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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
35 Hours
Testimonials (5)
the trainer had patience, and was eager to make sure we all understood the topics, the classes were fun to attend
Mamonyane Taoana - Road Safety Department
Course - Statistical Analysis using SPSS
Day 1 and Day 2 were really straight forward for me and really enjoyed that experience.
Mareca Sithole - Africa Health Research Institute
Course - R Fundamentals
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
Michael the trainer is very knowledgeable and skillful about the subject of Big Data and R. He is very flexible and quickly customize the training meeting clients' need. He is also very capable to solve technical and subject matter problems on the go. Fantastic and professional training!.
Xiaoyuan Geng - Ottawa Research and Development Center, Science Technology Branch, Agriculture and Agri-Food Canada
Course - Programming with Big Data in R
I enjoyed the Excel sheets provided having the exercises with examples. This meant that if Tamil was held up helping other people, I could crack on with the next parts.
