Unit 3964: Applied Statistics

1. Introduction to Applied Statistics

• Role of an applied statistician
• Importance of statistical analysis in decision-making
• Key concepts and tools in applied statistics

2. Experimental Design and Analysis

2.1 Principles of Experimental Design

• Randomization
• Replication
• Control

2.2 Analyzing Experimental Data

• Overview of statistical tests (t-tests, ANOVA, chi-square tests)
• Interpreting results and drawing conclusions

3. Regression Analysis

3.1 Simple Linear Regression

• Model formulation
• Estimating parameters
• Assumptions of linear regression

3.2 Multiple Regression

• Incorporating multiple predictors
• Multicollinearity and model diagnostics

3.3 Interpretation

• Coefficients and significance
• Making predictions
• Assessing model fit (R-squared, residual analysis)

4. Hypothesis Testing

• Formulating null and alternative hypotheses
• Choosing significance levels (alpha)
• Types of errors (Type I and Type II)
• Conducting tests (z-test, t-test, chi-square test)
• Interpreting p-values and confidence intervals

5. Data Visualization

• Importance of visual data representation
• Histograms and frequency distributions
• Box plots for distribution and outlier detection
• Scatter plots to show relationships between variables
• Other graphical methods (bar charts, heatmaps, pair plots)

6. Time Series Analysis

• Characteristics of time series data
• Trend analysis and decomposition
• Seasonal variations and cyclic patterns
• Forecasting methods
  • Moving averages
  • Exponential smoothing
• Evaluating forecast accuracy

7. Case Study: Marketing Analytics

• Applying statistical methods to marketing data
• Customer segmentation techniques
• A/B testing for campaign optimization
• Measuring marketing campaign effectiveness

8. Case Study: Healthcare Analytics

• Evaluating healthcare outcomes using statistics
• Assessing patient populations and risk factors
• Identifying factors influencing treatment effectiveness

9. Case Study: Financial Analytics

• Statistical analysis of financial data
• Risk assessment and management
• Portfolio optimization fundamentals
• Forecasting financial trends

10. Ethical Considerations in Applied Statistics

• Ethical issues in data collection
• Privacy and confidentiality concerns
• Bias and fairness in data and analysis
• Responsible use and interpretation of statistical results
• Transparency and reproducibility