Bachelor of Science in Statistics
Degree Program Hours: 120
Lower Division Preparation
To qualify for admission to the program, FIU undergraduates must have met all the lower division requirements including CLAST, completed 60 semester hours, and must be otherwise acceptable into the program.
One of the following:
Courses required for the degree:
Upper Division Program
Required Courses: (33)
Six additional credit hours of approved statistics courses 6. Three additional credit hours in an approved statistics, mathematics, or computer science course 3. A grade of ‘C’ or higher in each of these courses is necessary for the major.
The balance of the 120 semester hour requirement for graduation may be chosen from any courses in the University approved by the student’s advisor.
Remarks: The student must consult his or her advisor to determine which courses, in addition to the required courses listed above, satisfy the requirements for a statistics major. The following courses are not acceptable for credit toward graduation, unless a student has passed the course before declaring a statistics major: MAC 2233, STA 1013, STA 2023, STA 3033, STA 3111, STA 3112, STA 2122, STA 3123, STA 3145 and QMB 3200 (College of Business Administration).
Combined Bachelor’s/Master’s Degree in Statistics
Courses and other General Requirements
Students enrolled in the program may count up to 9 hours as credits for both the undergraduate and graduate degree programs. These courses must be taken at least the 5000 level and can be chosen from the following list (amongst others):
Students who count cross listed courses towards the degree will not get credit for both the 4000 level and the 5000 level course. In fact, the students will not be allowed to take both the courses.
In addition, as part of earning the MS degree the students are required to take the following core courses:
The BS/MS program is designed to be a continuous program; however, upon completion of all the requirements of the undergraduate degree, students will receive the BS degree. Students in this program have up to one year after receipt of the bachelor’s degree to complete the MS degree. Students who fail to meet the post BS requirement or who elect to leave the combined program at any time and earn only the BS degree will have the same access requirements to regular graduate programs as any other student but will not be able to use the 9 credits for both the bachelor’s and master’s degree.
Admission into the combined program does not automatically qualify the students for admission into the MS degree program. To enroll in the MS degree program, the students must apply (in their senior year) to the graduate school and meet all graduate admission requirements.
Students enrolled in the program must maintain an overall GPA of 3.0 or higher and must get a minimum grade of “B” in all the core courses. Upon completion of the entire 4+1 program, students must have accumulated a minimum of 30 hours of credits at the graduate (5000+) level. In addition, to get the MS degree, the students will also be required to take a comprehensive examination or do a thesis. Students opting for the comprehensive exam will be required to take an additional 6 hours of credits at the graduate (5000+) level. All students enrolled in the program will be expected to attend the departmental seminars.
Minor in Statistics
Lower or Upper Division Preparation: (3 or 4)
Upper Division Program: (12)
Two additional courses from the following list:
1STA 4321 has MAC 2313 as a prerequisite.
A grade of ‘C’ or higher in each of these courses is necessary for the minor.
Remarks: No courses in statistics, mathematics or computer sciences can be applied to more than one minor in these disciplines, nor can courses used to satisfy major requirements be used towards minor requirements. In the case where a course is required for both a major in one area and a minor in another, the student should see his or her advisor for an appropriate substitution for the requirement of the minor.
Certificate Program in Actuarial Studies
See section on certificate programs under College of Arts and Sciences.
Definition of Prefixes
MAP - Mathematics, Applied; STA - Statistics.
MAP 5117 Mathematical and Statistical Modeling (3). Study of ecological, probabilistic, and various statistical models. Prerequisites: COP 2210, MAC 2313, MAS 3105; and STA 3033 or STA 3164 or STA 4322.
STA 1013 Statistics for Social Services (3). This is an elementary course in statistics, covering graphical and numerical condensation of data as well as the most basic parametric and non-parametric methods. Emphasis is placed on the interpretation of statistical results, rather than on ways to analyze experimental data. Prerequisite: High school algebra.
