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Kansas State University

Statistics

John Boyer, Head

Professors Boyer, Higgins, Johnson, Loughin, Kemp, Milliken, Nelson, and Yang; Associate Professors Neill and Pontius; Assistant Professors Dubnicka, and Wang; Emeritus: Professors Perng and Feyerherm.

785-532-6883

Fax: 785-532-7736

E-mail: head@stat.ksu.edu

www.k-state.edu/stats/

Statistics is a combination of classical mathematics, the theory of probability, and new concepts related to inductive reasoning that have developed during the past 75 years.

Almost all activities of plants and animals (including people) depend to some degree on chance events, and most decisions made by people depend on sampling information—which also depends on chance events, and hence on probability. Consequently, fields of interest and activities for a statistician potentially are very broad.

Likewise, the professional activities open to a trained statistician are varied. The existence of modern-day computers relieves the statistician of tedious computations and elevates his or her professional activity to dealing with people and/or engaging in basic research.

Students who major in statistics may seek a bachelor of arts degree or a bachelor of science degree by satisfying the general requirements of that degree and by completing the following:

MATH 220Analytic Geometry and Calculus I4
MATH 221Analytic Geometry and Calculus II4
MATH 222Analytic Geometry and Calculus III4
CIS 200Fundamental of Computer Programming (or approved substitute)3-4
One course selected from MATH 551, CIS 209,  CIS 3003
ENGL 516Written Communication for the Sciences3
One of STAT 320, 330, 340, or 3503
STAT 341 or 3513
(Note: STAT courses at the 400 level or higher may replace either or both of the 300-level STAT courses.)
STAT 510Introductory Probability and Statistics I3
STAT 511Introductory Probability and Statistics II3
STAT 704Analysis of Variance and Covariance2
STAT 705Regression and Correlation Analyses2
One of STAT 710, 720, or 7222-3
One additional STAT course at 700 level2-3
 
Upper-division quantitative electives to give a total of 46 credit hours. Courses must be at the 400 level or above, and may include IMSE 541, math, computer science, statistics, or course in other area with substantial quantitative content.
 
A minimum of 2.0 GPA in STAT courses taken as part of the major is required for graduation.
 

Statistics minor

Students interested in quantitative methods to complement their major area of study may select a minor in statistics. The requirements are:

One of: STAT 320, 330, 340, 350, 510
One of: STAT 341, 351, 511
Both: STAT 704, 705
Five additional hours that require statistics as a prerequisite. Courses may be statistics courses or quantitative courses from another department containing substantial statistical content. These courses should be pre-approved by the Department of Statistics.
 

Dual majors and dual degrees

Students may major in statistics and another discipline within the College of Arts and Sciences. The degree requirements of both departments must be met. For instance, it is possible to complete a dual statistics- mathematics degree in four years.

Students may obtain a dual degree in statistics and a field in another college such as business administration or engineering. The degree requirements of both colleges must be met and a minimum of 150 hours must be completed. Students who choose this option should complete the calculus sequence by the end of the sophomore year.

Statistics courses

University General Education courseSTAT 100. Statistical Literacy in the Age of Information. (3) I, II. This course is intended for majors in non-quantitative fields. Focus will be on the development of an awareness of statistics at the conceptual and interpretative level, in the context of everyday life. Data awareness and quality, sampling, scientific investigation, decision making, and the study of relationships are included. Emphasis will be on the development of critical thinking through in-class experiments and activities, discussions, analyses of real data sets, written reports, and collaborative learning. Computing activities will be included where appropriate; no previous computing experience required. Pr.: MATH 100. Cannot be taken for credit if credit has been received for any other statistics course.

University General Education courseSTAT 320. Elements of Statistics. (3) I, II, S. A basic first course in probability and statistics; frequency distributions; averages and measures of variation; probability; simple confidence intervals and tests of significance appropriate to binomial and normal populations; correlation and regression, including confidence intervals and tests of significance for bivariate populations. Pr.: MATH 100.

University General Education courseSTAT 330. Elementary Statistics for the Social Sciences. (3) I, II, S. A basic first course in probability and statistics with textbook, examples, and problems aimed toward the social sciences and humanities. Frequency distributions, averages, measures of variation, probability, confidence intervals; tests of significance appropriate to binomial, multinomial, and normal sampling; simple regression and correlation. Pr.: MATH 100. Cannot be taken for credit if credit has been received for STAT 320, 340, or 350.

