Statistics

Undergraduate Programs

Majors

Program Locations Total Credits
Statistics BS BS - Bachelor of Science
  • Mankato
120
Statistics BS Actuarial Track BS - Bachelor of Science
  • Mankato
120

Minors

Program Locations Total Credits
Statistics Minor
  • Mankato
21

Policies & Faculty

Policies

Admission to Major. Admission is granted by the Department. Admission requirements are:

  • A minimum of 32 earned semester credit hours and a 2.0 minimum cumulative GPA
  • Completion of 10 credits of mathematics and statistics counting towards the Major with a 2.5 GPA or higher.

Contact the College of Science, Engineering and Technology Student Relations Office for application procedures.

GPA Policy. Statistics majors and minors must earn a grade of “C” (2.0) or better in all courses applied to the major or minor.

Course Application Policy. Within each major or minor, no course may be applied to more than one requirement.

P/N Grading Policy. All 300- and 400-level courses are offered for grade only with the exception of STAT 498 and STAT 499 which are available for both P/N and letter grade.

Credit by Examination. Credit by examination will not be approved for courses in which a student has already received a grade.

Policy

 Students seeking enrollment in MATH 112 College Algebra, MATH 201 Elements of Mathematics, or STAT 154 Elementary Statistics must demonstrate readiness to succeed in the course through one of the following means: 

 
Course ACT Math Sub Score SAT Math Sub Score MCA Math Score Next Gen Accuplacer AAF Test Score Course Prerequisites
 MATH 112

22 or higher

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20-21 and HS GPA 2.7 or higher

530 or higher

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520-529 and HS GPA 2.7 or higher

1158 or higher

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1152-1157 and HS GPA 2.7 or higher

250 or higher

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236-249 and HS GPA 2.7 or higher

Grade of P in MATH 098
MATH 201 22 or higher 550 or higher 1148 or higher 250 or higher Grade of P in MATH 098 or "C" (2.0) or better in MATH 112 or MATH 115
STAT 154 19 or higher 500 or higher

1148 or higher

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1146-1147 and HS GPA 2.7 or higher

250 or higher Grade of P in MATH 098 or "C" (2.0) or better in MATH 112, MATH 115, or MATH 121

 Students not meeting one of the test requirements will be placed in MATH 098: Intermediate Algebra.

Students seeking enrollment in courses beyond those listed above must demonstrate readiness to succeed in the course through one of the following means: ACT score, SAT score, Next Gen ACCUPLACER score(s), or satisfactory completion (i.e. grade of C or better) of pre-requisite coursework, according to the chart below. 

Course Minimum ACT Math Sub Score Minimum SAT Math Score Minimum Next Gen Accuplacer Score Course Prerequisites 
MATH 113 22 550 260 MATH 112 with "C" or better
MATH 115 23 560 260 Grade of P in MATH 098
MATH 121 24 580 275 and "classic" Accuplacer Calculus Readiness score of 21 or higher MATH 115 or both MATH 112 and MATH 113 with "C" or better
MATH 130 23 560 260 MATH 112 or MATH 115 with a "C" or better
MATH 181 23 560 260 MATH 112 or MATH 115 with a "C" or better

 NOTE 1: Documented Next Gen ACCUPLACER scores from any Minnesota State College and Universities (MinnState) institution taken within two calendar years will be accepted.

NOTE 2:  ACT and SAT scores and “classic” or Next Gen ACCUPLACER scores that are more than two years old will not be accepted for mathematics placement. 

Contact Information

273 Wissink Hall 

Office (507) 389-1453
https://cset.mnsu.edu/mathstat/

Faculty

100 Level

Credits: 4

An introduction to statistical concepts and methods that is applicable to all disciplines. Topics include descriptive measures of data, probability and probability distributions, statistical inference, tests of hypotheses, confidence intervals, correlation, linear regression, and analysis of variance. The use of statistical software will be emphasized. Prereq: ACT Math sub-score of 19 or higher, successful completion of MATH 098 or appropriate placement scores (see Placement Information under Statistics) Fall, Spring, Summer GE-4

Prerequisites: Satisfy Placement Table in this section, or MATH 098 with grade of P. 

Goal Areas: GE-02, GE-04

200 Level

Credits: 3

An introduction to statistics with emphasis on the applied probability models used in Science and Engineering. Topics covered include samples, probability, probability distributions, estimation, one and two samples hypotheses tests, correlation, simple and multiple linear regressions.

