in ascending order of the complexity of their calculations, these are the mode, median, mean, and trimmed mean. There are also four principal models of variability discussed in this chapter: the range, interquartile range, standard deviation, and variance. For the latter two statistics, students are shown three possible formulas (sample standard deviation and variance, population standard deviation and variance, and population standard deviation and variance estimated from sample data), along with an explanation of when it is appropriate to use each formula. No statistical model of central tendency or variability tells you everything you may need to know about your data. Only by using multiple models in conjunction with each other can you have a thorough understanding of your data.

Chapter 3 describes in detail the data sources and research designs used throughout the book, including observational data sources, experiments on national samples of American citizens, and panel surveys tracking the same people over time. It also summarizes aggregate public opinion on key variables through time, including approval, confidence, trust, procedural perceptions, and broadly targeted and narrowly targeted Court-curbing. The chapter concludes that the Court’s “reservoir of goodwill” within the American public is not as deep or wide as many scholars suggest.

This chapter discusses Feature Engineering techniques that look holistically at the feature set, therefore replacing or enhancing the features based on their relation to the whole set of instances and features. Techniques such as normalization, scaling, dealing with outliers and generating descriptive features are covered. Scaling and normalization are the most common, it involves finding the maximum and minimum and changing the values to ensure they will lie in a given interval (e.g., [0, 1] or [−1, 1]). Discretization and binning involve, for example, analyzing a feature that is an integer (any number from -1 trillion to +1 trillion) and realize that it only takes the values 0, 1 and 10 so it can be simplified into a symbolic feature with three values (value0, value1 and value10). Descriptive features is the gathering of information that talks about the shape of the data, the discussion centres around using tables of counts (histograms) and general descriptive features such as maximum, minimum and averages. Outlier detection and treatment refers to looking at the feature values across many instances and realizing some values might present themselves very far from the rest.

This chapter presents a detailed example that applies the compensation analytics concepts developed in Chapter 6. The reader is assumed to be a compensation consultant charged with evaluating whether gender-based discrimination in pay is present in a public university system in the sciences. Section 7.1 walks through the analysis step-by-step, from formulating the business question, to acquiring and cleaning data, to analyzing the data and interpreting the results from voluminous statistical output in light of the business question. Section 7.2 covers exploratory data mining, causality, and experiments. Exploratory data mining covers situations in which the manager does not know in advance which relationships in the data will be of interest, in contrast to the example in section 7.1 in which a statistical model and specific measures could be constructed that were directly tailored to address the business question at hand. Section 7.2 covers the challenges associated with establishing causality in compensation research and how experiments can sometimes be designed to address those challenges. Randomization and some pitfalls associated with compensation experiments are also covered

This chapter presents a detailed example that applies the compensation analytics concepts developed in Chapter 6. The reader is assumed to be a compensation consultant charged with evaluating whether gender-based discrimination in pay is present in a public university system in the sciences. Section 7.1 walks through the analysis step-by-step, from formulating the business question, to acquiring and cleaning data, to analyzing the data and interpreting the results from voluminous statistical output in light of the business question. Section 7.2 covers exploratory data mining, causality, and experiments. Exploratory data mining covers situations in which the manager does not know in advance which relationships in the data will be of interest, in contrast to the example in section 7.1 in which a statistical model and specific measures could be constructed that were directly tailored to address the business question at hand. Section 7.2 covers the challenges associated with establishing causality in compensation research and how experiments can sometimes be designed to address those challenges. Randomization and some pitfalls associated with compensation experiments are also covered

This chapter responds to the growing importance of business analytics on "big data" in managerial decision-making, by providing a comprehensive primer on analyzing compensation data. All aspects of compensation analytics are covered, starting with data acquisition, types of data, and formulation of a business question that can be informed by data analysis. A detailed, hands-on treatment of data cleaning is provided, equipping readers to prepare data for analysis by detecting and fixing data problems. Descriptive statistics are reviewed, and their utility in data cleaning explicated. Graphical methods are used in examples to detect and trim outliers. The basics of linear regression analysis are covered, with an emphasis on application and interpreting results in the context of the business question(s) posed. One section covers the question of whether or not the pay measure (as a dependent variable) should be transformed via a logarithm, and the implications of that choice for interpreting the results are explained. Precision of regression estimates is covered via an intuitive, non-technical treatment of standard errors. An appendix covers nonlinear relationships among variables.

This chapter responds to the growing importance of business analytics on "big data" in managerial decision-making, by providing a comprehensive primer on analyzing compensation data. All aspects of compensation analytics are covered, starting with data acquisition, types of data, and formulation of a business question that can be informed by data analysis. A detailed, hands-on treatment of data cleaning is provided, equipping readers to prepare data for analysis by detecting and fixing data problems. Descriptive statistics are reviewed, and their utility in data cleaning explicated. Graphical methods are used in examples to detect and trim outliers. The basics of linear regression analysis are covered, with an emphasis on application and interpreting results in the context of the business question(s) posed. One section covers the question of whether or not the pay measure (as a dependent variable) should be transformed via a logarithm, and the implications of that choice for interpreting the results are explained. Precision of regression estimates is covered via an intuitive, non-technical treatment of standard errors. An appendix covers nonlinear relationships among variables.

This chapter provides a basic introduction to descriptive statistics in the context of neuroscience experiments.