University General Education courseSTAT 340. Biometrics I. (3) I, II. A basic first course in probability and statistics with textbook, examples, and problems aimed toward the biological sciences. Frequency distributions, averages, measures of variation, probability, confidence intervals; tests of significance appropriate to binomial, multinomial, Poisson, and normal sampling; simple regression and correlation. Pr.: MATH 100. Cannot be taken for credit if credit has been received for STAT 320, 330, or 350.

STAT 341. Biometrics II. (3) II. Analysis and interpretation of biological data using analysis of variance, analysis of covariance, and multiple regression. Negative binomial distribution and its applications. Pr.: STAT 320, 330, 340, or 350.

University General Education courseSTAT 350. Business and Economic Statistics I. (3) I, II, S. A basic first course in probability and statistics with textbook, examples, and problems pointed toward business administration and economics. Frequency distributions, averages, index numbers, time series, measures of variation, probability, confidence intervals, tests of significance appropriate to binomial, multinomial, Poisson, and normal sampling; simple regression and correlation. Pr.: MATH 100. Cannot be taken for credit if credit has been received for STAT 320, 330, or 340.

STAT 351. Business and Economic Statistics II. (3) I, II, S. Continuation of STAT 350 including study of index numbers, time series, business cycles, seasonal variation, multiple regression and correlation, forecasting; some nonparametric methods applicable in business and economic studies. Pr.: STAT 320, 330, 340, or 350.

University General Education courseSTAT 399. Honors Seminar in Statistics. (3) Selected topics. May be used to satisfy quantitative requirements for BS degree. Open only to students in the honors program.

STAT 410. Probabilistic Systems Modeling. (3) II. Descriptive statistics and graphical methods; basic probability; probability distributions; several random variable; Poisson processes; computer simulation of random phenomena; confidence interval estimation; hypothesis testing. Pr.: MATH 221 and CIS 300.

STAT 490. Statistics for Engineers. (1) I, II. First course in statistics with examples and problems toward engineering. Distributions, means, measures of variation, confidence intervals, graphical display of data, simple regression and correlation, philosophy of experimentation. Must be taken conc. with a laboratory course in engineering which uses statistics.

STAT 491. Statistics for Engineers II. (1) I, II. A continuation of STAT 490. Offered second half of the semester following STAT 490. Statistical tests, multiple regression, model fitting, simple comparative and factorial experiments. Emphasis on computer analysis of data. Pr.: STAT 490.

STAT 510. Introductory Probability and Statistics I. (3) I, II. Descriptive statistics, probability concepts and laws, sample spaces; random variables; binomial, uniform, normal, and Poisson; two-dimensional variates; expected values; confidence intervals; binomial parameter, median, normal mean, and variance; testing simple hypotheses using CIs and X2 goodness of fit. Numerous applications. Pr.: MATH 221.

STAT 511. Introductory Probability and Statistics II. (3) II. Law of Large Numbers, Chebycheff's Inequality; continuation of study of continuous variates; uniform, exponential, gamma, and beta distribution; Central Limit Theorem; distributions from normal sampling; introduction to statistical inference. Pr.: STAT 510.

Undergraduate and graduate credit

STAT 702. Statistical Methods for Social Sciences. (3) I, II. Statistical methods applied to experimental and survey data from social sciences; test of hypotheses concerning treatment means; linear regression; product-moment, rank, and bi-serial correlations; contingency tables and chi-square tests. Pr.: MATH 100.

STAT 703. Statistical Methods for Natural Scientists. (3) I, II, S. Statistical concepts and methods basic to experimental research in the natural sciences; hypothetical populations; estimation of parameters; confidence intervals; parametric and nonparametric tests of hypotheses; linear regression; correlation; one-way analysis of variance; t-test; chi-square test. Pr.: Junior standing and equiv. of college algebra.

STAT 704. Analysis of Variance. (2) I, II, S. Computation and interpretation for two- and three-way analyses of variance; multiple comparisons; applications including use of computers. Meets four times a week during first half of semester. Pr.: One previous statistics course.

STAT 705. Regression and Correlation Analyses. (2) I, II, S. Multiple regression and correlation concepts and methods; curvilinear regression; applications including use of computers. Meets four times a week during second half of semester. Pr.: One previous statistics course.

STAT 706. Basic Elements of Statistical Theory. (3) I. The mathematical representation of frequency distributions, their properties, and the theory of estimation and hypothesis testing. Elementary mathematical functions illustrate theory. Pr.: MATH 205, 210, or 220 and STAT 320 or equiv.

