Effective: 2017-08-01

Course Description

The course outline below was developed as part of a statewide standardization process.

General Course Purpose

To serve as a second course in statistics that focuses on multivariate and nonparametric techniques useful to business, science, and social science majors.

Course Prerequisites/Corequisites

Prerequisite: Completion of MTH 245 or equivalent with a grade of C or better.

Course Objectives

• Review of Hypothesis Testing
• Conduct hypothesis tests for population means and proportions.
• Conduct a hypothesis test for the equality of two population means where:
• The samples are independent and the population variances are assumed unequal.
• The data consists of matched pairs.
• Conduct a hypothesis test for the presence of correlation.
• Experimental Design
• Define and apply the basic principles of design, including randomization, replication, and treatment/control groups.
• Explain single and double blinding.
• Describe the placebo and experimenter effects and describe how they can be countered using blinding.
• Design experiments using the following methods:
• Completely randomized.
• Randomized block.
• Matched pairs.
• Explain the concept of confounding.
• Correlation and Regression
• Construct and interpret the residual plot related to a simple least-squares regression model.
• Conduct hypothesis tests related to the coefficients of a simple least-squares regression model.
• Construct and Apply a logistic regression model.
• Calculate the coefficient of determination, the adjusted coefficient of determination, and overall P-value for a multiple regression model and use them to construct a best-fit multiple regression equation.
• Categorical Data Anaylsis
• Conduct chi-squared tests for:
• Goodness of fit.
• Independence between rows and columns of a two-way contingency table.
• Homogeneity of population proportions.
• Analysis of Variance (ANOVA)
• Conduct one-way ANOVA to test the equality of two or more population means for both equal and unequal sample sizes and recognize its relationship to the pooled two sample t-test.
• Conduct a multiple comparison test, such as Tukey's HSD, to determine which of the three or more population means differs from the others.
• Conduct two-way ANOVA on sample data categorized with two fixed factors.
• Nonparametric Methods
• Determine the rank of each element of a sorted data set.
• Identify the relationship between a nonparametric test and its corresponding parametric technique.
• Conduct a Wilcoxon signed-ranks test for a single sample.
• Conduct a Wilcoxon signed-ranks test for matched pairs.
• Technology Application
• Construct statistical tables, charts, and graphs using appropriate technology.
• Perform statistical calculations using an appropriate statistical software package.
• Complete statistical project. Students are required to complete some form of semester project in their course that is worth a significant portion of the student's grade. This could be either an individual or group effort, and could be completed in stages through the semester or as a single, stand-alone exercise. As a minimum, the project should require students to manipulate and draw statistical inferences from a large, realistic data set using a statistical software package.

Major Topics to be Included

• Hypothesis Testing
• Experimental Design
• Correlation and Regression
• Categorical Data Analysis
• Analysis of Variance
• Nonparametric Methods