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Course Outline
Scientific Method, Probability & Statistics
- A concise history of statistics
- Building confidence in research 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
- Identifying 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
- Understanding Pearson Correlation Values
- Guessing Correlations Simulation
- Properties of Pearson's r
- Calculating Pearson's r
- Restriction of Range Demonstration
- Variance Sum Law II
- 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
- Exercises
Normal Distributions
- Introduction
- Historical Context
- Areas Under Normal Distributions
- Varieties of Normal Distribution Demonstration
- The 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- 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 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)
- Handling Unequal Sample Sizes
- Supplemental Tests for ANOVA
- Within-Subjects ANOVA
- Power of Within-Subjects Designs Demonstration
- Exercises
Chi Square
- Chi Square Distribution
- One-Way Tables
- Testing Distributions Demonstration
- Contingency Tables
- 2 x 2 Table Simulation
- Exercises
Case Studies
Analysis of selected case studies
Requirements
Participants must possess a strong understanding of descriptive statistics (including mean, average, standard deviation, and variance) and a fundamental knowledge of probability.
It is advisable to first take 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.
