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 

  1. ProbabilityĀ 

    1. Law of large numbers (chapters 2, 3, 4)Ā 

    2. Sampling with and without replacement (2, 3)Ā 

    3. Contingency tables (2, 3, 4)Ā 

  2. Research methodsĀ 

    1. Sampling (2, 3, 4)

    2. Measurement: categorical v. quantitative variables (2)

    3. Organizing data (2)Ā 

    4. Research design: correlational v. experimental (4)Ā 

    5. Correlation, causality, and confounding (4, 7, 8)Ā 

  3. Descriptive statisticsĀ 

    1. Univariate visualizations: histograms, box plots, bar graphs (3)

    2. Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (4)

    3. Summary statistics:Ā 

      1. center (mean, median, mode) (3, 5)Ā 

      2. shape (skew, normal, uniform, multimodal) (3, 5)Ā 

      3. spread (standard deviation, sums of squares, variance) (3, 5, 6)Ā 

      4. five number summary (3)Ā 

      5. regression, correlation coefficient (8)

    4. Quantitative and categorical predictors; quantitative outcomes (2)Ā 

    5. Z score (6)Ā 

  4. Inferential statistics and sampling distributionsĀ 

    1. Mathematical distributions

      1. Probability under mathematical distributions (6)Ā 

      2. normal/Z distribution, t distribution, F distribution (6)Ā 

    2. Computational techniquesĀ 

      1. Simulation (2, 4, 6)

      2. Bootstrapping (3)Ā 

      3. Randomization (4)

    3. Hypothesis testing

      1. ANOVA (7, 8)Ā 

      2. Regression (8)Ā 

      3. Type I and Type II error (4)Ā 

      4. Concepts of power and effect size (7)Ā 

  5. Organizing concepts (throughout textbook)Ā 

    1. Modeling: DATA = MODEL + ERRORĀ 

    2. General linear model; GLM notation

    3. Data analysis using RĀ 

Topics Covered Aligned with Advanced Data Science I (ABC)Ā 

  1. ProbabilityĀ 

    1. Law of large numbers (chapters 2, 3, 4, 9, 10, 11)Ā 

    2. Sampling with and without replacement (2, 3)Ā 

    3. Contingency tables (2, 3, 4)Ā 

  2. Research methodsĀ 

    1. Sampling (2, 3, 4)

    2. Measurement: categorical v. quantitative variables (2)Ā 

    3. Organizing data (2)Ā 

    4. Research design: correlational v. experimental (4, 9)Ā 

    5. Correlation, causality, and confounding (4, 7, 8, 9, 10)Ā 

  3. Descriptive statisticsĀ 

    1. Univariate visualizations: histograms, box plots, bar graphs (3)Ā 

    2. Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (4)Ā 

    3. Summary statistics:Ā 

      1. center (mean, median, mode) (3, 5)Ā 

      2. shape (skew, normal, uniform, multimodal) (3, 5)Ā 

      3. spread (standard deviation, sums of squares, variance) (3, 5, 6)

      4. five number summary (3)Ā 

      5. regression, correlation coefficient (8)Ā 

    4. Quantitative and categorical predictors; quantitative outcomes (2)Ā 

    5. Z score (6)

  4. Inferential statistics and sampling distributionsĀ 

    1. Mathematical distributions

      1. Probability under mathematical distributions (6, 9, 10, 11)Ā 

      2. Central limit theorem (9)Ā 

      3. normal/Z distribution, t distribution, F distribution (6, 9, 10, 11)Ā 

    2. Computational techniquesĀ 

      1. Simulation (2, 4, 6, 9, 10, 11)Ā 

      2. Bootstrapping (3, 9, 10, 11)Ā 

      3. Randomization (4, 9, 10, 11)

    3. Hypothesis testing

      1. t-test (9)Ā 

      2. ANOVA (7, 8, 10)Ā 

      3. Regression (8, 10)Ā 

      4. Type I and Type II error (4, 10, 11)Ā 

      5. Concepts of power and effect size (7, 10, 11)Ā 

    4. Confidence intervals (11)Ā 

  5. Organizing concepts (throughout textbook)Ā 

    1. Modeling: DATA = MODEL + ERRORĀ 

    2. General linear model; GLM notation

    3. Data analysis using RĀ 

Topics Covered Aligned with Statistics and Data Science II (XCD)Ā 

  1. ProbabilityĀ 

    1. Law of large numbers (chapters 4, 5, 6)Ā 

    2. Sampling with and without replacement (4, 5, 6)Ā 

    3. Contingency tables (1, 2)Ā 

  2. Research methodsĀ 

    1. Sampling (1, 2, 4, 5, 6)Ā 

    2. Measurement: categorical v. quantitative variables (1)Ā 

    3. Organizing data (1)Ā 

    4. Research design: correlational v. experimental (2, 3)Ā 

    5. Correlation, causality, and confounding (2, 3, 4, 5, 6, 7, 8)Ā 

  3. Descriptive statisticsĀ 

    1. Univariate visualizations: histograms, box plots, bar graphs (1)Ā 

    2. Bivariate visualizations: frequency tables, faceted histograms, scatterplots, bar graphs, box plots (2, 7)Ā 

    3. Summary statistics:Ā 

      1. center (mean, median, mode) (1, 2)Ā 

      2. shape (skew, normal, uniform, multimodal) (1)Ā 

      3. spread (standard deviation, sums of squares, variance) (1, 2, 3)Ā 

      4. five number summary (1)Ā 

      5. regression, correlation coefficient (3)Ā 

    4. Quantitative and categorical predictors; quantitative outcomes (2, 3)Ā 

    5. Z score (covered in AB)Ā 

  4. Inferential statistics and sampling distributionsĀ 

    1. Mathematical distributionsĀ 

      1. Probability under mathematical distributions (4, 5, 6)Ā 

      2. Central limit theorem (4)Ā 

      3. normal/Z distribution, t distribution, F distribution (4, 5, 6)Ā 

    2. Computational techniquesĀ 

      1. Simulation (4, 5, 6, 7, 8)Ā 

      2. Bootstrapping (6, 7, 8)Ā 

      3. Randomization (4, 5, 6, 7, 8)Ā 

    3. Hypothesis testingĀ 

      1. t-test (4)Ā 

      2. ANOVA (3, 4, 5)Ā 

      3. Regression (3, 4, 5)Ā 

      4. Type I and Type II error (4, 5, 6)Ā 

      5. Concepts of power and effect size (3, 4, 5, 6)Ā 

    4. Confidence intervals (7)Ā 

  5. Organizing concepts (throughout textbook)Ā 

    1. Modeling: DATA = MODEL + ERRORĀ 

    2. General linear model; GLM notationĀ 

    3. Data analysis using RĀ 

Ā 

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