Statistics 103 covers the basic laws of probability and statistical inference. Topics include: random events, independence and dependence, Bayes theorem, discrete and continuous random variables, density, and distribution functions. Expectations and variances of linear combinations of random variables. Introductions to maximum likelihood estimation and Bayesian inference. Hypothesis testing and confidence intervals, and simple linear regression. Applications in economics and quantitative social sciences, and natural sciences emphasized.
Assignments
The assignments will include several in-class quizzes, two midterm exams and a final exam, labs, and a final project. In the computing labs, students analyze datasets using statistical software JMP.
Additional Information. This course assumes students have only an introductory calculus background. Calculus will not be used heavily, but mathematical maturity at the calculus level will be assumed. The course will use summation notation and mathematical symbols. Use of calculus will be limited to simple derivates and integrals. No multivariate calculus will be used.
This course covers many topics needed for further study in statistical methods, including random variables and calculation of arbitrary expectations and variances. Therefore, students preparing for econometrics and finance courses in Economics should take Statistics 103. Students interested in a mathematically rigorous introduction to statistics should take Statistics 114. Students who want a conceptual introduction to statistics, or who are not comfortable with summation notation or moderate mathematics, should take Statistics 101. Students interested in an algebra-based introductory statistics course that focuses on statistics in the legal arena should take STA 102B.
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