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Department of Statistics

STAT 700

700—Applied Statistics I (3) Introduction to probability and the concepts of estimation and hypothesis testing for use in experimental, social, and professional sciences. One and two-sample analyses, nonparametric tests, contingency tables, sample surveys, simple linear regression, various statistical packages. Not to be used for M.S. or Ph.D. credit in statistics or mathematics.


Usually Offered: Fall Semesters

Purpose: To provide future scientists in these fields with a base on which to build a continually expanding array of methods for experimental design and data analysis. Students will ideally come away with: (1) an understanding of basic probability and the manner in which all formal statistical inference depends on it; (2) the ability to carry out the basic analyses listed in the description using widely available software; (3) a knowledge of the universal principles underlying all hypothesis testing and interval estimation, thereby facilitating interactions with professional statisticians.

Current Textbook: An Introduction to Statistical Methods and Data Analysis (6th edition), by R. Lyman Ott and Michael Longnecker. Brooks/Cole, 2010.


Topics Covered Chapter Time
Data collection and description: introduction to data collection, graphical and numerical summaries for one and two variables, introduction to SAS and R 1-3 2 weeks
Probability and probability distributions: basic rules of probability, discrete and continuous random variables (binomial, hypergeometric, Poisson, exponential, normal) 4 2.5 weeks
Sampling Distributions: central limit theorem, normal approximation to the binomial, quantile-quantile plots, sampling distributions for normal populations (t, chi-square, and F distributions) 4 cont. 1.5 weeks
Inferences for one and two populations: parametric confidence intervals and tests for means and variances for one and two populations (t, chi-square, F, and modified Levene for two samples) 5-7 3 weeks
Nonparametric methods: sign, signed-rank, and rank sum tests, estimators, and intervals, and the bootstrap. 5-7 cont. 1.5 weeks
Categorical data: inferences about proportions and chi-squared tests (exact and large sample tests; Agresti-Coull correction; tests for goodness of fit, homogeneity, and independence; McNemar's test and the Mantel-Haenszel test) 10 1.25 week
Simple linear regression and correlation: the least squares regression line, t-test and interval for slope, assumption checking, prediction interval, correlation coefficient 11 1.25 weeks

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

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