Applied Statistics Minor
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 RIT /
 Rochester Institute of Technology /
 Academics /
 Applied Statistics Minor
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Minor Advisor
Bernard Brooks, Professor
585‑475‑5138, smsminors@rit.edu
585‑475‑5138, smsminors@rit.edu
Offered within the
School of Mathematical Sciences
School of Mathematical Sciences
Overview
Deepen your technical background and gain further appreciation for modern mathematical sciences and the use of statistics as an analytical tool.
Notes about this minor:
 The minor is closed to students majoring in applied statistics and actuarial science.
 Posting of the minor on the student's academic transcript requires a minimum GPA of 2.0 in the minor.
The plan code for Applied Statistics Minor is STATSMN.
Curriculum for Applied Statistics Minor
Course  

Prerequisites  
Choose one of the following course sequences:  
MATH181 
Projectbased Calculus I
This is the first in a twocourse sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers functions, limits, continuity, the derivative, rules of differentiation, applications of the derivative, Riemann sums, definite integrals, and indefinite integrals. (Prerequisite: A or better in MATH111 or A or better in ((NMTH260 or NMTH272 or NMTH275) and NMTH220) or a math placement exam score greater than or equal to 70 or department permission to enroll in this class.) Lecture 6 (Fall, Spring, Summer).

MATH182 
Projectbased Calculus II
This is the second in a twocourse sequence intended for students majoring in mathematics, science, or engineering. It emphasizes the understanding of concepts, and using them to solve physical problems. The course covers techniques of integration including integration by parts, partial fractions, improper integrals, applications of integration, representing functions by infinite series, convergence and divergence of series, parametric curves, and polar coordinates. (Prerequisites: C or better in (MATH181 or MATH173 or 1016282) or (MATH171 and MATH180) or equivalent course(s).) Lecture 6 (Fall, Spring, Summer).

or  
MATH181A  Calculus I 
MATH182A  Calculus II 
or  
MATH171 
Calculus A
This is the first course in a threecourse sequence (COSMATH171, 172, 173). This course includes a study of functions, continuity, and differentiability. The study of functions includes the exponential, logarithmic, and trigonometric functions. Limits of functions are used to study continuity and differentiability. The study of the derivative includes the definition, basic rules, and implicit differentiation. Applications of the derivative include optimization and relatedrates problems. (Prerequisite: C or better in MATH111 or C or better in ((NMTH260 or NMTH272 or NMTH275) and NMTH220) or a math placement exam score greater than or equal to 50.) Lecture 5 (Fall, Spring).

MATH172 
Calculus B
This is the second course in threecourse sequence (COSMATH171, 172, 173). The course includes Riemann sums, the Fundamental Theorem of Calculus, techniques of integration, and applications of the definite integral. The techniques of integration include substitution and integration by parts. The applications of the definite integral include areas between curves, and the calculation of volume. (Prerequisites: C or better in MATH171 or 1016171T or 1016281 or 1016231 or equivalent course.) Lecture 5 (Fall, Spring).

MATH173 
Calculus C
This is the third course in threecourse sequence (COSMATH171, 172, 173). The course includes sequences, convergence and divergence of series, representations of functions by infinite series, curves defined by parametric equations, and polar coordinates. Also included are applications of calculus to curves expressed in parametric and polar form. (Prerequisites: C or better in MATH172 or equivalent course.) Lecture 5 (Fall, Spring).

Electives  
Choose five of the following:  
MATH251 
Probability and Statistics I
This course introduces sample spaces and events, axioms of probability, counting techniques, conditional probability and independence, distributions of discrete and continuous random variables, joint distributions (discrete and continuous), the central limit theorem, descriptive statistics, interval estimation, and applications of probability and statistics to realworld problems. A statistical package such as Minitab or R is used for data analysis and statistical applications. (Prerequisites: MATH173 or MATH182 or MATH 182A or equivalent course.) Lecture 3 (Fall, Spring, Summer).

MATH252 
Probability and Statistics II
This course covers basic statistical concepts, sampling theory, hypothesis testing, confidence intervals, point estimation, and simple linear regression. The statistical software package MINITAB will be used for data analysis and statistical applications. (Prerequisites: STAT251 or MATH251 or equivalent course.) Lecture 3 (Fall, Spring).

MATH505 
Stochastic Processes
This course explores Poisson processes and Markov chains with an emphasis on applications. Extensive use is made of conditional probability and conditional expectation. Further topics, such as renewal processes, Brownian motion, queuing models and reliability are discussed as time allows. (Prerequisites: MATH241 and MATH251 or equivalent courses.) Lecture 3 (Spring).

