# Statistics, Hypothesis Testing, & Regression Modeling

## Using Mintab software

A little background, motivation, course overview, and a welcome from the instructor, Steven Wachs

## Training Objectives

The objective of the curriculum is to provide participants with the analytical tools and methods necessary to:

Note: Relevant modules are shown in parentheses.

- Describe and summarize data effectively with descriptive statistics and graphical methods (1,2)
- Utilize appropriate probability distributions to describe data (1)
- Correctly compare groups with respect to means, variability, and proportions by testing hypotheses (e.g. whether the groups have a statistically significant difference) (3,4,5)
- Estimate key statistics and quantify uncertainty (confidence intervals) (6)
- Apply Equivalence Testing to determine if groups are the same from a practical perspective (3,4)
- Characterize expected process variation based on sample data (tolerance intervals) (3)
- Determine appropriate sample sizes to achieve adequate power for hypothesis tests and equivalence tests (6)
- Determine appropriate sample sizes for estimation of key statistics (6)
- Handle discrete data by using common discrete data distributions (7)
- Conduct Chi-Square tests for relationships between categorical variables (7)
- Apply Non-Parametric Hypothesis Tests when assumptions for parametric tests are violated (8)
- Assess whether continuous variables have a significant relationship (correlation) (9)
- Develop, validate, and utilize predictive models for continuous responses (9,10)
- Develop and validate Regression Models with Binary, Ordinal, or Nominal responses (11)

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