 Statistics and Data Analytics - Content Outline

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1. Introduction to Statistics
• Key Points
• Variability is everywhere.
• There are ethical considerations.
• Quality is paramount!

2. Basic Probability, Sampling, and Survey Methods
• Basic Probability
• Probability of Two or More Events
• Rules of Multiplication
• Bayes' Theorem
• Sampling
• Simple Random Sampling
• Systematic Random Sampling
• Stratified Random Sampling
• Sampling Error
• Probability Distributions
• Normal Distribution
• t-distribution
• f-distribution
• Survey Methods
• Sample Selection
• Instrument Design
• Data Collection
• Data Analysis

3. Data, Data, Data!
• Data Characteristics
• Quantitative and Qualitative
• Ordinal, Interval, Ratio
• Dataset Structure
• Cross-section, Time Series, Panel
• Getting to Know the Data
• Housekeeping (Cleaning and Coding)
• Inflating and Indices
• Creating an Index
• Annualized Percent Change
• Distribution
• Testing for Normality
• Examining Graphically
• Testing for Outliers
• Simple Outlier Statistic
• Boxplot
• Descriptive Statistics
• Range, Median, Mode, Mean, Variance, Standard Deviation, Quartiles
• Weighted vs Unweighted
• Big Data
• Analysis
• Build a Sensible Dataset

4. Analytical Tools
• Areas Under the Curve
• Confidence Intervals
• population mean
• population proportion
• One-Sample Hypothesis Testing
• Population mean testing (standard deviation unknown)
• Population mean testing (standard deviation known)
• Testing for proportion
• One-tail versus Two-tail tests
• Two-Sample Hypothesis Tests
• Paired (Dependent) Samples
• Other (Test of Means, Standard Deviation Known; Test of Means, Independent Samples)
• Regression Analysis
• Regression Equation
• Coefficient Estimates
• Correlation Coefficient
• Coefficient of Determination
• Statistical Significance (p-values)
• ANOVA (SSR, SSE)
• Standard Error of the Estimate
• Error Structure
• Multivariate Regression
• Coefficient Estimates
• Comparison to Simple Linear Regression Estimates
• Coefficient of Determination
• Statistical Significance (individual vs global)
• Discrete Methods
• Probit
• Logit

5. It's Not Just About the Numbers
• Planning a Project
• Client Considerations
• Communicating the Need for a Formal Statistical Process
• Communicating Results Effectively
• Point Estimates
• Forecasting
• Communicating Technical Concepts
• Causality vs Correlation
• Functional Form
• Elasticity

6. Common Errors and Diagnostics
• Concerns Regarding Hypothesis Testing
• Regression Diagnostics
• Perfect Collinearity
• Multicollinearity
• Endogeneity
• Heteroskedasticity
• Autocorrelation
• Model Sensitivity and Full Disclosure
• Common Sense vs Statistical Sense

• Monte Carlo Simulation

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