May 21, 2024  
2023-2024 General Catalog 
    
2023-2024 General Catalog [ARCHIVED CATALOG]

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MAT 162 - Business Statistics


Course Department: Mathematics
Last Date of Approval: Spring 2023

4 Credits
Total Lecture Hours: 60
Total Lab Hours: 0
Total Clinical Hours: 0
Total Work-Based Experience Hours: 0

Course Description:
This course is an extension of introductory statistics, primarily for business majors. It investigates methods of collection, organization, presentation, analysis and interpretation of data as tools in effective business decision-making.  The course covers descriptive and inferential statistics, probability, confidence intervals and hypothesis testing for one and two samples, regression, correlation and chi-square. Computer applications are used to assist in visualizing and analyzing data.  This course will also help students gain mathematical literacy which will be of vital significance when making important life decisions. In addition, this course will help with any career that involves applied mathematics, decision making, or problem solving. 

Prerequisites: Completed MAT 157 - Statistics  or equivalent with a C or better. 

 
Mode(s) of Instruction: Traditional/Face-to-Face

Credit for Prior Learning: There are no Credit for Prior Learning opportunities for this course.

Course Fees: ebook/Access Code: $124.99 (charged once per term for all courses that use Cengage Unlimited)

Common Course Assessment(s): None

Student Learning Outcomes and Objectives:
Outcome 1: Analyze and interpret data.
1.    Identify various sampling methods and survey designs. 
2.    Display data using tables and graphs.
3.    Determine a five - number summary and use it to construct a boxplot. 
4.    Calculate and interpret measures of center.
5.    Calculate and interpret measures of variation.

Outcome 2: Interpret and evaluate probabilities.
1.    Compute and interpret probability. 
2.    Construct a probability distribution and find the mean and standard deviation. 
3.    Calculate probability for a given discrete distribution (binomial, Poisson, etc).
4.    Calculate probability for a given normal distribution. 

Outcome 3: Apply inferential statistics and hypothesis testing methods.
1.    Find the point estimate and construct a confidence interval for the population mean and proportion.
2.    Construct and interpret confidence intervals for the difference between two population means and proportions.
3.    Compute the sample size for the specified margin of error and confidence level.
4.    Perform a hypothesis test for one population mean and proportion.
5.    Define and apply the concepts of Type I and Type II errors.
6.    Compute Type II error probabilities and the power of a hypothesis test.
7.    Perform a hypothesis test for two population means and proportions. 
8.    Perform a hypothesis test for one and two population standard deviations (if time permits).  

Outcome 4:Apply analysis of variance and inferential methods in regression and correlation. 
1.    Use the least-squares criterion to determine the regression equation.
2.    Explain what it means for a set of data to satisfy the assumptions for the regression model.
3.    Calculate the standard error estimate.
4.    Calculate an estimate for the mean of the population of y-values that correspond to a particular x-value.
5.    Perform a residual analysis.
6.    Perform prediction for an individual y-value corresponding to a particular x-value.
7.    Perform inferences for the slope of the population regression line.
8.    Perform inferences in correlation. 
9.    Determine F-values from the table.
10.    Perform the one-way analysis of variance test.
11.    Perform a multiple-comparison method.

Outcome 5:Utilize other selected statistic procedures such as multiple regression, chi-square tests, or non-parametric procedures.
1.    Perform the chi-square goodness-of-fit test.
2.    Determine chi-square values from the table.
3.    Perform the chi-square independence test.
4.    Perform the chi-square test for population standard deviation.
5.    Perform non-parametric hypothesis test for population means (if time permits). 
6.    Use technology to perform and interpret multiple regression analysis (if time permits). 



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