This course is an introduction to descriptive statistics, probability and its applications, statistical inference and hypothesis testing, predictive statistics and linear regression.
Prerequisites
Quarters Offered
Fall,
Winter,
Spring,
Summer
Course Outcomes
Upon successful completion of the course, students should be able to demonstrate the following knowledge or skills:
- Determine appropriate methods to compute various probabilities
- Identify, analyze, and describe statistical distributions
- Use statistics to make population-level inferences
- Use linear regression to identify correlations and draw inferences
Institutional Outcomes
IO2: Students will be able to reason mathematically.
Course Content Outline
- Types of variables (numerical, categorical, explanatory, response, confounding, etc.)
- Sampling strategies
- Measures of center and spread
- Graphical displays of data
- Basic probability, including sample space and simple probabilities, disjoint and independent events, complementary events, multiplication rules/addition rules, marginal and conditional probabilities, expected value
- Normal and Binomial distributions
- Central Limit Theorem
- Hypothesis testing for
- 1 and 2 parameters
- Proportions, means, goodness-of-fit/independence
- ANOVA
- Correlation and simple linear regression
Optional Topics:
Simpson’s paradox
Study/Experimental design and its implications
Hypothesis testing for paired data
Poisson distribution
Bayes’ Theorem