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Your search for courses for 21/WI and with code: STATCORE found 4 courses.

### MATH 240.00 Probability 6 credits

Closed: Size: 30, Registered: 21, Waitlist: 0

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11:30am12:40pm | 11:30am12:40pm | 11:10am12:10pm |

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(Formerly Mathematics 265) Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

*Prerequisite:* Mathematics 120 or Mathematics 211

Formerly Mathematics 265

### MATH 240.02 Probability 6 credits

Open: Size: 30, Registered: 12, Waitlist: 0

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11:30am12:40pm | 11:30am12:40pm | 11:10am12:10pm |

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(Formerly Mathematics 265) Introduction to probability and its applications. Topics include discrete probability, random variables, independence, joint and conditional distributions, expectation, limit laws and properties of common probability distributions.

*Prerequisite:* Mathematics 120 or Mathematics 211

Formerly Mathematics 265

### STAT 230.00 Applied Regression Analysis 6 credits

Open: Size: 25, Registered: 20, Waitlist: 0

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8:30am9:40am | 8:30am9:40am | 8:30am9:30am |

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(Formerly Mathematics 245) A second course in statistics covering simple linear regression, multiple regression and ANOVA, and logistic regression. Exploratory graphical methods, model building and model checking techniques will be emphasized with extensive use of statistical software to analyze real-life data.

*Prerequisite:* Statistics 120 (formerly Mathematics 215) or Statistics 250 (formerly Mathematics 275), Psychology 200, or AP Statistics Exam score of 4 or 5.

Formerly Mathematics 245

### STAT 250.00 Introduction to Statistical Inference 6 credits

Closed: Size: 28, Registered: 28, Waitlist: 0

M | T | W | TH | F |
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2:30pm3:40pm | 2:30pm3:40pm | 3:10pm4:10pm |

#### Requirements Met:

(Formerly Mathematics 275) Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.

*Prerequisite:* Mathematics 240 Probability (formerly Mathematics 265)

Formerly Mathematics 275

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