Chapter 1  Inroduction to This Book  Debugged Codes What is statistics and why learn it?  Statistics, data science, machine learning, etc.  Target audience  Prerequisites  Using the code with this book  AI assistance
Chapter 2  What are (is?) data?  Debugged Codes Is "data" singular or plural?  Where do data come from, what do they mean?  What do data look like?  Limitations of data  Accuracy, precision, resolution, range  Data types  From anecdotes to populations  Data management  The ethics of making up data
Chapter 3  Visualizing data  Debugged Codes Why visualize data?  How to visualize data  Bar plots  Pie charts  Box plots  Histograms  Lines vs. bars in a histogram  Violin plots  Linear vs. logarithmic axis scaling  Discretizing continuous data  Radial plots  Color
Chapter 4  Descriptive statistics  Debugged Codes Descriptive vs. inferential statistics  Data distributions  Central tendency  Measures of dispersion  Interquartile range (IQR)  QQ plots  Statistical "moments"  Histograms part 2: Number of bins
Chapter 5  Simulating Data  Debugged Codes Why simulate data?  Random data from distributions  Random elements of a set  Random permutations  Reproducing randomness  Running experiments with random numbers  The amazing world of datasimulations  Finding publicly available real datasets
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Chapter 6  Transformations  Debugged Codes What, why, and how of data transformations  Zscore standardization  Minmax normalization  Zscoring vs. minmax scaling  Percent change  Nonlinear data transformations  Interpreting transformed data
Chapter 7  Data Quality Matters  Debugged Codes Data quality influences datadriven decisions  Data cleaning phases  Assessing data quality  Improving data quality through transformations  What are outliers?  Identifying outliers  Analysisbased solutions to outliers  Missing data
Chapter 8  Probability Theory  Debugged Codes From descriptive to inferential statistics  What is probability?  Probability vs. proportion  Computing probabilities  Probability functions, mass, and density  Cumulative distribution function (cdf)  Expected value  Softmax
Chapter 9  Sampling and Distribution  Debugged Codes Sampling variability and its annoyances  Creating sample estimate distributions  Standard error of the mean  Random and representative sampling  The Law of Large Numbers  The Central Limit Theorem
Chapter 10  Hypothesis Testing  Debugged Codes Hypotheses  IVs, DVs, models, and other stats lingo  Can you prove a hypothesis?  Sample distributions under H_{0} and H_{A}  Where do H0 distributions come from?  Pvalues: definition and misinterpretations  Pvalues and significance categorization  TypeI and TypeII errors  Various interpretations of "significant"  Multiple comparisons  Degrees of freedom
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