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Course Outline
Scientific Method, Probability & Statistics
- A brief overview of the history of statistics
- Understanding the basis for confidence in conclusions
- Probability and decision-making processes
Research Preparation (Determining "What" and "How")
- The Big Picture: Viewing research as a process with inputs and outputs
- Data collection strategies
- Questionnaires and measurement techniques
- Identifying what 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
- The Variance Sum Law II
- Practice exercises
Probability
- Introduction to probability
- Fundamental concepts
- Demonstration: Conditional probability
- Simulation: The Gambler's Fallacy
- Demonstration: The Birthday Problem
- The Binomial Distribution
- Demonstration of Binomial concepts
- Understanding base rates
- Demonstration: Bayes' Theorem
- Demonstration: The Monty Hall Problem
- Practice exercises
Normal Distributions
- Introduction to normal distributions
- Historical context
- Exploring areas under normal distributions
- Demonstration: Varieties of normal distributions
- The Standard Normal distribution
- Normal approximation to the binomial distribution
- Demonstration of normal approximation
- Practice exercises
Sampling Distributions
- Introduction to sampling distributions
- Basic demonstration
- Demonstration: The impact of sample size
- Demonstration: The Central Limit Theorem
- The sampling distribution of the mean
- The sampling distribution of the difference between means
- The sampling distribution of Pearson's r
- The sampling distribution of a proportion
- Practice exercises
Estimation
- Introduction to estimation
- Understanding degrees of freedom
- Characteristics of estimators
- Simulation: Bias and variability
- Confidence intervals
- Practice exercises
The Logic of Hypothesis Testing
- Introduction to hypothesis testing
- Significance testing
- Type I and Type II errors
- One-tailed and two-tailed tests
- Interpreting significant results
- Interpreting non-significant results
- Steps involved in hypothesis testing
- The relationship between significance testing and confidence intervals
- Common misconceptions
- Practice exercises
Testing Means
- Single mean testing
- Demonstration: The t-distribution
- Comparing two means (independent groups)
- Simulation: Robustness
- All pairwise comparisons among means
- Specific comparisons
- Comparing two means (correlated pairs)
- Simulation: Correlated t-tests
- Specific comparisons (correlated observations)
- Pairwise comparisons (correlated observations)
- Practice exercises
Statistical Power
- Introduction to statistical power
- Example calculations
- Factors influencing power
- Practice exercises
Prediction
- Introduction to simple linear regression
- Demonstration: Linear fit
- Partitioning sums of squares
- Standard error of the estimate
- Demonstration: The prediction line
- Inferential statistics for slope (b) and correlation (r)
- Practice exercises
ANOVA
- Introduction to ANOVA
- ANOVA designs
- One-Factor ANOVA (Between-Subjects)
- Demonstration: One-way ANOVA
- Multi-Factor ANOVA (Between-Subjects)
- Handling unequal sample sizes
- Tests that supplement ANOVA
- Within-Subjects ANOVA
- Demonstration: Power in within-subjects designs
- Practice exercises
Chi-Square
- The Chi-Square distribution
- One-way tables
- Demonstration: Testing distributions
- Contingency tables
- Simulation: 2 x 2 tables
- Practice exercises
Case Studies
Analysis of selected case studies
Requirements
Participants are expected to have a strong understanding of descriptive statistics (including mean, average, standard deviation, and variance) and a basic familiarity with probability.
It is recommended that you consider taking the preparatory 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.
