#### About STAT 143 OL1

Data analysis, probability models, parameter estimation, hypothesis testing. Multi- factor experimental design and regression analysis. Quality control, SPC, reliability. Engineering cases and project. Statistical analysis software. Credit not given for more than one of STAT 141 or STAT 143. Prerequisites: MATH 020 or MATH 022; Sophomore standing.

#### Notes

Dates: May 23 - July 1, 2022; Prereqs: MATH 020 or MATH 022; Minimum sophomore standing; Credit not given for more than one of STAT 141 or 143.

#### Section Description

Data analysis, probability models, parameter estimation, hypothesis testing, and regression analysis. Pre-requisites: Math 20 or 22. Learning Outcomes Upon successful completion of the course, students will be able to: • Be able to classify data by the appropriate variable type(s). • Make and interpret the appropriate graph/table based on variable type for univariate and bivariate data. • Choose, calculate and interpret the appropriate numerical summaries/statistics for data by variable type(s) and distribution characteristics. • Identify outliers. • Correctly apply general probability rules, set theory formulas, Law of Total Probability, and Bayes Rule to solve probability problems. • Correctly apply counting rules to solve probability problems. • Solve discrete random variable probability problems. • Solve continuous random variable probability problems using calculus. • Be able to distinguish and apply probability models to solve problems (Bernoulli, Binomial, Poisson, and Normal). • Use the Binomial model as the basis for the sampling distribution of one proportion. • Use the Normal Model, Standard Normal Model, and t-distribution with the appropriate skills. • To identify that all assumptions/conditions are met for statistical inference techniques. • Construct and interpret a traditional method confidence interval using the appropriate model for one proportion, one mean, and regression slope. • Conduct a traditional method hypothesis test and state findings using p-value and level of significance for one proportion, one mean, two proportions, two independent means, mean difference of two dependent groups, Goodness of Fit, Test of Homogeneity/Independence, and regression slope. • Be able to transform non-linear data to apply linear regression techniques. • To interpret statistical software output.

#### Section Expectation

You will be expected to complete the 6 quizzes, 3 exams, and post your progress in a Journal. The usual for online courses (constant online presence) and keeping up with the work.

#### Evaluation

Grading: Your grade for the course will be based on: • Quizzes (15%) • Journal (10%) • Tests (25% each) All assignments must be completed to receive a passing grade for the course.

### Course Dates

#### May 23, 2022 to July 1, 2022

##### Location

Online (View Campus Map)

### Important Dates

Note: These dates may not be accurate for select courses during the Summer Session.

Last Day to Add May 25, 2022 May 27, 2022 May 31, 2022 June 2, 2022 June 17, 2022

### Resources

There are no courses that meet this criteria.