Syllabus Applied Statistics, 2020.doc

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Modules of Faculty of Business

MODULE OF INFERENTIAL STATISTICS

1. Module Code: STAT 612 Masters of Business Administration

2. Module Title: Applied Statistics

3. Level: 6 Semester: 1 Credits:15

4. First Year of Presentation: 2019-2020 Administering Faculty: MBA 5. Pre-Requisite: None

6. ALLOCATION OF STUDY AND TEACHING HOURS:

No Criteria Student

hours Lecturehours

1 Lectures 25 50

2 Seminar /workshops 10 10

3 Practical classes/Laboratory(computer LAB for

statics software) 20 40

4 Structured exercises 25 10

5 Set reading 20 5

6 Self directed study 25 2

7 Assignments-preparation and writing 20 8

8 Examination & participation 5 15

Total student hours 150 140

7. DESCRIPTION OF AIMS AND CONTENT

The course aims to provide the student with the appropriate methods and procedures to process the data and convert it into information, as well as assess the reliability of the conclusions drawn from the sample and guide the decision-making process in the context of a Christian worldview. Students will be competent in the computer analysis of data sets using SPSS statistics software and Excel. Topics for discussion are: Statistical Estimation and Confidence Intervals, Sampling Methods. Parametric Hypothesis Testing: One/Two Sample Test of Hypothesis, ANOVA and Post Hoc Multiple Comparison Tests, Analysis of Covariance (ANCOVA). Nonparametric Test: Mann Whitney U, Kruskal Wallis H, Wilcoxon, McNemar, Friedman, Spearman’s rho. Association between variables measured at the Interval-Ratio Level: Correlation and Regression Theory. Multivariate Techniques: Partial Correlation, Multiple Regression Analysis and Logistic Regression.

8. LEARNING OUTCOMES

8.1 Knowledge and Understanding

After successful completion of this module, every student should be able to:

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 Communicate the results of statistical work, and more specifically write up the results of statistical analysis in a report consisting of an abstract aimed at decision makers and exact interpretation of the results.

 Understand randomness, sampling techniques, and experiments.

 Analyze real world scenarios and determine the appropriate type of analytical problem solving techniques to utilize.

 Analyze and interpret data from a prediction study using one criterion variable and multiple predictor variables.

8.2 Cognitive/Intellectual Skills/Application of Knowledge

The student, having successfully completed the module, will be able to:

 Compute and use descriptive statistics, probability and statistical inference and apply them in the real context.

 Calculate and use probability and inferential statistics to take a sample of the population and use its results for decision making.

 Use Analysis of Variance (ANOVA) or Analysis of Covariance (ANCOVA) where appropriate to analyze and interpret data collected from factorial designs.

 Use multivariate analysis is a tool to find patterns and relationships between several variables simultaneously. It lets us predict the effect a change in one variable will have on other variables.

 Use and interpret when is necessary Non parametric test.

8.3 Communication/ICT/Analytic Techniques/Practical Skills

The student who completed this module will have basic IT skills to:

 Gain skills in problem-solving, using statistical software packages, which allow the student to locate and analyze databases to transform them into information.

 Evaluate the integrity of data, and to understand the ethical uses of information.

 Set up data, from a suitable quantitative study, for data analysis using Excel and SPSS to do statistical computations (enter data, generate descriptive statistics and graphs, estimate population parameters, and perform hypothesis tests).

 Class notes, assignments, syllabus and sample exams are posted in course website (https://sites.google.com/a/upeu.edu.pe/rosa-padilla/).

8.4 General Transferable Skills

 Acquire the ability to interpret the output from statistical tests and data analyses, and communicate the findings to a variety of audiences including business situation, scientist, government, managers and stakeholders.

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 Understand and interpret the test for the means of comparison and also for the relationship with the results reported in the published research reports, therefore, understand the reasoning / basis behind each statistical test.

 Strengthens Christian worldview through various curricular, co-curricular and social projections developed during the course.

9. INDICATIVE CONTENT

Chapter One: fundamental statistical and data analysis skills Chapter Two: Confidence Interval and Sampling

Chapter Three: Hypothesis for Parametric Test

Chapter Four: Analysis of Variance and Analysis of Covariance

Testing

Chapter Five: Hypothesis for Non parametric test

Chapter Six: Multivariate Techniques: Partial Correlation, Multiple Regression Analysis and Logistic Regression.

