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Course Outline
Scientific Method, Probability & Statistics
- Brief history of statistics
- Understanding confidence in conclusions
- Probability and decision-making
Research Preparation (Determining 'What' and 'How')
- The big picture: Research as a process with inputs and outputs
- Data collection
- Questionnaires and measurement
- 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
- Correlation guessing simulation
- Properties of Pearson's r
- Computing Pearson's r
- Range restriction demonstration
- Variance Sum Law II
- Exercises
Probability
- Introduction
- Core concepts
- Conditional probability demonstration
- Gambler's Fallacy simulation
- Birthday paradox demonstration
- Binomial distribution
- Binomial distribution demonstration
- Base rates
- Bayes' Theorem demonstration
- Monty Hall problem demonstration
- 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
- 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
- Exercises
Estimation
- Introduction
- Degrees of freedom
- Characteristics of estimators
- Bias and variability simulation
- 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
- 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-distribution simulation
- Specific comparisons (correlated observations)
- Pairwise comparisons (correlated observations)
- Exercises
Power
- Introduction
- Example calculations
- Factors affecting statistical power
- 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
- Exercises
ANOVA
- Introduction
- ANOVA designs
- One-factor ANOVA (between-subjects)
- One-way demonstration
- Multi-factor ANOVA (between-subjects)
- Unequal sample sizes
- Post-hoc tests for ANOVA
- Within-subjects ANOVA
- Power of within-subjects designs demonstration
- Exercises
Chi Square
- Chi-square distribution
- One-way tables
- Distribution testing demonstration
- Contingency tables
- 2 x 2 table simulation
- Exercises
Case Studies
Analysis of selected case studies
Requirements
Participants must have a solid understanding of descriptive statistics (mean, average, standard deviation, variance) and a basic grasp of probability.
It is recommended to complete 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.
