Course Outline

Module 1: Introduction to Statistical Analysis

• The importance of careful experimental design
• Variable Classification
• Overview of statistical analysis
• Parametric Versus Nonparametric Analyses
• Case Study: Designing an Experiment to Test a New Drug

Module 2: Probability and Distributions

• Review of Probability
• Common Distributions (Binomial, Poisson, Gaussian, etc.)
• Parameters describing distributions (Central Tendency, Spread, etc.)
• Central Limit Theorem
• Case Study: Analyzing the Probability of Success in a Marketing Campaign

Module 3: Exploratory Data Analysis (EDA)

• Univariate Non-Graphical and Graphical EDA
• Multivariate Non-Graphical and Graphical EDA
• Covariance and Correlation
• Cross-Tabulation
• Case Study: Exploring a Dataset from a Customer Satisfaction Survey

Module 4: Software Tools for Data Analysis

• Overview of SPSS, Stata, Excel, and SAS
• Data Entry and Import
• Creating and Recoding Variables
• Graphical and Non-Graphical EDA in Software
• Case Study: Performing Data Analysis Using SPSS for a Retail Dataset

Module 5: Hypothesis Testing

• t-Test (Independent and Paired)
• One-Way ANOVA
• Assumption Checking and Results Interpretation
• Threats to Experiment Validity
• Case Study: Comparing Customer Retention Rates Between Two Different Marketing Strategies

Module 6: Regression Analysis

• Simple Linear Regression
• Multiple Regression and Interaction
• Analysis of Covariance (ANCOVA)
• Regression Calculations and Interpretation
• Case Study: Analyzing the Impact of Advertising Spend on Sales

Module 7: Advanced ANOVA Techniques

• Two-Way ANOVA
• Interpreting Two-Way ANOVA Results
• Application Areas and Practical Examples
• ANCOVA with Interaction
• Case Study: Evaluating the Effect of Different Training Programs on Employee Performance

Module 8: Statistical Power and Effect Size

• Understanding Statistical Power
• Improving Power and Calculating Effect Sizes
• Power Calculations for ANOVA and Regression
• Practical Considerations for Power Analysis
• Case Study: Planning a Study to Determine the Sample Size Needed for Reliable Results

Module 9: Contrasts and Mixed Models

• Contrasts and Custom Hypotheses
• Within-Subjects Designs and Paired t-Test
• Mixed Models and Their Applications
• Model Selection and Penalized Likelihood Methods
• Case Study: Analyzing a Clinical Trial with Repeated Measures

Module 10: Categorical Data Analysis

• Contingency Tables and Chi-Square Analysis
• Logistic Regression
• Testing Independence and Goodness-of-Fit
• Predictions in Logistic Regression Models
• Case Study: Predicting Customer Churn Using Logistic Regression