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# MAT 260 - Applied Probability and Statistics

Descriptive statistics, probability and random variables, discrete and continuous probability distributions, joint distributions, sampling distributions, confidence interval estimates, hypothesis tests on means, categorical populations, and the form of distributions, linear regression analysis on bivariate and multivariate data, single factor ANOVA, randomized block experiments, all with a strong emphasis on engineering applications and the use of statistical software to simulate, model, and analyze data.

Prerequisite- Corequisite
Prerequisite:  MAT 181 Calculus I

Credits: 4
Hours
4 Class Hours
Course Profile
Learning Outcomes of the Course:

Upon successful completion of this course the student will be able to:

1.  Use statistical software to construct data plots and displays, interpret these.
2.  Compute probabilities using the basic rules of probability.
3.  Compute probabilities, means and variances of discrete and continuous random variables, and interpret these.
4.  Compute probabilities, means and variances of sampling distributions, and interpret these.
5.  Compute probabilities, means and covariances of joint distributions, and interpret these.
6.  Perform computer simulations to investigate characteristics of probability distributions.
7.  Use statistical software to check whether data meet underlying assumptions of a probability model.
8.  Compute confidence interval estimates and interpret these.
9.  Perform computer simulations to illustrate confidence interval estimates.
10.  Perform hypothesis tests about means and interpret the results.
11.  Perform hypothesis tests about categorical populations and interpret the results.
12.  Perform hypothesis tests about the form of distributions and interpret the results.
13.  Use statistical software to perform Analysis of Variance (ANOVA) for the Single Factor and Randomized Block experiments, and interpret the results.
14.  Use statistical software to perform linear regression analysis for bivariate and multivariate data, and interpret the results.
15.  Use statistical software to perform residual analysis for linear regression models, and interpret the results.

In the context of the course objectives listed above, upon successful completion of this course the student will be able to:

1.  Interpret and draw inferences from mathematical models such as formulas, graphs, tables and schematics.
2.  Represent mathematical information symbolically, visually, numerically and verbally.
3.  Employ quantitative methods such as arithmetic, algebra, geometry, or statistics to solve problems.
4.  Estimate and check mathematical results for reasonableness.
5.  Recognize the limitations of mathematical and statistical methods.

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