Business-Analytics-Using-Computational-Statistics

1 Introduction to Computation

Computational Statistics in Data Science Our Tools: RStudio First Look at R

2 Visualizing, Describing, and Simulating Data

Centrality and Dispersion Kernel Density Plots / Histograms Population vs. Sample Statistics Simulating Data from Distributions

3 Confidence Intervals & The Bootstrap

Describing Distributions Confidence Intervals Bootstrapping

4 Tests Revisited

Review of Descriptives Classical Hypothesis Testing Bootstrapping the Alternative Random Walks

5 Simulating Null vs Alternative Distributions

Revisiting the t-distribution Bootstrapped Hypothesis Testing Revisiting and Bootstrapping the F-distribution Relationships in Data

6 Analysis of Variance

One-Way ANOVA Bootstrapping ANOVA Design of Experiments A/B Experiments

7 Experiments, Error, and Power

Factorial Design Inference and Error Statistical Power Rethinking Statistical Significance

9 Data Similarity

Tests and Power Similarity: Cosine, Correlation Item-Item Collaborative Filtering Collaborative filtering at Amazon Dangers of statistical mistakes Causal Modeling

10 Linear Regression

Tests and Power Similarity: Cosine, Correlation Item-Item Collaborative Filtering Simulation:
Regression and Scatterplots

11 Regression Deep Dive

Model Fit Non-Linearity & Multi-Collinearity Confidence vs. Prediction Intervals Modeling Higher Dimensional Data

12 Moderation and Mediation

Moderation and Interactions Multicollinearity Partial Orthogonalization

13 Principal Components Analysis

Non-linear Relationships Confidence Intervals Prediction Intervals Interactive Simulation: PCA

14 Multidimensional Variables

Composite Variables Principal Components Analysis (PCA)

15 Measurement Models

Rotation and Interpretation Parallel Analysis: more than just noise? Creating and Testing Complex Models

16 PLS Path Modeling

Components vs. Factors
Structural Equation Modeling SEMinR

17 TBA