An introduction to the basic concepts and techniques of probability and statistics with an emphasis on applications. Topics to be covered include the axioms of probability, combinatorial methods, conditional probability, discrete and continuous random variables and distributions, expectations, confidence interval estimations, and tests of hypotheses using the normal, t- and chi-square distributions. Students taking this course may not subsequently receive credit for MATH 114, except by permission of the chair. Not open to students who are taking or have received credit for MATH 611.

Statistics Elective

This course covers basic statistics, including: measures of central tendency, measures of dispersion, graphs, correlation, the regression line, confidence intervals, the significance of differences, and hypothesis testing, including z-tests, t-tests, and chi-square tests.

Mathematics Elective

3 hours; 3 creditsSample spaces; combinatorial theory; elementary probability; randomvariables; discrete and continuous probability distributions; confidenceintervals; hypothesis testing. (Not open to students who are enrolled inor have completed Mathematics 3501 [51.1].)Prerequisite: Mathematics *1206 [4.3] or 1211 [4.31] or 5.10 or 5.20.

An in-depth study of permutations and combinations, conditional probability, independent events, random variables, probability distributions and densities, expectation, moments, moment generating functions, functions of random variables, Central Limit Theorem, Law of large numbers, sampling, confidence intervals.

Measures of central tendency and dispersion, the normal curve, hypothesis testing. Linear correlation and regression, basic concepts in probability with application to problems in the social, behavioral, physical, and biological sciences. Statistical computer programs will be used extensively. Not open to students who have completed MTH 214. (math). (STEM)

CBA Credit - Liberal Arts

This course will provide students with an in-depth understanding of the fundamental concepts and computational methods of statistics. These concepts will be developed through the question of how to estimate an unknown quantity using sample data. Students will learn to incorporate the foundational concepts of mathematics with statistical analysis to describe and solve real-life problems and questions. Students will be taught to use estimation and precision and will learn the math study skills to assess and enhance their learning, their processes and their results. Students will use statistical software, graphing calculators, Microsoft Excel, MyMathLab and MyStatsLab to carry out a semester-long project involving data description and analysis. Students will work collaboratively and write using appropriate mathematical and non-mathematical language in order to successfully complete their project. Thee topics addressed include: displaying categorical data using tables, bar graphs, and circle graphs; drawing conclusions about categorical data; displaying quantitative data using dot plots, stem-and-leaf plots, histograms and box-and-whisker plots; describing data distributions using measures of center (mode, mean, and median) and measures of spread (standard deviation, range and IQR); Displaying bivariate data using scatterplots; analyzing bivariate data using linear regression; elementary probability; normal probability distributions, sampling distributions; confidence intervals and hypothesis testing of the proportion and the mean.

The student will identify, define, and compute the measures of central tendency and dispersion; develop frequency distributions and related histograms; determine the level of correlation; and draw inferences from regression lines. The student will also solve problems involving sample spaces, counting techniques, and mathematical expectation; determine the probability of normally distributed events through use of tables; conduct hypothesis testing; and determine confidence intervals.

Satisfies Required Core - Mathematical & Quantitative Reasoning

Emphasis on the probability theory necessary for the study of statistical inference. Topics include studies of discrete, continuous, and multivariate distributions. Applications to problems involving normal, binomial, Poisson, and other distributions. Introduction to theory and methods of testing hypotheses and of estimation.

Elective Credit

Mathematics Elective

NOT YET EVALUATED

This course will provide a calculus-based introduction of probability theory and applications to statistical inference. Topics will include discrete and continuous probability distributions, moment generating functions, laws of large numbers, limit theorems, sampling distributions and statistical inference using z, t, F and c2 distributions.

Elective Credit

Axioms of probability, combinatorial methods, conditional probability, discrete and continuous random variables and distributions, binomial, Poisson, normal and exponential distributions, independent discrete random variables, Law of Large Numbers and the Central Limit Theorem, expectation, confidence intervals and test of hypotheses.

Elective Credit

Introduces the basic principles of statistics and probability, with an emphasis on understanding the underlying concepts, real-world applications, and the underlying story that the numbers tell. Uses Microsoft Excel's statistical functions to analyze data. Provides an introduction to probability, descriptive statistics, hypothesis testing, and inferential statistics.

Elementary data analysis, simple regression, correlation, basic probability theory: combinatorial problems, random variables, distributions, expectation; law of large numbers, central limit theorem, applications.