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Course Outline
Scientific Method, Probability & Statistics
- Brief historical overview of statistics
- Understanding the basis for confident conclusions
- Probability and its role in decision-making
Research Preparation (Determining 'What' and 'How')
- The broader context: research as a process with inputs and outputs
- Strategies for data collection
- Questionnaires and measurement techniques
- Identifying variables to measure
- Observational studies
- Experimental design
- Data analysis and graphical methods
- Essential research skills and techniques
- Research management strategies
Analyzing Bivariate Data
- Introduction to bivariate data
- Interpreting Pearson Correlation values
- Correlation guessing simulation
- Properties of Pearson's r
- Calculating Pearson's r
- Demonstration of range restriction
- The Variance Sum Law II
- Practical exercises
Probability Theory
- Introduction to probability
- Fundamental concepts
- Conditional probability demonstration
- Gambler's Fallacy simulation
- Birthday problem demonstration
- Binomial distribution
- Binomial distribution demonstration
- Understanding base rates
- Bayes' Theorem demonstration
- Monty Hall problem demonstration
- Practical exercises
Normal Distributions
- Introduction to normal distributions
- Historical context
- Calculating areas under normal distributions
- Varieties of normal distribution demonstration
- The standard normal distribution
- Normal approximation to the binomial distribution
- Normal approximation demonstration
- Practical exercises
Sampling Distributions
- Introduction to sampling distributions
- 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
- Practical exercises
Estimation Techniques
- Introduction to estimation
- Understanding degrees of freedom
- Characteristics of estimators
- Bias and variability simulation
- Confidence intervals
- Practical exercises
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
- Step-by-step hypothesis testing process
- Relationship between significance testing and confidence intervals
- Common misconceptions
- Practical exercises
Testing Means
- Single mean analysis
- t-distribution demonstration
- Comparing two means (independent groups)
- Robustness simulation
- All pairwise comparisons among means
- Specific comparisons
- Comparing two means (correlated pairs)
- Correlated t-simulation
- Specific comparisons (correlated observations)
- Pairwise comparisons (correlated observations)
- Practical exercises
Statistical Power
- Introduction to statistical power
- Example calculations
- Factors influencing power
- Practical exercises
Prediction Models
- 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
- Practical exercises
ANOVA (Analysis of Variance)
- Introduction to ANOVA
- ANOVA experimental designs
- One-Factor ANOVA (Between-Subjects)
- One-way ANOVA demonstration
- Multi-Factor ANOVA (Between-Subjects)
- Handling unequal sample sizes
- Post-hoc tests to supplement ANOVA
- Within-Subjects ANOVA
- Power of within-subjects designs demonstration
- Practical exercises
Chi-Square Tests
- Chi-square distribution
- One-way tables
- Testing distributions demonstration
- Contingency tables
- 2 x 2 table simulation
- Practical exercises
Case Studies
Detailed analysis of selected real-world case studies
Requirements
Participants must possess a solid grasp of descriptive statistics, including mean, average, standard deviation, and variance, along with a foundational understanding of probability concepts.
For those needing foundational knowledge, we recommend attending 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.
