Republic of the Philippines LEYTE NORMAL UNIVERSITY Paterno St., Tacloban City. Course Syllabus in FD nd Semester SY:

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1 Republic of the Philippines LEYTE NORMAL UNIVERSITY Paterno St., Tacloban City Course Syllabus in FD nd Semester SY: I. COURSE CODE : FD 602 II. COURSE TITLE : Advanced Statistics I III. COURSE CREDIT : 3 units IV. PRE-REQUISITE : V. COURSE DESCRIPTION : This course covers topics in nonparametric statistics, ANOVA techniques, multiple and special topics in multivariate statistics and their applications to researches in education, management and the social sciences. The course, likewise, orients the students on the use of the statistical software as a tool for data analysis. VI. GENERAL OBJECTIVES : At the end of the semester, the students are expected to: 1. explain basic concepts and procedures underlying selected nonparametric and multivariable/multivariate parametric statistical tests; 2. choose and apply appropriate data analysis methods given a research problem in one s area of specialization; 3. perform appropriate data analysis using ; 4. interpret computer printouts of data analysis; and 5. appreciate the importance of statistical methods and computerized data analysis in solving research problems in education, management and the social sciences. VII. COURSE CONTENT Weeks Learning Outcomes Content 1 st After the class orientation, the students are expected to have: CLASS ORIENTATION Teaching Strategies Discussion Sharing of ideas Learning Activities Resources Assessment Levelling of expectations on the subject Syllabus

2 2 2 to 3 4 to 6 1. acquainted with the content, grading system, policies, and requirements of the course; and 2. developed their sense of preparedness for the course. 1. classify variables according measurement scales, 2. formulate research and null hypothesis based on a given research problem, 3. identify the most appropriate statistical test given the research problem, 4. carry out the test using, and 5. interpret the output relative to the research problem. 1. identify common designs in generating statistical data, 2. discuss the concept of analysis of variance, 3. verify if the assumptions of the ANOVA are satisfied, 4. carry out analysis of data from single-factor and multifactor experiments using, and 5. interpret the output. REVIEW OF BASIC STATISTICAL CONCEPTS AND TEST OF HYPTHESIS 1. Review on Variables and Scales of Measurement 2. Review of Basic Probability 3. Concepts in Hypothesis Testing 4. Parametric Tests for One Sample, Two Samples and Three or more Samples DESIGN OF EXPERIMENTS AND ANALYSIS OF VARIANCE 1. Classical Experimental Designs 2. Analysis of Variance and its Assumptions 3. Single-factor experiments 4. Factorial experiments 5. Repeated measures design discussion using discussion using MIDTERM EXAMINATION Graded solving Graded solving

3 3 8 to differentiate parametric and nonparametric statistical tests, 2. state the advantages and disadvantages of non-parametric statistics 3. conduct hypothesis testing using non-parametric statistical tests using, and 4. interpret the output. NONPARAMETRIC STATISTICS 1. Introduction to Nonparametric statistics 2. One-Sample Tests of Significance (Binomial Test, Runs Test, and Chi-Square Test) 3. Paired Samples Tests (McNemar Test, Sign Test, Wilcoxon Signed Ranks Test & the Permutation Test for Paired Samples) 4. Independent Samples Tests (Wilcoxon Rank Sum Test, Median Test, Permutation Test, Chi- Square Test) 5. Kruskal Walllis test for completely randomized experiments 6. Friedman test for experiments in randomized blocks 7. Chi-Square test of independence and homogeneity discussion using Graded solving

4 4 11 to differentiate correlation and, 2. discuss the assumptions of linear regression model 3. conduct simple and multiple linear using, 4. perform model validation and residual analysis, 5. identify and correct problems associated with multiple linear, and 6. interpret computer output of linear CORRELATION AND REGRESSION ANALYSES 1. Correlation analyses 2. Simple linear regression analysis 3. Standard assumptions of linear regression model 4. Multiple linear 5. Residual analysis 6. Other issues with multiple linear (outliers, influential points, multicollinearity, serial correlation, dummy variables) discussion using Graded solving 15 to formulate research problems which can be answered using binary logistic and ordinal regression analyses, factor Analysis, and path analysis, 2. carry out binary logistic and ordinal regression analyses, factor analysis, and path analysis using, and 3. interpret computer outputs from. SPECIAL TOPICS 1. Binary logistic regression and ordinal regression analyses 2. Factor analysis 3. Path analysis discussion using FINAL-TERM EXAMINATION Graded solving

5 5 VIII. GRADING SYSTEM Term Examinations : 50 % Class Standing (Problem //Written Reports) : 50 % IX. REFERENCES 1. Acock, A. C. (2014). A Gentle Introduction to, 4 th Ed. Stata Press, 4905 Lakeway Drive, College Station, Texas Chatterjee, S. and Hadi, A. S. (2012). Regression Analysis by Example, 5 th Ed. John Wiley & Sons, Inc., Hoboken, New Jersey 3. Chatterjee, S. and Simonoff, J. S. (2013). Handbook of Regression Analysis. John Wiley & Sons, Inc., Hoboken, New Jersey 4. Corder, G. W. and Foreman, D. I. (2009). Nonparametric Statistics for Non-Statisticians: A Step-by-Step Approach. John Wiley & Sons, Inc., Hoboken, New Jersey 5. Johnson, R. A. and Wichern, D. W. (2007). Applied Multivariate Statistical Analysis, 6 th Ed. Pearson Prentice Hall. Pearson Education, Inc. Upper Saddle River, NJ Milla, N. E. (2002). Study Guide in Social Science Statistics (Unpublished). 7. Pedhazur, E. J. (1997). Multiple Regression in Behavioral Research: Explanation and Prediction, 3 rd Ed. Thomson Learning, Inc. Thomson Learning TM Prepared by: Approved by: NORBERTO E. MILLA, Ph.D. Associate Professor 1 PARA O. TANGANAN, Ed.D. Dean, College of Reviewed by: Submitted to: JUANITA A. AGWANTA, Ed.D. Unit Chair, ARIEL B. LUNZAGA, Ph.D. Dean, Graduate School Date Received: