
Statistics and Data Analytics - Content Outline
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- Introduction to Statistics
- Key Points
- Variability is everywhere.
- There are ethical considerations.
- Quality is paramount!
- Basic Probability, Sampling, and Survey Methods
- Basic Probability
- Probability of Two or More Events
- Rules of Addition
- 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
- 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
- Annualized Percent Change
- Distribution
- Testing for Normality
- Testing for Outliers
- Simple Outlier Statistic
- Boxplot
- Descriptive Statistics
- Range, Median, Mode, Mean, Variance, Standard Deviation, Quartiles
- Weighted vs Unweighted
- Adjusted Weights
- Big Data
- 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
- It's Not Just About the Numbers
- Planning a Project
- Communicating the Need for a Formal Statistical Process
- Communicating Results Effectively
- Point Estimates
- Forecasting
- Communicating Technical Concepts
- Causality vs Correlation
- Functional Form
- Elasticity
- 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
- Additional Applications