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Course Outline
Scientific Method, Probability & Statistics
- A brief history of statistics
- Understanding the basis for confidence in conclusions
- Probability and decision-making processes
Research Preparation (Determining 'What' and 'How')
- The broader context: viewing research as a process with inputs and outputs
- Data collection strategies
- Questionnaires and measurement techniques
- Identifying variables to measure
- Observational studies
- Experimental design
- Data analysis and graphical methods
- Essential research skills and techniques
- Research management
Describing Bivariate Data
- Introduction to bivariate data
- Understanding Pearson Correlation values
- Simulation: Guessing Correlations
- Properties of Pearson's r
- Calculating Pearson's r
- Demonstration: Restriction of Range
- Variance Sum Law II
- Practical exercises
Probability
- Introduction
- Fundamental concepts
- Demonstration: Conditional Probability
- Simulation: The Gambler's Fallacy
- Demonstration: Birthday Problem
- The Binomial Distribution
- Demonstration: Binomial Distribution
- Understanding Base Rates
- Demonstration: Bayes' Theorem
- Demonstration: The Monty Hall Problem
- Practical exercises
Normal Distributions
- Introduction
- Historical background
- Calculating areas under Normal Distributions
- Demonstration: Varieties of Normal Distribution
- The Standard Normal Distribution
- Normal Approximation to the Binomial
- Demonstration: Normal Approximation
- Practical exercises
Sampling Distributions
- Introduction
- Basic demonstration
- Demonstration: Effect of Sample Size
- Demonstration: Central Limit Theorem
- Sampling Distribution of the Mean
- Sampling Distribution of the Difference Between Means
- Sampling Distribution of Pearson's r
- Sampling Distribution of a Proportion
- Practical exercises
Estimation
- Introduction
- Understanding Degrees of Freedom
- Characteristics of Estimators
- Simulation: Bias and Variability
- Confidence Intervals
- Practical exercises
The Logic of Hypothesis Testing
- Introduction
- Significance testing
- Type I and Type II Errors
- One-tailed and Two-tailed Tests
- Interpreting Significant Results
- Interpreting Non-Significant Results
- Steps in Hypothesis Testing
- Linking Significance Testing and Confidence Intervals
- Common Misconceptions
- Practical exercises
Testing Means
- Single Mean Analysis
- Demonstration: t Distribution
- Difference between Two Means (Independent Groups)
- Simulation: Robustness
- All Pairwise Comparisons Among Means
- Specific Comparisons
- Difference between Two Means (Correlated Pairs)
- Simulation: Correlated t
- Specific Comparisons (Correlated Observations)
- Pairwise Comparisons (Correlated Observations)
- Practical exercises
Power Analysis
- Introduction
- Example Calculations
- Factors Affecting Statistical Power
- Practical exercises
Prediction
- Introduction to Simple Linear Regression
- Demonstration: Linear Fit
- Partitioning Sums of Squares
- Standard Error of the Estimate
- Demonstration: Prediction Line
- Inferential Statistics for b and r
- Practical exercises
ANOVA
- Introduction
- ANOVA Designs Overview
- One-Factor ANOVA (Between-Subjects)
- Demonstration: One-Way ANOVA
- Multi-Factor ANOVA (Between-Subjects)
- Handling Unequal Sample Sizes
- Tests Supplementing ANOVA
- Within-Subjects ANOVA
- Demonstration: Power of Within-Subjects Designs
- Practical exercises
Chi Square
- Chi Square Distribution
- One-Way Tables
- Demonstration: Testing Distributions
- Contingency Tables
- Simulation: 2 x 2 Table
- Practical exercises
Case Studies
Analysis of selected case studies
Requirements
Participants must have a firm understanding of descriptive statistics (including mean, average, standard deviation, and variance) and a foundational knowledge of probability.
It is recommended that you attend the preparation course: Statistics Level 1
35 Hours
Testimonials (3)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
Course - Forecasting with R
The variation with exercise and showing.
Ida Sjoberg - Swedish National Debt Office
Course - Econometrics: Eviews and Risk Simulator
The real life applications using Statcan and CER as examples.
