About STAT 2430 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 awarded for both STAT 1410 and STAT 2430. Prerequisites: MATH 1212 or MATH 1234.
Notes
Prereqs: MATH 1212 or MATH 1234; Minimum sophomore standing; Credit not given for more than one of STAT 1410 or 2430; Asynchronous online
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.
Important Dates
Note: These dates may change before registration begins.
Courses may be cancelled due to low enrollment. Show your interest by enrolling.
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Resources
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