10. LEARNING AND TEACHING STRATEGY

In the development of the subject will use the following methodology:

 Theoretical class: Exhibition will include a first stage, and then develop constructive learning with student participation, to strengthen cognitive contents.

 Group dynamics: Students form groups to solve exercises and problems programmed for this purpose. After submitting their report, will be the exposure of results obtained, so that reinforce cognitive content, procedural and attitudinal also the respective feedback.

 Individual work: Application Development Course exercises in the specialty outside the classroom.

 Consulting professor: guidance and counseling teacher to clarify doubts and assistance in the performance of their tasks through a personal interview or WhatsApp group.

 Course Project: The student will analyze a data set, demonstrating mastery of the concepts and techniques learned in the class. The data can come from a source available to the student or may be obtained from the Instructor. In either case, the data must be pre-approved by the Instructor. Details of the project will be given during the course.

 Integration Activities

1. Making decisions based on Christian principles axiological. 2. Using biblical references in academic.

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11. ASSESSMENT STRATEGY

Assessment is a necessity in teaching otherwise the learner cannot take this process as a serious thing. According to AUCA internal regulations, Continuous Assessment Tests (CAT) is composed of four parts, which composed of 70% of the grade and the Final Exams 30% as reflected in the assessment pattern table.

12. ASSESSMENT PATTERN

Component Weighting (%) Learning Objective Covered

CAT

Class active participation 10 All Objectives

Course project (analysis and write up of a data set, must be approved by lecturer)

10 Objectives 8.1; 8.2 ; 8.3.2; 8.4

Quizzes and assignments 20 Objectives 8.1.1; 8.1.2

Mid-semester exam 30 Objectives 8.1.1; 8.1.2; 8.1.3

Final examination 30 Objectives 8.1; 8.3

Total 100

13. STRATEGY FOR FEEDBACK AND STUDENT SUPPORT DURING THE

MODULE

Lively class discussions and case-study would form part of the module delivery. This will provide enough opportunity for validating students’ understanding of the theoretical topics discussed during each lesson. For the students who need help, 4 hours per week is given for consultations and explanation in the lecturer’s office. Assignments, quizzes and exams given to students are returned after grading and feedback made known to students as progress report. Feedback on the final assessment is usually published by the Registrar’s office.

14. INDICATIVE RESOURCES

Core Texts (These are available at the reference section of AUCA library)

Textbooks

Derek, W. (2008). Statistics for Business. United States of America: Butterworth-Heinemann. (330 Wal).

Doane, D. & Seward, L. (2013). Applied Statistics in Business and Economics. 4th_{Ed. New York: McGraw-Hill}

Irwin

Groebner, D; Shannon, P. (1985). Business Statistics A Decision- Making Approach. 2nd_{Ed. United States of}

America: Merrill. (519.5 G874).

Healey, J. (2005). Statistics a Tool for Social Research. 7th_{Ed. United States of America: Thomson. (519.5 H 434)}

Hinkle, D., Wiersma, W. & Jurs, S. (2003). Applied Statistics for the Behavioral Sciences. 5th_{Ed. USA: University}

of Toledo.

Kaplan, R. & Saccuzzo, D. (2007). Psychological Testing. Principles, Application and Issues. 6th_{Ed. Indian:}

Thomson. (155 283).

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Levine, D. & Stephan, D. (2010). Even You Can Learn Statistics. 2nd_{Ed. United States of America: Pearson}

Education. (519.5 Lev)

Lind, D., Marchal, Wathen, S. (2013). Basic Statistics for Business and Economics, 8th_{Ed. New York:}

McGraw-Hill. (330 LIN. CP.04).

Lind, D., Marchal, Wathen, S. (2008). Statistical Techniques in Business and Economics, 13th_{Ed. New York:}

McGraw-Hill. (519.5/ LIN. CP.02).

Neter, J. & Wasserman, W. (1974). Applied Linear Statistical Models. Regression, Analysis of Variance and Experimenter Designs. Paris: Richard D. Irwin, INC.

Newbold, P; Carlson, W; & Thorne, B. (2010). Statistics for Business and Economics. 7th_{Ed. United States of}

America: Pearson. (330 N533).

Tabachnick, B. & Fidell, L. (2007). Using Multivariate Statistics. 5th_{Ed. United States of America: Pearson}

Education. (310 T112).

