515—Statistical Methods I. (3) (Prereq: a grade of C or higher in MATH 122 or MATH 141; or both MATH 111 or higher and any statistics class) Applications and principles of elementary probability, essential discrete and continuous probability distributions, sampling distributions, estimation, and hypothesis testing. Inferences for means, variances, proportions, one-way ANOVA, simple linear regression, and contingency tables. Statistical packages such as SAS or R.
Sample Course Homepage:
Usually Offered: Fall, Spring, and Summer I Semesters
Learning Objectives: By the end of the term successful students should be able to do the following:
- Demonstrate an understanding of and use basic statistical terminology.
- Recognize and be able to apply standard discrete and continuous probability distributions, including the binomial, hypergeometric, Poisson, normal and exponential distributions.
- Demonstrate an understanding of the variation in data and its relationship to the normal-theory based sampling distributions for the mean, variance, and ratio of two variances, including the concept of robustness.
- Conduct inference using parameter estimation and hypothesis testing for one and two samples, simple linear regression, one-way analysis of variance, and contingency tables, including use of appropriate technology.
- Interpret and explain the results of inferential procedures in the appropriate context.
Current Textbook: Statistics, 13/E, by J.T. McClave and T. Sincich, Prentice Hall, 2017.
|Introduction to R and/or SAS||
|Basic Probability: probability rules, simple and compound events, additive and multiplicative rules, conditional probability and independence, Bayes' rule, introduction to counting rules||
|Discrete Distributions: graphs and tables of probability distributions, expected value and variance, binomial random variables, Poisson random variables||
|Continuous Distributions: normal distribution, quantile-quantile plots, exponential distribution||
|Sampling Distributions: central limit theorem; t, chi-squared, and F distributions, and sketch of the underlying mathematics||
|Estimation and Inference: confidence intervals and hypothesis tests for one and two means, variances, and proportions; p-value; power; introduction to nonparametrics and randomization tests||
|One-way ANOVA: the analysis of variance table and partitioning the sum of squares||
|Simple Linear Regression: the least squares regression line, inference for the slope, checking assumptions, the correlation coefficient||
|Categorical Data Analysis: tests for independence, homogeneity, and goodness of fit; derivation of expected values and degrees of freedom||
The above textbook and course outline should correspond to the most recent offering of the course by the Statistics Department. Please check the current course homepage or with the instructor for the course regulations, expectations, and operating procedures.
Contact Faculty: Brian Habing