MATH 375 Syllabus

Statistical Methods II

Revised: February, 2015

Course Description

Analysis of variance, regression, and correlation; analysis of categorical data; distribution free procedures. Prerequisites: MATH 270. Three semester hours.

Objectives

By the end of the course students will be able to

  • Test models used for nominal and ordinal data scales;

  • Fit data to linear and non-linear regression curves and test the appropriateness of the model;

  • Compare means from several populations simultaneously and examine ways to describe variation in the data when fitted to the various models; and

  • Interpret the results of statistical analyses

Text

Roxy Peck, Chris Olsen, and Jay DeVore. Introduction to Statistics and Data Analysis, Fourth Edition. (Brooks-Cole/Cengage Learning), 2011.

Grading Procedure

Grading procedures and factors influencing course grade are left to the discretion of individual instructors, subject to general university policy.

Attendance Policy

Attendance policy is left to the discretion of individual instructors, subject to general university policy.

Course Outline

  • Since Chapters 1 through 10 are covered in Math 270, they should be briefly reviewed as needed.
    o Chapter 1: The Role Of Statistics.
    o Chapter 2: The Data Analysis Process And Collecting Data Sensibly.
    o Chapter 3: Graphical Methods For Describing Data.
    o Chapter 4: Numerical Methods For Describing Data.
    o Chapter 5: Summarizing Bivariate Data.
    o Chapter 6: Probability.
    o Chapter 7: Population Distributions.
    o Chapter 8: Sampling Variability And Sampling Distributions.
    o Chapter 9: Estimation Using A Single Sample.
    o Chapter 10: Hypothesis Testing Using A Single Sample.
  • Chapter 11 Comparing Two Populations Or Treatments. (6 days)
    Inferences Concerning the Difference Between Two Population Means or Treatments Using Independent Samples. Inferences Concerning the Difference Between Two Population Means Using Paired Samples. Large-Sample Inferences Concerning a Difference Between Two Population Proportions. Distribution-Free Procedures for Inferences Concerning a Difference Between Two Population Means Using Independent Samples (Optional). Interpreting the Results of Statistical Analyses.
  • Chapter 12 The Analysis Of Categorical Data And Goodness-Of-Fit Tests. (6 days)
    Chi-squared Tests for Univariate Categorical Data. Tests for Homogeneity and Independence in a Two-Way Table. Interpreting the Results of Statistical Analyses.
  • Chapter 13: Simple Linear Regression And Correlation Inferential Methods. (8 days)
    The Simple Linear Regression Model. Inferences Concerning the Slope of Population Regression Line. Checking Model Adequacy. Inferences Based on the Estimated Regression Line (Optional). Inferences About the Population Correlation Coefficient (Optional). Interpreting the Results of Statistical Analyses.
  • Chapter 14: Multiple Regression Analysis. (6 days)
    Multiple Regression Models. Fitting a Model and Assessing its Utility. Inferences Based on an Estimated Model. Other Issues in Multiple Regression.
  • Chapter 15: The Analysis Of Variance. (6 days)
    Single Factor ANOVA and the F Test. Multiple Comparisons. The F Test for a Randomized Block Experiment. Two-Factor ANOVA. Interpreting the Results of Statistical Analyses.
  • Chapter 16: Nonparametric (Distribution-Free) Statistical Methods (8 days)
    Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Independent Samples (Optional). Distribution-Free Procedures for Inferences About a Difference Between Two Population or Treatment Means Using Paired Samples. Distribution-Free ANOVA.

    Most instructors for this course require the use of statistical calculators.

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