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 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

         

  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
      • Adjusted Weights
    • 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

       

  7. Additional Applications
  • Monte Carlo Simulation

 

 

 


 

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