STA 1061 Introduction to SPSSX for Data Analysis (1). Data coding and entry for use on the mainframe. How to input data, create variables, select subsets of data. Use procedures such as: LIST, FREQUENCIES, CROSSTABS, DESCRIPTIVES, MEANS and CORRELATIONS. Prerequisite: A course in statistics.
STA 1062 Introduction to SAS for Data Analysis (1). Data coding for entry use on the mainframe. SAS Data step to input data, create variables, select subsets of data, PROCs such as: PRINT, FORMAT, MEANS, FREQ, SUMMARY, TEST, CORR, UNI-VARIATE and PLOT. Prerequisite: A course in statistics.
STA 2023 Statistics for Business and Economics (3). Starting with an introduction to probability, the course provides an introduction to statistical techniques used in management science. It includes descriptive statistics, probability distributions, estimation and testing of
hypotheses. Subsequent credit for STA 2122 or STA 3111 will not be granted. Prerequisite: High school algebra. (F,S,SS)
STA 2122 Introduction to Statistics I (3). A course in descriptive and inferential statistics. Topics include: probability distribution of discrete and continuous random variables. Sampling distributions. Large sample estimation and hypothesis testing for means and proportions. Prerequisite: High school algebra. (F,S,SS)
STA 3033 Introduction to Probability and Statistics for CS (3). Basic probability laws, probability distributions, basic sampling theory, point and interval estimation, tests of hypotheses, regression and correlation. Prerequisite: MAC 2312. (F,S,SS)
STA 3060L Statistics Laboratory (1). A laboratory course designed to illustrate important statistical concepts through experiments. Data are analyzed using statistical software packages. Prerequisite or Corequisite: A statistics course.
STA 3111 Statistics I (3). Descriptive statistics. Basic probability rules. Discrete and continuous probability distributions. Point and interval estimation, hypothesis testing based on a single sample. Comparison of two proportions using independent and large samples. Subsequent credit for STA 2122 or STA 2023 will not be granted. Prerequisite: High school algebra. (F,S,SS)
STA 3112 Statistics II (3). Estimation and hypothesis testing based on two samples. Analysis of Variance. Simple linear regression. Linear correlation. Analysis of categorical data. Non-parametric methods. Use of statistical software packages. Subsequent credit for STA 3123 will not be granted. Prerequisite: STA 3111. (F,S,SS)
STA 3123 Introduction to Statistics II (4). Small sample statistical inference for means and variances. T, chi-square and F distributions. Analysis of variance, regression, correlation, basic nonparametric tests, goodness of fit tests and tests of independence. Prerequisites: STA 2122 or equivalent. (F,S,SS)
STA 3145 Statistics for the Health Professions (3). Statistical analysis with applications in the health sciences. Binomial and normal distributions. Inferences about means and proportions. Regression, correlation, goodness of fit tests. Prerequisite: High school algebra. (F,S,SS)
STA 3163-STA 3164 Statistical Methods I and II (3-3). This course presents tools for the analysis of data. Specific topics include: use of normal distribution, tests of means, variances and proportions; the analysis of variance and covariance (including contrasts and components of variance models), regression, correlation, sequential analysis, and non-parametric analysis. Prerequisites: A course in statistics, or MAC 2312, or high school equivalent. (F,S)
STA 3905 Independent Study (1-6). Individual conferences, assigned readings, and reports on independent investigations.
STA 3930 Special Topics (1-6). A course designed to give groups of students an opportunity to pursue special studies not otherwise offered.
STA 3949 Cooperative Education in Statistics (1-3). One semester of either part-time or full-time work in an outside organization. Limited to students admitted to the Co-op program. A written report and supervisor evaluation are required of each student. Prerequisites: 2 courses in statistics and permission of Chairperson.
