Statistical Methods II

Revised: November 2006

Course Description

Description: Statistical analysis of data, to include non.parametric methods, hypothesis testing, correlation and regression analysis, and methods used in one and two way analysis of variance. Prerequisite: MATH 270. Three semester hours.

Objectives

   1. Acquaint students with techniques used to test models used for nominal and ordinal data scales.

   2. Develop methods for fitting data to linear and non.linear regression curves and test the appropriateness of the model.

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

Text

Jay DeVore & Roxy Peck. Statistics the Exploration and Analysis of Data, Fourth Edition. W. H. Freeman, 2001.

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. (8 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. (8 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. (10 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. (5 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. (10 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.

* Note:

         1. At appropriate places in this course, time should be allotted to elaborate on the historical aspects relevant to the subject.

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