Thank you for sending your enquiry! One of our team members will contact you shortly.
Thank you for sending your booking! One of our team members will contact you shortly.
Course Outline
Scientific Method, Probability & Statistics
- A concise history of statistics
- Foundations of confidence in statistical conclusions
- Probability and its role in decision-making
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 for measurement
- Observational studies
- Experimental design
- Data analysis and graphical representation
- Essential 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
- Calculating Pearson's r
- Range restriction demonstration
- Variance Sum Law II
- Exercises
Probability
- Introduction to probability concepts
- Core concepts and definitions
- Conditional probability demonstration
- Gambler's Fallacy simulation
- Birthday problem demonstration
- Binomial distribution
- Binomial demonstration
- Base rates
- Bayes' Theorem demonstration
- Monty Hall problem demonstration
- Exercises
Normal Distributions
- Introduction to normal distributions
- Historical context
- Areas under normal distribution curves
- Varieties of normal distributions demonstration
- The standard normal distribution
- Normal approximation to the binomial distribution
- Normal approximation demonstration
- 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
- Exercises
Estimation
- Introduction to estimation
- Degrees of freedom
- Characteristics of estimators
- Bias and variability simulation
- Confidence intervals
- 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
- Steps in hypothesis testing
- Significance testing and confidence intervals
- Common misconceptions
- Exercises
Testing Means
- Single mean tests
- 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)
- Exercises
Power Analysis
- Introduction to statistical power
- Example calculations
- Factors affecting 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 slope (b) and correlation (r)
- Exercises
ANOVA
- Introduction to Analysis of Variance
- ANOVA designs
- One-factor ANOVA (Between-Subjects)
- One-way ANOVA demonstration
- Multi-factor ANOVA (Between-Subjects)
- Handling unequal sample sizes
- Post-hoc tests supplementing ANOVA
- Within-Subjects ANOVA
- Power of within-subjects designs demonstration
- Exercises
Chi-Square Tests
- Chi-square distribution
- One-way tables
- Testing distributions demonstration
- Contingency tables
- 2 x 2 table simulation
- 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.
It is recommended to complete the prerequisite 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.
