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Course Outline
Scientific Method, Probability & Statistics
- A very brief history of statistics
- How we can be "confident" about conclusions
- Probability and decision-making
Preparation for research (deciding "what" and "how")
- The big picture: research as part of a process with inputs and outputs
- Gathering data
- Questionnaires and measurement
- What to measure
- Observational studies
- Design of experiments
- Data analysis 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
- Gambler's fallacy simulation
- Birthday demonstration
- Binomial distribution
- Binomial demonstration
- Base rates
- Bayes' theorem demonstration
- Monty Hall problem demonstration
- Exercises
Normal Distributions
- Introduction
- History
- Areas under 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 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
- 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
A solid understanding of descriptive statistics (mean, average, standard deviation, variance) and a basic grasp of probability are required.
You may wish to attend the preparation course: Statistics Level 1
35 Hours
Testimonials (3)
knowledge of the trainer, tailor based, all topics covered
eleni - EUAA
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