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Course Outline
Scientific Method, Probability & Statistics
- A brief historical overview of statistics
- Understanding the basis for "confidence" in research conclusions
- The role of probability in decision-making
Preparing for Research (Determining "What" and "How")
- The Big Picture: 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
- 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
- Exercises
Probability
- Introduction
- Core concepts
- Demonstration: Conditional probability
- Simulation: The Gambler's Fallacy
- Demonstration: The Birthday Problem
- Binomial distribution
- Demonstration: Binomial probability
- Understanding base rates
- Demonstration: Bayes' Theorem
- Demonstration: The Monty Hall Problem
- Exercises
Normal Distributions
- Introduction
- Historical context
- Calculating areas under normal distributions
- Demonstration: Varieties of normal distributions
- The standard normal distribution
- Normal approximation to the binomial
- Demonstration: Normal approximation
- Exercises
Sampling Distributions
- Introduction
- Basic demonstration
- Demonstration: Impact 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
- Exercises
Estimation
- Introduction
- Degrees of freedom
- Characteristics of estimators
- Simulation: Bias and variability
- Confidence intervals
- Exercises
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
- Significance testing and confidence intervals
- Common misconceptions
- Exercises
Testing Means
- Single mean tests
- Demonstration: t-distribution
- Comparing two means (independent groups)
- Simulation: Robustness
- All pairwise comparisons among means
- Specific comparisons
- Comparing two means (correlated pairs)
- Simulation: Correlated t
- Specific comparisons (correlated observations)
- Pairwise comparisons (correlated observations)
- Exercises
Power
- Introduction
- Example calculations
- Factors affecting statistical power
- 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
- Exercises
ANOVA
- Introduction
- ANOVA designs
- One-factor ANOVA (between-subjects)
- Demonstration: One-way ANOVA
- Multi-factor ANOVA (between-subjects)
- Handling unequal sample sizes
- Supplemental tests for ANOVA
- Within-subjects ANOVA
- Demonstration: Power of within-subjects designs
- Exercises
Chi Square
- Chi-square distribution
- One-way tables
- Demonstration: Testing distributions
- Contingency tables
- Simulation: 2 x 2 tables
- Exercises
Case Studies
Analysis of selected real-world case studies
Requirements
Participants must have a solid grasp of descriptive statistics (including mean, average, standard deviation, and variance) and a fundamental understanding of probability.
For those needing foundational knowledge, we recommend 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
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The real life applications using Statcan and CER as examples.