STA 3951 Oral Presentations in Statistics (0). Students are required to communicate orally all stages of a simple statistical analysis through a formal presentation in front of a group of faculty and students. Prerequisites: ENC 3211 and STA 3164 or equivalent. (F,S,SS)
STA 4102 Introduction to Statistical Computing (3). Data manipulation and statistical procedures using popular software, simulation, and statistical algorithms. Prerequisites: STA 3112 or STA 3123 or STA 3164, and COP 2210.
STA 4173-HSC 4510 Statistical Applications in Health Care (3). A course in descriptive and inferential statistics for the Health Services. Topics include probability distributions, point and interval estimation, hypothesis testing, regression and correlation, and contingency table analysis. Prerequisites: STA 1013 or equivalent college mathematics course.
STA 4182 Statistical Models (3). This is a specialized course in the use of statistical models to represent physical and social phenomena. The emphasis is on providing tools which will allow a researcher or analyst to gain some insight into phenomena being studied. An introductory knowledge of probability theory and random variables is assumed. Specific topics include: introduction to discrete and continuous probability distributions, transformation of variables, approximation of data by empirical distributions, central limit theorem, propagation of moments, Monte Carlo simulation, probability plotting,testing distributional assumptions. Prerequisites: STA 3033 or STA 4321.
STA 4202 Introduction to Design of Experiments (3). Completely randomized, randomized block, Latin square, factorial, nested and related designs. Multiple comparisons. Credit will not be given for both STA 4202 and STA 5206. Prerequisites: STA 3163 or STA 3112 or STA 3123 or STA 4322.
STA 4321-STA 4322 Introduction to Mathematical Statistics I and II (3-3). This course presents an introduction to the mathematics underlying the concepts of statistical analysis. It is based on a solid grounding in probability theory, and requires a knowledge of single and multivariable calculus. Specific topics include the following: basic probability concepts, random variables, probability densities, expectations, moment generating functions, sampling distributions, decision theory, estimation, hypothesis testing (parametric and non-parametric), regression, analysis of variance, and design of experiments. Prerequisite: MAC 2313. (F,S)
STA 4234 Introduction to Regression Analysis (3). Multiple and polynomial regression, residual analysis, model identification and other related topics. Credit will not be given for both STA 4234 and STA 5236. Prerequisites: STA 3112 or STA 3123 or STA 3164.
STA 4502 Introduction to Non-parametric Methods (3). Sign, Mann-Whitney U, Wilcoxon signed rank, Kruskal- Wallis, Friedman and other distribution-free tests. Rank correlation, contingency tables and other related topics. Credit for both STA 4502 and STA 5507 will not be granted. Prerequisite: A course in statistics.
STA 4664 Statistical Quality Control (3). This course presents the simple but powerful statistical techniques employed by industry to improve product quality and to reduce the cost of scrap. The course includes the use and construction of control charts (means, percentages, number defectives, ranges) and acceptance sampling plans (single and double). Standard sampling techniques such as MIL STD plans will be reviewed. Prerequisite: A course in statistics.
STA 4905 Independent Study (1-6). Individual conferences, assigned readings, and reports on independent investigations.
STA 4930 Special Topics (1-6). Designed to give students an opportunity to pursue special studies not otherwise offered. May be repeated.
STA 4949 Cooperative Education in Statistics (1-3). One semester of either part-time or full-time work, in an outside organization. Limited to students admitted to the Co-op program. A written report and supervisor evaluation are required of each student. Prerequisites: STA 3164, STA 4322 and permission of Chairperson.
STA 5065L SAS Data Analysis Lab (1). Entering data, descriptive statistics, graphing data, crosstabulations, t-tests, correlation and regression, and analysis of variance. Prerequisites: A statistics course and graduate standing or permission of the instructor.