Wiersma, W. & Jurs, S. (2005). Research and Methods in Education. 8th _{Ed. United State: Pearson (Library)}

Internet Links

National Institute of Statistics of Rwanda. Link: https://www.statistics.gov.rw/ Panick, M. (2012). Statistical Inference. Retrieved from:

http://international.scholarvox.com/catalog/book/docid/88813247?searchterm=Statistics%20books Balakrishnan, N., Voinov, V. and Nikulin, M.(2013). Chi-Squared Goodness of Fit Tests with Applications.

Retrieved from: http://international.scholarvox.com/reader/docid/88811763?searchterm=Statistics%20books Everitt, B., Landau, S. and Leese, M. (2011). Cluster Analysis. Retrieved from:

http://international.scholarvox.com/catalog/book/docid/88803189?searchterm=Statistics%20books Patrick JMT.(2013). Bayes’ Theorem/Law. Retrieved from: https://www.youtube.com/watch?v=E4rlJ82CUZI Jbstatistics. (2012). An Introduction to Continuous Random Variables. Retrieved from: https://www.youtube.com/

watch?v=OWSOhpS00_s

Dell. (2016). Electronic Textbook by StatSoft - organized by "modules" accessible by buttons, representing classes of analytic techniques. A glossary of statistical terms and a list of references for further study are included. Retrieved from: http://www.statsoft.com/textbook/stathome.html

RStatsInstitute. (2011). Excel and Economics Statistics. Retrieved from: https://www.youtube.com/watch? v=QkG9K7BYz_c&index=1&list=PL09A6B27CDCD97205

Garson, D. (2012). Online Textbook - One of the most comprehensive statistics texts on the internet presented with a social science orientation. Retrieved from: http://www2.chass.ncsu.edu/garson/pa765/statnote.htm Hans Mikelson. (2011). ANOVA Example Part 1 of 2. Retrieved from: http://www.youtube.com/watch?

v=ZFCzSRg0ibg&feature=related

Forrest, Y.; De Leeuw, J. & Takane, J. Regression with qualitative and quantitative Variables: an alternating least Squares method with optimal scaling Features. Psychometrika--vol. 41, no. 4. Retrieved from:

http://takane.brinkster.net/Yoshio/p006.pdf

ANOVA Example Part 1 of 2. Retrieved from http://www.youtube.com/watch?v=ZFCzSRg0ibg&feature=related Hisashi Yamada. (2009). A Factorial Analysis of the Decline in Japan's Labor Productivity Through an

International Comparison by Industry- Strategies for Increasing Productivity in Retail and Services. Japan Research Institute, Limited. Retrieved from: http://www.jri.co.jp/english/release/2008/080904/

Introductory Statistics - Chapter 10: Regression. Retrieved from: http://www.youtube.com/watch? v=MIqyiGvrUXE&feature=related

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How2stats. (2011). Multiple Regression - SPSS (part 5). Retrieved from: http://www.youtube.com/watch? v=UiJ4G3rLlXA&feature=related

Withers, C. and Nadarajah S. (2008). Canonical regression models for exponential families. Journal of the Korean Statistical Society, volume 37, pp. 119-127. Retrieved from: http://www.irl.cri.nz/canonical-regression-models-exponential-families

Websites

15. COMPUTER REQUIREMENTS

The teacher provides the course on the Web: modules of the chapters, notes, homework, instructions to solve the course project, model of previous exams and tutorial of the statistical software.

Padilla, R. (2011). Course website: Class notes, data sets, syllabus, handouts, exams, etc. Retrieved from: https://sites.google.com/a/upeu.edu.pe/rosa-padilla/

16. MODULE TEAM

Dr. Rosa Padilla de Casamayor, Team Leader Dr. Santiago Casamayor, Member

17. UNIT APPROVAL

Deans and Heads of all Departments contributing to the programme to confirm agreement

FACULTY _HOD/DEAN

1 Signature_{Print Name: Dr. Butera Edison}

Dean, Faculty of Business Administration

2 Signature_{Print Name: Dr. Musabyimana Ruzima William} HOD, Faculty of Business Administration

Seen and Agreed

Library Signature_{Print Name: Mukabariza Rachel}_(Director)

ICT Signature_{Print Name: Dr. Nigigema Papias (Director)}