Traditional Statistics Alignment with CourseKata
A mapping of traditional statistics topics and where they are covered in CourseKata.
Last updated: 8/25/22
Topic Alignment, by BookĀ
Topics Covered Aligned with Data Science I (AB)Ā
ProbabilityĀ
Law of large numbers (chapters 2, 3, 4)Ā
Sampling with and without replacement (2, 3)Ā
Contingency tables (2, 3, 4)Ā
Research methodsĀ
Sampling (2, 3, 4)
Measurement: categorical v. quantitative variables (2)
Organizing data (2)Ā
Research design: correlational v. experimental (4)Ā
Correlation, causality, and confounding (4, 7, 8)Ā
Descriptive statisticsĀ
Univariate visualizations: histograms, box plots, bar graphs (3)
Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (4)
Summary statistics:Ā
center (mean, median, mode) (3, 5)Ā
shape (skew, normal, uniform, multimodal) (3, 5)Ā
spread (standard deviation, sums of squares, variance) (3, 5, 6)Ā
five number summary (3)Ā
regression, correlation coefficient (8)
Quantitative and categorical predictors; quantitative outcomes (2)Ā
Z score (6)Ā
Inferential statistics and sampling distributionsĀ
Mathematical distributions
Probability under mathematical distributions (6)Ā
normal/Z distribution, t distribution, F distribution (6)Ā
Computational techniquesĀ
Simulation (2, 4, 6)
Bootstrapping (3)Ā
Randomization (4)
Hypothesis testing
ANOVA (7, 8)Ā
Regression (8)Ā
Type I and Type II error (4)Ā
Concepts of power and effect size (7)Ā
Organizing concepts (throughout textbook)Ā
Modeling: DATA = MODEL + ERRORĀ
General linear model; GLM notation
Data analysis using RĀ
Topics Covered Aligned with Advanced Data Science I (ABC)Ā
ProbabilityĀ
Law of large numbers (chapters 2, 3, 4, 9, 10, 11)Ā
Sampling with and without replacement (2, 3)Ā
Contingency tables (2, 3, 4)Ā
Research methodsĀ
Sampling (2, 3, 4)
Measurement: categorical v. quantitative variables (2)Ā
Organizing data (2)Ā
Research design: correlational v. experimental (4, 9)Ā
Correlation, causality, and confounding (4, 7, 8, 9, 10)Ā
Descriptive statisticsĀ
Univariate visualizations: histograms, box plots, bar graphs (3)Ā
Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (4)Ā
Summary statistics:Ā
center (mean, median, mode) (3, 5)Ā
shape (skew, normal, uniform, multimodal) (3, 5)Ā
spread (standard deviation, sums of squares, variance) (3, 5, 6)
five number summary (3)Ā
regression, correlation coefficient (8)Ā
Quantitative and categorical predictors; quantitative outcomes (2)Ā
Z score (6)
Inferential statistics and sampling distributionsĀ
Mathematical distributions
Probability under mathematical distributions (6, 9, 10, 11)Ā
Central limit theorem (9)Ā
normal/Z distribution, t distribution, F distribution (6, 9, 10, 11)Ā
Computational techniquesĀ
Simulation (2, 4, 6, 9, 10, 11)Ā
Bootstrapping (3, 9, 10, 11)Ā
Randomization (4, 9, 10, 11)
Hypothesis testing
t-test (9)Ā
ANOVA (7, 8, 10)Ā
Regression (8, 10)Ā
Type I and Type II error (4, 10, 11)Ā
Concepts of power and effect size (7, 10, 11)Ā
Confidence intervals (11)Ā
Organizing concepts (throughout textbook)Ā
Modeling: DATA = MODEL + ERRORĀ
General linear model; GLM notation
Data analysis using RĀ
Topics Covered Aligned with Statistics and Data Science II (XCD)Ā
ProbabilityĀ
Law of large numbers (chapters 4, 5, 6)Ā
Sampling with and without replacement (4, 5, 6)Ā
Contingency tables (1, 2)Ā
Research methodsĀ
Sampling (1, 2, 4, 5, 6)Ā
Measurement: categorical v. quantitative variables (1)Ā
Organizing data (1)Ā
Research design: correlational v. experimental (2, 3)Ā
Correlation, causality, and confounding (2, 3, 4, 5, 6, 7, 8)Ā
Descriptive statisticsĀ
Univariate visualizations: histograms, box plots, bar graphs (1)Ā
Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (2, 7)Ā
Summary statistics:Ā
center (mean, median, mode) (1, 2)Ā
shape (skew, normal, uniform, multimodal) (1)Ā
spread (standard deviation, sums of squares, variance) (1, 2, 3)Ā
five number summary (1)Ā
regression, correlation coefficient (3)Ā
Quantitative and categorical predictors; quantitative outcomes (2, 3)Ā
Z score (covered in AB)Ā
Inferential statistics and sampling distributionsĀ
Mathematical distributionsĀ
Probability under mathematical distributions (4, 5, 6)Ā
Central limit theorem (4)Ā
normal/Z distribution, t distribution, F distribution (4, 5, 6)Ā
Computational techniquesĀ
Simulation (4, 5, 6, 7, 8)Ā
Bootstrapping (6, 7, 8)Ā
Randomization (4, 5, 6, 7, 8)Ā
Hypothesis testingĀ
t-test (4)Ā
ANOVA (3, 4, 5)Ā
Regression (3, 4, 5)Ā
Type I and Type II error (4, 5, 6)Ā
Concepts of power and effect size (3, 4, 5, 6)Ā
Confidence intervals (7)Ā
Organizing concepts (throughout textbook)Ā
Modeling: DATA = MODEL + ERRORĀ
General linear model; GLM notationĀ
Data analysis using RĀ
Ā