Statistics: Statistics for Engineering

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 awarded for both STAT 1410 and STAT 2430. Prerequisites: MATH 1212 or MATH 1234.

Prereqs: MATH 1212 or MATH 1234; Minimum sophomore standing; Credit not given for more than one of STAT 1410 or 2430; Asynchronous online

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.

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.

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.