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Nov 10, 2024
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MA 2410 - Statistics for AS4 lecture hours 0 lab hours 4 credits Course Description The course is designed to expose actuarial science majors to the statistical tools needed to make decisions based on the computed probability of occurrence. Both descriptive and inferential statistics will be considered. This course is designed to satisfy all the required topics of SOA (Society of Actuaries) VEE Mathematical Statistics curriculum. (prereq: MA 2323 , AS student, or consent from AS program director) Course Learning Outcomes Upon successful completion of this course, the student will be able to:
- Choose which probability distribution applies to a given statistical situation
- Perform a complete hypothesis test
- Correctly calculate and interpret a p-value
- Recognize the similarities between the various hypothesis tests and the formulas used by these tests
- Construct estimators using method of moments and maximum likelihood estimator
- Demonstrate understanding of sampling distributions
- Perform analysis of variance when appropriate and interpret the results
- Describe properties of estimators, including mean squared errors and UMVUE
- Apply Neyman-Pearson Lemma and likelihood ratio tests
- Construct confidence intervals for mean, the difference of two means, the difference of proportions and variance
- Apply analysis of variance and chi-square goodness-of-fit tests
Prerequisites by Topic
- Differential and integral calculus (both single and multivariable)
Course Topics
- Major probability distributions used in hypothesis testing including normal, student-t, chi-square, and F
- Sampling distribution and central limit theorem
- Constructing estimators using such as the method of moments and maximum likelihood estimator
- Properties of estimators, including mean squared errors and UMVUE
- Theory of hypothesis test, including Neyman-Pearson Lemma and likelihood ratio test
- One-sample and two-sample hypothesis testing
- Confidence intervals for mean, the difference of two means, the difference of proportions and variance
- Analysis of variance and chi-square goodness-of-fit test
Coordinator Dr. Yvonne Yaz
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