QMB 3200 - Quantitative Methods for Business

College of Business

Credit(s): 3
Contact Hours: 47
Effective Term Spring 2026 (660)

Requisites

(Admission to Business Administration (Bachelor of Science) (BUS-BS) or
Admission to Management and Organizational Leadership (Bachelor of Applied Science) (MGTORG-BAS) or
Admission to Business Analyst Specialist (Advanced Technical Certificate with Financial Aid Eligibility) (ANLST-ATC)) and
(Prerequisite STA 2023 with a minimum grade of C or
Prerequisite QMB 2100 with a minimum grade of C (course not offered at SPC))

Course Description

This course builds on introductory statistics topics, using quantitative tools for solving real-world business problems. It provides students with a conceptual understanding of the role that data analysis plays in the decision-making process. Numerical data is analyzed and used to make informed decisions for the firm.

Learning Outcomes and Objectives

  1. The student will explain foundational statistics concepts by:
    1. summarizing descriptive statistics.
    2. calculating measures of central tendency, variability, position and correlation.
    3. applying discrete and continuous probability distributions, including binomial and normally distributed data.
  2. The student will examine sampling distributions and confidence intervals by:
    1. identifying types of samples and sampling error.
    2. applying the Central Limit Theorem and standard error principles.
    3. constructing confidence intervals for means and proportions.
  3. The student will break down hypothesis testing for single and dual data sets by:
    1. explaining the hypothesis and alternate hypothesis statements.
    2. differentiating between Type I and Type II errors.
    3. determining the appropriate significance level, critical value and test statistic.
    4. interpreting the results of a test, including p-value.
  4. The student will apply correlation to simple (univariate) linear regression models by:
    1. examining the effect a single factor has on the means of populations.
    2. differentiating dependent and independent variables and their relationships.
    3. determining the significance of a model with Excel.
    4. interpreting a model’s ANOVA statistics.
    5. using a model to make a prediction about the dependent variable.
  5. The student will develop statistically significant multivariate regression models by:
    1. utilizing an advanced statistics software or Excel add-in.
    2. planning appropriate dependent and independent variables to test.
    3. revising the model for multicollinearity and autocorrelation issues.
    4. assessing the different types of regression tests available, including stepwise and best subsets.
    5. testing the regression equation to make a prediction.
  6. The student will create time series forecasting models by:
    1. organizing data sets in Excel.
    2. constructing moving average, smoothing and regression-based data sets.
    3. utilizing dummy variables when appropriate.
    4. making a forecast for the next period of the series.

Criteria Performance Standard

Upon successful completion of the course the student will, with a minimum of 70% accuracy, demonstrate mastery of each of the above stated objectives through classroom measures developed by individual course instructors.

History of Changes

C&I 6/23/2009, BOT 7/21/2009, Effective 20091(0415). C&I Approval: 11/30/2012, BOT Approval: 01/15/2013, Effective Term: Fall 2013 (475). C&I Approval: 02/09/2017, BOT Approval: 03/21/2017, Effective Term: Fall 2017 (535).
C&I Approval: 07/21/2025, BOT Approval: , Effective Term: Spring 2026 (660)

Related Programs

  1. Business Administration (BUS-BS) (610) (Active)
  2. Business Analyst Specialist (ANLST-ATC) (625) (Active)