This course introduces commonly used statistical methods. The intent of this course is to provide an understanding of statistical techniques and guidance on the appropriate use of methodologies. The course covers the mathematical foundations of common methods as an aid toward understanding both the types of applications that are appropriate and the limits of the methods. MATLAB and statistical software are used so students can apply statistical methodology to practical problems in the workplace. Topics include the basic laws of probability and descriptive statistics, conditional probability, random variables, expectation and variance, discrete and continuous probability models, bivariate distributions and covariance, sampling distributions, hypothesis testing, method of moments and maximum likelihood point (MLE) estimation, confidence intervals, contingency tables, analysis of variance (ANOVA), and linear regression modeling.
Course prerequisites: 
Multivariate calculus.
Course instructor: 
Bodt, Ermolaeva, Savkli, Wang

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Course all programs: 
Applied and Computational Mathematics
Data Science
Engineering Management
Financial Mathematics