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COURSE SYLLABUS Ohio Northern University College of Arts and Sciences Department of Mathematics and Statistics Date: Fall 2011 Course Stat 2561 Name: Statistics for Professionals 2 Credit hours: 3 Lecture hours/week: 3 Lab hours/week: 0 Instructor: Staff Usual Student Level: Freshman or Sophomore Course required of students in: Pharmacy, Biological and Allied Health Sciences, Business Administration Course frequency per semester/year: Offered Fall and Spring semesters every year Average enrollment per year: 400 This course has a prerequisite: Stat 1561 (preferred) or Stat 1311 or Stat 2501 This course is a prerequisite for: Any Stat 35x1 level course Catalogue Description: Review of inferential statistics; Categorical data analysis (including goodness-of-fit tests and contingency table analysis); Simple linear regression and correlation; Multiple regression analysis; Analysis of variance; Analysis of covariance; Statistical study designs. Course Objectives: To introduce students to the methods categorical data analysis, regression and correlation methods, analysis of variance, and analysis of covariance. Textbook: “Statistics: Principles and Methods” by Johnson and Bhattacharyya (Wiley 6e) Outline of content follows: (see attached) Course Outline Stat 2561 Statistics For Professionals 2 Review of inferential statistics (confidence intervals and hypothesis tests for a single quantitative variable mean and the difference between two quantitative variable means) Inferences for a single proportion and for the difference between two proportions (point estimation, confidence intervals, and hypotheses tests) Categorical data analysis: Pearson chi-square tests for a single categorical variable Contingency table analysis (tests of independence / homogeneity for two categorical variables) Linear regression and correlation Simple linear regression (least squares estimation of regression coefficients, estimation of variance parameter, confidence intervals and hypotheses tests for regression coefficients) Correlation (calculation and interpretation of correlation coefficient, including caveats) Multiple regression (can include a brief discussion or overview if time is limited) Analysis of Variance Single-factor ANOVA (point estimation of group means and variance parameter, hypothesis test of equality of group means, multiple comparisons) Two-factor ANOVA (hypothesis test for a treatment effect in a completely randomized design and/or a randomized block design) Analysis of covariance (can include a brief discussion or overview if time is limited) Study design Experimental vs. observational designs Completely randomized vs. randomized block experimental designs Matched vs. unmatched data Remarks 1. In general, emphasis should be on “statistical reasoning” as opposed to “statistical calculations”. 2. The TI-83 or 84 calculator is required for this course. Please do not use built-in STAT functions to compute confidence intervals or perform hypothesis tests until after the students have mastered these concepts. 3. Students should receive some exposure to statistical software output (such as Minitab or SPSS). Outline of topics from “Statistics: Principles and Methods” (6e) by Johnson and Bhattacharyya: 11. Regression Analysis − I (Simple Linear Regression) Can deemphasize Section 6.4 12. Regression Analysis − II (Multiple Linear Regression and Other Topics) Can cover this material briefly in any way one desires 13. Analysis of Categorical Data 14. Analysis of Variance (ANOVA)