Common Course Outline

Course discipline/number/title MATH 2350: Introduction to Mathematical Statistics

Catalog Description

Credits 4
Hours/Week 4
Prerequisites (Course discipline/number) MATH 1119 or MATH 1127
Other requirements College level reading
MnTC Goals (if any) NA

Course Description

This course is an introduction to mathematical statistics. Topics will include probability, discrete and continuous random variables, estimation, hypothesis testing, and regression analysis. Computers and graphics calculators will be used extensively in the classroom and computer lab setting. Students may take MATH 1119 or MATH 1127 and MATH 2350 concurrently by contacting the instructor.

Date Last Revised (Month, year)

February, 2025

Outline of Major Content Areas

  1. Statistical Terminology and Methodology
  2. Measures of Center and Dispersion
  3. Hypothesis Testing with Z-tests, t-tests, Chi-Squared Tests, and F Tests
  4. Univariate, Multivariate, and Polynomial Regression and Correlation
  5. Probability, Combinatorics, Expected Value and Bayes Rule.
  6. Probability Distributions, Expectations, and Variances, both Discrete and Continuous
  7. Bayesian Prior and Posterior Distribution Analysis
  8. Computerized Computation of Probability and Statistical Concepts

Learning Outcomes (General)

The student will be able to:

  1. Identify the role of statistics in the analysis of data from engineering, computer science, and the sciences.
  2. Learn to utilize proper statistical terminology to convey the results of their analysis to others.
  3. Calculate and interpret probability theory and then connect the role that probability plays in statistical inference.
  4. Create, evaluate, and integrate graphical representations of data within statistical inference and analysis.
  5. Set up, calculate, and interpret the results of:
    1. Discrete probability distributions
    2. Continuous probability distributions
    3. Estimates using confidence intervals
    4. Hypothesis test for given data sets
    5. Categorical data analysis
    6. Analysis of Variance (ANOVA)
  6. Select, fit, and interpret linear, polynomial, and multiple regression models.
  7. Perform basic Bayesian analysis using Prior and Posterior distributions.
  8. Utilize software and programming languages such as, but not limited to, Excel, Python, and R for statistical analysis.

Learning Outcomes (MnTC)

NA

Methods for Evaluation of Student Learning

Methods may include but are not limited to:

RCTC Core Outcome(s)

This course contributes to meeting the following RCTC Core Outcome(s):

Critical Thinking
Students will think systematically and explore information thoroughly before accepting or formulating a position or conclusion.

Special Information (if any)

Students must have, either personal or RCTC facility, access to a computer for laboratory assignments.