STAT 710. Sample Survey Methods. (2) I, in even years. Design, conduct, and interpretation of sample surveys. Pr.: STAT 702 or 703. Meets four times a week during first half of semester.

STAT 713. Applied Linear Statistical Models. (4) I. Matrix-based regression and analysis of variance procedures at a mathematical level appropriate for a first-year graduate statistics major. Topics include simple linear regression, linear models in matrix form, multiple linear regression, model building and diagnostics, analysis of covariance, multiple comparison methods, contrasts, multifactor studies, blocking, subsampling, and split-plot designs. Pr.: Prior knowledge of matrix or linear algebra and one prior course in statistics. A student may not receive credit for both STAT 704/705 sequence and STAT 713.

STAT 716. Nonparametric Statistics. (2) II, in odd years. Hypothesis testing when form of population sampled is unknown: rank, sign, chi-square, and slippage tests; Kolmogorov and Smirnov type tests; confidence intervals and bands. Meets four times a week during second half of semester. Pr.: One previous course in statistics.

STAT 717. Categorical Data Analysis. (3) II. Analysis of categorical count and proportion data. Topics include tests of association in two-way tables; measures of association; Cochran-Mantel-Haenzel tests for 3-way tables; generalized linear models; logistic regression; loglinear models. Pr.: STAT 704, 705.

STAT 720. Design of Experiments. (3) II, S. Planning experiments so as to minimize error variance and avoid bias; Latin squares; split-plot designs; switch-back or reversal designs; incomplete block designs; efficiency. Pr.: STAT 704 and 705.

STAT 722. Experimental Designs for Product Development and Quality Improvement. (3) II. A study of statistically designed experiments which have proven to be useful in product development and quality improvement. Topics include randomization, blocking, factorial treatment structures, factional factorial designs, screening designs, and response surface methods. Pr.: STAT 511 or STAT 704 and STAT 705.

STAT 725. Digital Statistical Analysis. (1) I. Topics may include basic environment and syntax, reading and importing data from files, writing and exporting data to files, data manipulation, basic graphics, and built-in and user-defined functions. Pr.: One graduate-level course in statistics.

STAT 726. Introduction to Splus/R Computing. (1). II. Topics may include basic environment and syntax, reading and importing data from files, data manipulation basic graphics, and built-in and user-defined functions. Pr.: One graduate-level course in statistics.

STAT 730. Multivariate Statistical Methods. (3) I. Multivariate analysis of variance and covariance; classification and discrimination; principal components and introductory factor analysis; canonical correlation; digital computing procedures applied to data from natural and social sciences. Pr.: STAT 704, 705.

STAT 736. Bioassay. (2) II, in odd years. Direct assays; quantitative dose-response models; parallel line assays; slope ratio assays; experimental designs for bioassay; covariance adjustment; weighted estimates; assays based on quantal responses. Meets four times a week during second half of semester. Pr.: STAT 704, 705.

STAT 740. Nonlinear Models. (3) S, in even years. Methods of estimating parameters of nonlinear models; procedures for testing hypotheses; construction of confidence intervals and regions; nonlinear analysis of covariance; quantal dose response and probabilistic choice models. Pr.: MATH 222, STAT 720.

STAT 745. Graphical Methods, Smoothing, and Regression Analysis. (3) II, in even years. Visual display of quantitative information. Graphical techniques to portray distributions of data, multivariate information, means comparisons, and assessment of distributional assumptions. Data smoothing techniques including loess, parametric, robust, and nonparametric regression, and generalized additive models. Graphical evaluation of smoothing techniques including assessment of assumption. Regression diagnostics.

STAT 770. Theory of Statistics I. (3) I. Probability models, concepts of probability, random discrete variables, moments and moment generating functions, bivariate distributions, continuous random variables, sampling, Central Limit Theorem, characteristic functions. More emphasis on rigor and proofs than in STAT 510 and 511. Pr.: MATH 222.

STAT 771. Theory of Statistics II. (3) II. Introduction to multivariate distributions; sampling distributions, derivation, and use; estimation of parameters, testing hypothesis; multiple regression and correlation; simple experimental designs; introduction to nonparametric statistics; discrimination. Pr.: STAT 770.

STAT 799. Topics in Statistics. (Var.) I, II, S. Pr.: STAT 703 or 770 and consent of instructor.