Prerequisites: MATH 112 with grade of “C” (2.0) or better 

300 Level

Credits: 4

A calculus based introduction to probability and statistics. Topics include probability, random variables, probability distributions (discrete and continuous), joint probability distributions (discrete and continuous), statistical inference (both estimation and hypothesis testing), confidence intervals for distribution of parameters and their functions, sample size determinations, analysis of variance, regression, and correlation. This course meets the needs of the practitioner and the person who plans further study in statistics. Same as MATH 354. Prereq: MATH 122 with C or better or consent Fall, Spring, Summer

Prerequisites: MATH 122 with C or better or consent 

Credits: 3

Introduction to basic programming techniques: creating DATA and PROC statements, libraries, functions, programming syntax, and formats. Descriptive and Inferential statistics in SAS. Emphasis is placed on using these tools for statistical analyses. Working with arrays, loop and SAS macro.

Prerequisites: STAT 154 or instructor’s approval

Credits: 0

Curricular Practical Training: Co-Operative Experience is a zero-credit full-time practical training experience for one semester and an adjacent fall or spring term. Special rules apply to preserve full-time student status. Please contact an advisor in your program for complete information.

Prerequisites: At least 60 credits earned; in good standing; instructor permission; co-op contract; other prerequisites may also apply.

400 Level

Credits: 3

Simple and multiple linear regression, model adequacy checking and validation, identification of outliers, leverage and influence, polynomial regression, variable selection and model building strategies, nonlinear regression, and generalized linear regression.

Prerequisites: MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent 

Credits: 3

Randomized complete block design, Latin squares design, Graco- Latin squares design, balanced incomplete block design, factorial design, fractional factorial design, response surface method, fixed effects and random effects models, nested and split plot design.

Prerequisites: MATH 354 / STAT 354 or STAT 455 with “C” (2.0) or better or consent 

Credits: 4

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications. Includes probability, continuous probability distributions, multivariate distributions, functions of random variables, central limit theorem and statistical inference. Same as MATH 455. Prereq: MATH 223 with C or better or consent

Prerequisites: MATH 223 with “C” (2.0) or better or consent

Credits: 4

A mathematical approach to statistics with derivation of theoretical results and of basic techniques used in applications, including sufficient statistics, additional statistical inference, theory of statistical tests, inferences about normal models and nonparametric methods. Same as MATH 456. Prereq: MATH/STAT 455 with C or better or consent

Prerequisites: MATH 455, STAT 455 with “C” (2.0) or better or consent

Credits: 3

Sampling distributions: means and variances. Bias, robustness and efficiency. Random sampling, systematic sampling methods including stratified random sampling, cluster sampling and two-stage sampling, ratio, regression, and population size estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites: Either MATH/STAT 354 or both MATH 121 adn STAT 154 with "C" (2.0) or better, or consent.

Credits: 3

Forms of multivariate analysis for discrete data, two dimensional tables, models of independence, log linear models, estimation of expected values, model selection, higher dimensional tables, logistic models and incompleteness. Logistic regression. Suitable statistical software is introduced, for example, MATLAB, R, SAS etc.

Prerequisites: Either MATH/STAT 354 or both MATH 121 and STAT 154 with “C” (2.0) or better, or consent.

Credits: 3

Derivation and usage of nonparametric statistical methods in univariate, bivariate, and multivariate data. Applications in count, score, and rank data, analysis of variance for ranked data. Nonparametric regression estimation. Suitable statistical software is introduced, for example, MATLAB, R, SAS, etc.

Prerequisites: Either MATH/STAT 354 or both STAT 154 and MATH 121 with “C” (2.0) or better, or consent.

Credits: 1-3

The study of a particular topic primarily based upon recent literature. May be repeated for credit on each new topic.

Prerequisites: none

Credits: 1-4

A course designed to upgrade the qualifications of persons on-the-job. May be repeated for credit on each new topic.

Prerequisites: none

Credits: 3

This course is designed to allow undergraduate students an opportunity to integrate their statistics experiences by engaging each student in working on problems in applied or theoretical statistics. Spring

Prerequisites: STAT 457, STAT 458, STAT 459, STAT 450 (at least two of these)

Credits: 1-4

A course in an area of statistics not regularly offered. May be repeated for credit on each new topic.

Prerequisites: none

Credits: 1-12

Provides a student the opportunity to gain expertise and experience in a special field under the supervision of a qualified person.

Prerequisites: none

Credits: 1-4

Independent individual study under the guidance and direction of a faculty member. Special arrangements must be made with an appropriate faculty member. May be repeated for credit of each new topic.

Prerequisites: none