Course Outline
Scientific Method, Probability & Statistics
- Very short history of statistics
- Why can be "confident" about the conclusions
- Probability and decision making
Preparation for research (deciding "what" and "how")
- The big picture: research is a part of a process with inputs and outputs
- Gathering data
- Questioners and measurement
- What to measure
- Observational Studies
- Design of Experiments
- Analysis of Data and Graphical Methods
- Research Skills and Techniques
- Research Management
Describing Bivariate Data
- Introduction to Bivariate Data
- Values of the Pearson Correlation
- Guessing Correlations Simulation
- Properties of Pearson's r
- Computing Pearson's r
- Restriction of Range Demo
- Variance Sum Law II
- Exercises
Probability
- Introduction
- Basic Concepts
- Conditional Probability Demo
- Gamblers Fallacy Simulation
- Birthday Demonstration
- Binomial Distribution
- Binomial Demonstration
- Base Rates
- Bayes' Theorem Demonstration
- Monty Hall Problem Demonstration
- Exercises
Normal Distributions
- Introduction
- History
- Areas of Normal Distributions
- Varieties of Normal Distribution Demo
- Standard Normal
- Normal Approximation to the Binomial
- Normal Approximation Demo
- Exercises
Sampling Distributions
- Introduction
- Basic Demo
- Sample Size Demo
- Central Limit Theorem Demo
- Sampling Distribution of the Mean
- Sampling Distribution of 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
- Misconceptions
- Exercises
Testing Means
- Single Mean
- t Distribution Demo
- 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 Power
- Exercises
Prediction
- Introduction to Simple Linear Regression
- Linear Fit Demo
- Partitioning Sums of Squares
- Standard Error of the Estimate
- Prediction Line Demo
- Inferential Statistics for b and r
- Exercises
ANOVA
- Introduction
- ANOVA Designs
- One-Factor ANOVA (Between-Subjects)
- One-Way Demo
- Multi-Factor ANOVA (Between-Subjects)
- Unequal Sample Sizes
- Tests Supplementing ANOVA
- Within-Subjects ANOVA
- Power of Within-Subjects Designs Demo
- Exercises
Chi Square
- Chi Square Distribution
- One-Way Tables
- Testing Distributions Demo
- Contingency Tables
- 2 x 2 Table Simulation
- Exercises
Case Studies
Analysis of selected case studies
Requirements
Solid understanding of descriptive statistics (mean, average, standard deviation, variance) and basic understanding of probability is required.
You may want to participate in preparation course: Statistics Level 1
Testimonials (8)
The pace was just right and the relaxed atmosphere made candidates feel at ease to ask questions.
Rhian Hughes - Public Health Wales NHS Trust
Course - Introduction to Data Visualization with Tidyverse and R
We were using road accident data for practicals
Maphahamiso Ralienyane - Road Safety Department
Course - Statistical Analysis using SPSS
Well thought out and high grade planning materials.
Andrew - Office of Projects Victoria - Department of Treasury & Finance
Course - Forecasting with R
Wasn't boring, the trainer could keep the attention, the topics were covered in depth.
Marta - Ministerstwo Zdrowia
Course - Advanced R Programming
Very tailored to needs.
Yashan Wang
Course - Data Mining with R
The subject matter and the pace were perfect.
Tim - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
At the end of the class, we had a great overview of the language, we were provided tools to continue learning and were provided suggestions on how to continue learning. We covered AI/ML information.
Victor Prado - Global Knowledge Network Training Ltd
Course - R
That Haytham started with the basics and gave us enough time to do the examples and ensure that we were at the same page before we moved on to the next topic.