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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 big picture: Research as a process with inputs and outputs
- Data collection strategies
- Questionnaires and measurement techniques
- Selecting variables to measure
- Observational studies
- Experimental design
- Data analysis and graphical methods
- Research skills and techniques
- Research management
Describing Bivariate Data
- Introduction to Bivariate Data
- Pearson Correlation coefficients
- Correlation estimation simulation
- Properties of Pearson's r
- Calculating Pearson's r
- Range restriction demonstration
- Variance Sum Law II
- Practice exercises
Probability
- Introduction
- Core concepts
- Conditional probability demonstration
- Gambler's Fallacy simulation
- Birthday problem demonstration
- Binomial distribution
- Binomial demonstration
- Base rates
- Bayes' Theorem demonstration
- Monty Hall problem demonstration
- Practice exercises
Normal Distributions
- Introduction
- Historical context
- Areas under Normal Distributions
- Varieties of Normal Distribution demonstration
- Standard Normal distribution
- Normal approximation to the Binomial
- Normal approximation demonstration
- Practice exercises
Sampling Distributions
- Introduction
- Basic demonstration
- Sample size demonstration
- Central Limit Theorem demonstration
- Sampling distribution of the mean
- Sampling distribution of the difference between means
- Sampling distribution of Pearson's r
- Sampling distribution of a proportion
- Practice exercises
Estimation
- Introduction
- Degrees of freedom
- Characteristics of estimators
- Bias and variability simulation
- Confidence intervals
- Practice exercises
Logic of Hypothesis Testing
- Introduction
- Significance testing
- Type I and Type II errors
- One- and two-tailed tests
- Interpreting significant results
- Interpreting non-significant results
- Steps in hypothesis testing
- Significance testing and confidence intervals
- Common misconceptions
- Practice exercises
Testing Means
- Single mean testing
- t-distribution demonstration
- Difference between two means (independent groups)
- Robustness simulation
- All pairwise comparisons among means
- Specific comparisons
- Difference between two means (correlated pairs)
- Correlated t simulation
- Specific comparisons (correlated observations)
- Pairwise comparisons (correlated observations)
- Practice exercises
Power Analysis
- Introduction
- Example calculations
- Factors affecting power
- Practice exercises
Prediction
- Introduction to simple linear regression
- Linear fit demonstration
- Partitioning sums of squares
- Standard error of the estimate
- Prediction line demonstration
- Inferential statistics for b and r
- Practice exercises
ANOVA
- Introduction
- ANOVA designs
- One-factor ANOVA (between-subjects)
- One-way demonstration
- Multi-factor ANOVA (between-subjects)
- Handling unequal sample sizes
- Supplemental tests for ANOVA
- Within-subjects ANOVA
- Power of within-subjects designs demonstration
- Practice exercises
Chi-Square
- Chi-square distribution
- One-way tables
- Testing distributions demonstration
- Contingency tables
- 2 x 2 table simulation
- Practice exercises
Case Studies
Analysis of selected case studies
Requirements
Participants must possess a solid understanding of descriptive statistics (including mean, average, standard deviation, and variance) and a foundational knowledge of probability.
Those who wish to prepare may consider attending the preliminary 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.