STAT205 
Applied Statistics
This course covers basic statistical concepts and techniques including descriptive statistics, probability, inference, and quality control. The statistical package Minitab will be used to reinforce these techniques. The focus of this course is on statistical applications and quality improvement in engineering. This course is intended for engineering programs and has a calculus prerequisite. Note: This course may not be taken for credit if credit is to be earned in STAT145 or STAT155 or MATH 252.. (Prerequisite: MATH173 or MATH182 or MATH182A or equivalent course.) Lecture 3 (Fall, Spring).

STAT305 
Regression Analysis
This course covers regression techniques with applications to the type of problems encountered in realworld situations. It includes use of the statistical software SAS. Topics include a review of simple linear regression, residual analysis, multiple regression, matrix approach to regression, model selection procedures, and various other models as time permits. (Prerequisites: MATH241 and MATH252 or equivalent courses.) Lecture 3 (Spring).

STAT325 
Design of Experiments
This course is a study of the design and analysis of experiments. It includes extensive use of statistical software. Topics include singlefactor analysis of variance, multiple comparisons and model validation, multifactor factorial designs, fixed, random and mixed models, expected mean square calculations, confounding, randomized block designs, and other designs and topics as time permits. (Prerequisites: STAT205 or MATH252 or equivalent courses.) Lecture 3 (Fall).

STAT335 
Introduction to Time Series
This course is a study of the modeling and forecasting of time series. Topics include ARMA and ARIMA models, autocorrelation function, partial autocorrelation function, detrending, residual analysis, graphical methods, and diagnostics. A statistical software package is used for data analysis. (Prerequisites: STAT205 or MATH252 or equivalent courses.) Lecture 3 (Spring).

STAT345 
Nonparametric Statistics
This course is an indepth study of inferential procedures that are valid under a wide range of shapes for the population distribution. Topics include tests based on the binomial distribution, contingency tables, statistical inferences based on ranks, runs tests and randomization methods. A statistical software package is used for data analysis. (Prerequisites: STAT205 or MATH252 or equivalent courses.) Lecture 3 (Fall).

STAT405 
Mathematical Statistics I
This course provides a brief review of basic probability concepts and distribution theory. It covers mathematical properties of distributions needed for statistical inference. (Prerequisites: STAT205 or MATH252 or equivalent courses.) Lecture 3 (Fall).

STAT406 
Mathematical Statistics II
This course is a continuation of STAT405 covering classical and Bayesian methods in estimation theory, chisquare test, NeymanPearson lemma, mathematical justification of standard test procedures, sufficient statistics, and further topics in statistical inference. (Prerequisites: STAT405 or equivalent course.) Lecture 3 (Spring).

STAT425 
Multivariate Analysis
This course is a study of the multivariate normal distribution, statistical inference on multivariate data, multivariate analysis of covariance, canonical correlation, principal component analysis, and cluster analysis. A statistical software package such as Excel or SAS is used for data analysis. (Prerequisites: STAT305 or equivalent courses.) Lecture 3 (Spring).

STAT521 
Statistical Quality Control
This course presents the probability models associated with control charts, control charts for continuous and discrete data, interpretation of control charts, and some standard sampling plans as applied to quality control. A statistical software package will be used for data analysis. (Prerequisites: MATH252 or equivalent course.) Lecture 3 (Fall, Spring).

STAT511 
Statistical Software
This course is an introduction to two statisticalsoftware packages, SAS and R, which are often used in professional practice. Some comparisons with other statisticalsoftware packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; builtin functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications. (Prerequisites: MATH252 or equivalent course.) Lecture 3 (Fall, Spring).

STAT547 
Data Mining
The use of statistical models in computer algorithms allows users to make decisions and predictions, and to perform tasks that traditionally require human cognitive abilities. Data mining and Machine learning are interdisciplinary fields at the intersection of statistics, computer science, applied mathematics which develops such statistical models and interweaves them with computer algorithms. It underpins many modern technologies, such as speech recognition, Internet search, bioinformatics and computer vision. The course will provide an introduction to Statistical Machine Learning and its core models and algorithms. (Prerequisites: STAT305 or equivalent courses.) Lecture 3 (Spring).

STAT572 
Survey Design and Analysis
This course is an introduction to sample survey design with emphasis on practical aspects of survey methodology. Topics include: survey planning, sample design and selection, survey instrument design, data collection methods, and analysis and reporting. Application areas discussed will include program evaluation, opinion polling, customer satisfaction, product and service design, and evaluating marketing effectiveness. Data collection methods to be discussed will include facetoface, mail, Internet and telephone. (Prerequisites: MATH252 or equivalent course.) Lecture 3 (Fall).

STAT584 
Categorical Data Analysis
This course is intended to introduce students to categorical data analysis. Topics include: contingency tables, matched pair analysis, Fisher's exact test, logistic regression, analysis of odds ratios, log linear models, multicategorical logit models, ordinal and paired response analysis. (Prerequisites: STAT305 or equivalent courses.) Lecture 3 (Spring).