STA 5105L SPSS Data Analysis Lab (1). Topics include: Entering data from various sources, data checking, descriptive statistics, graphing data, cross tabulations, tests, correlation and regression, ANOVA, and reliability. Prerequisites: A statistics course or concurrent enrollment in a statistics course, and graduate standing or permission of the instructor. (F,S,SS)
STA 5106 Intermediate Statistics I (3). Power, measures of assoc., measurement, ANOVA: one-way and factorial, between and within subjects expected mean squares, planned comparisons, a-priori contrasts, fixed, random, mixed models. This course may be of particular interest to behavioral sciences. Prerequisites: STA 3111 or STA 3123 or STA 3033; and graduate standing. (F)
STA 5107 Intermediate Statistics II (3). Correlation and regression both simple and multiple, general linear model, analysis of covariance, analysis of nominal data, analysis
of categorical data. This course may be of particular interest to behavioral sciences. Prerequisite: Permission of the instructor. (S)
STA 5126-PSY 5206 Fundamentals of Design of Experiments (3). CRD and RCB designs. Latin square designs. Factorial, nested and nested-factorial experiments. Fixed, random and mixed models. Split-plot designs. Covariance analysis. Prerequisites: STA 3112 or STA 3123 or STA 3163 or STA 4322 or equivalent.
STA 5206 Design of Experiments I (3). Design and analysis of completely randomized block, Latin square factorial, nested experiments. Multiple comparisons. Credit for only one of three STA 4202, STA 5126, and STA 5206 courses will be granted. Prerequisites: STA 3033 or STA 3164 or STA 4322 or (STA 3163 and STA 4321).
STA 5207 Topics in Design of Experiments (3). This applied course in design of experiments covers topics such as split-plot design, confounding, fractional replication, incomplete block designs, and response surface designs. Prerequisite: STA 5206.
STA 5236 Regression Analysis (3). Simple, multiple and polynomial regression, analysis of residuals, model building and other related topics. Credit for both STA 4234 and STA 5236 will not be granted. Prerequisites: STA 3112 or STA 3123 or STA 3164, or STA 6167.
STA 5446-STA 5447 Probability Theory I and II (3-3). This course is designed to acquaint the student with the basic fundamentals of probability theory. It reviews the basic foundations of probability theory, covering such topics as discrete probability spaces, random walk, Markov Chains (transition matrix and ergodic properties), strong laws of probability, convergence theorems, and law of iterated logarithm. Prerequisite: MAC 2313.
STA 5507 Nonparametric Methods (3). Distribution-free tests: sign, Mann-Whitney U, Wilcoxon signed rank, Kruskal-Wallis, Friedman, etc. Rank correlation, contingency tables and other related topics. Credit for both STA 4502 and STA 5507 will not be granted. Prerequisite: A course in statistics.
STA 5666 Advanced Statistical Quality Control (3). Review of statistical methods useful in quality improvement. Statistical process control. Taguchi’s and Deming’s philosophies. Control charts. Process capability analysis. Acceptance sampling plans. Prerequisities: STA 3033 or STA 3163 or STA 4321 or equivalent.
STA 5676 Reliability Engineering (3). The course material is designed to give the student a basic understanding of the statistical and mathematical techniques which are used in engineering reliability analysis. A review will be made of the basic fundamental statistical techniques required. Subjects covered include: distributions used in reliability (exponential, binomial, extreme value, etc.); tests of hypotheses of failure rates; prediction of component reliability; system reliability prediction; and reliability apportionment. Prerequisite: STA 4322.
STA 5800 Stochastic Processes for Engineers (3). Probability and conditional probability distributions of a random variable, bivariate probability distributions, multiple random variables, stationary processes, Poisson and normal processes. Prerequisites: MAC 2313, MAP 2302, STA 3033.
STA 5826 Stochastic Processes (3). This course is intended to provide the student with the basic concepts of stochastic processes, and the use of such techniques in the analysis of systems. Subjects include: Markov Processes, queuing theory, renewal processes, birth and death processes, Poisson and Normal processes. Applications to system reliability analysis, behavioral science, and natural sciences will be stressed. Prerequisite: STA 5447.
STA 5906 Independent Study (1-6). Individual conferences, assigned reading, and reports on independent investigation.
College of Arts and Sciences