Modules of Faculty of Business
MODULE OF INFERENTIAL STATISTICS
1.
Module Code
:
STAT 215
Faculty:
Business Administration
2.
Module Title
:
Inferential Statistics
3.
Level
:
1
Semester:
2
Credits
:15
4.
First Year of Presentation
: 2019-2020
Administering Faculty:
Business Admin.
5.
Pre-Requisite
: Descriptive Statistics (STAT 122)
6.
ALLOCATION OF STUDY AND TEACHING HOURS
:
No
Criteria
Student
hours
Lecture
hours
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
Course of theoretical-practical, belongs to the area of science. Its purpose is to provide students
methods and statistical techniques using a sample to draw conclusions about a population and
make decisions about scientific and technological research in the context of a Christian
worldview. 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. The statistical analysis components of the SPSS software package will be used
extensively in this course.
8.
LEARNING OUTCOMES
8.1 Knowledge and Understanding
After successful completion of this module, every student should be able to:
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.
Know how to use the normal, t, and F tables to compute for probabilities; and also
probabilistic tables for nonparametric tests.
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 logistic regression and multiple linear regression (MLR) procedures to compute
partial analyses and interpret the results.
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:
Acquire fundamental technological skills that allow locating and analyzing databases to
transform them into information.
Evaluate the integrity of information, and to understand the ethical uses of information.
Set up data, from a suitable quantitative study, for data analysis using Excel, SPSS, and a
scientific calculator 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/). I highly recommend that you work
on the sample exams and discuss the answers with your groupmates/classmates.
8.4 General Transferable Skills
Develop and refine decision-making skills by basing decision upon the outcome of
statistical tests.
Analyze real world scenarios and determine the appropriate type of analytical problem
solving techniques to utilize.
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.
The students will be able to read research papers that describe experimental and
non-experimental studies with understanding. They will learn how to conduct several kinds
of inferential tests, and will practice conducting them in order to gain hands on
experience.
Strengthens Christian worldview through various curricular, co-curricular and social
projections developed during the semester.
9.
INDICATIVE CONTENT
Unit 1. Parameter Estimation. Sample Designs
Session Week Objectives [LearningCapabilities] Contents to study in classroom Learning outside the classroom(Assignments)
1 01/09 Summarize a set of datausing appropriate descriptive statistics.
Review of Syllabus and course requirements. Introduction of applied statistics in business. Quiz 1 to remember the basic tools of descriptive statistics. Download Chapter I of the lecture notes (posted on the course web site).
Solve the Quiz 1 that you have given in first class. In the next class, at random, some students will work out the exercises on the board. The objective of this quiz is that the student refreshes the knowledge and skills acquired in Descriptive
Statistics, since it is important to understand Inferential.
2 01/16
Provide participants with a basic knowledge of the SPSS program in to be able to use it in an business context and in the exploration of data
Understand the main features of SPSS. Use the SPSS effectively. Perform descriptive analyses with SPSS. Perform common parametric and non-parametric tests. Perform simple regressions and multivariate analyses.
Practice all the exercises of the module in class and out of class for at least 3 extra hours per week.
3 01/23
Comprehend the basic concepts used in estimating population parameters.
Research Design and Statistics. Course project (analysis and write up of a data set, must be approved by lecturer).
Introduction to Parameter Estimation. Confidence intervals for the
population mean and population proportion.
Solve assignment 1 due on the week 5.
Work the first step of the course project in groups of five; deliver on the week 5 (29/09-4/10).
4 01/30
Understand the concept of sampling distributions and calculate sample size for qualitative and
quantitative variable
Sample Designs, Estimating Sample Size and the Central Limit Theorem. Methods of collecting data (direct observation, experiments, and surveys)
Unit 2. Parametric Hypothesis Testing. (One, Two groups and ANOVA OR ANCOVA)
Session Date Objectives [LearningCapabilities] Contents to study in classroom Learning outside the classroom(Assignments)
5 02/06
Interpreting Results, significance level, Type I and II errors. Use SPSS and interpret all output that software provided.
Introduction to Hypothesis Testing. Type I and II errors, significance level, p-value. Probability value approach comparing p-value to significance level. Download Chapter II.
Solve assignment 2 due on the week 8 (20-25/10)
6 02/13 Comprehend the basic concepts used in testing statistical hypotheses.
Statistical Inference about Means and Proportions with a Single Population.
Continue solving assignment 2. Conduct and correctly interpret t tests for a single population.
7 02/20
Conduct tests of hypotheses and interpret the results of the significance of the Difference Between Two Sample Means
Statistical Inference about Means and Proportions with a two Populations. Independent and Paired samples t-test. Quiz 1.
Conduct and correctly interpret t tests for independent and related test. Deliver assignment 2 before mid exam.
8 02/27
Evidence knowledge of the subject for chapter I and II and applies to real life problems.
Mid-Sem Exam. Exam answers individually With honesty and criterion. Covers all material covered to date. Bring your student ID
The feedback is very important in the process of teaching and learning, it is why the student has the opportunity to review their examination after being evaluated. Solve mid exam posted on the web site course.
9 03/05
Use Analysis of Variance (ANOVA) or Analysis of Covariance (ANCOVA) where appropriate to analyze and interpret data collected from factorial designs.
The F Distribution, ANOVA assumptions. ANOVA and ANCOVA test. Download Chapter III.
Discussion and clarification of doubts to strengthen the knowledge for the Midterm Assessment. Solve assignment 3 due on the week 11 (10-15/11). Conduct and correctly interpret ANOVA and ANCOVA test.
10 03/12
Understand and interpret ANOVA, ANCOVA, and MLR results reported in published reports of research.
Post Hoc Analysis and Multiple
Comparison Tests Review and solving assignment 3 due on the week 11
Unit 3. Nonparametric Methods: Analysis of Ranked Data
Session Date Objectives [Learning Capabilities] Contents to study in classroom Learning outside the classroom (Assignments)
11 03/19
Conduct and interpret a test of hypothesis for independent and related samples using
Nonparametric Methods.
Introduction to Nonparametric Methods: Mann-Whitney’s U test for 2 independent samples. Wilcoxon and McNemar test for 2-Related sample.
Solve assignment 4 due on the week 14 – 19/04.
12 03/26
Conduct and interpret the Friedman and Kruscal-Wallis H test for several dependent and independent samples
Kruscal-Wallis H test for several independent samples. Friedman test for several related sample.
Spearman’s Rho for nonparametric simple relationship
Continue solving assignment 4 due on the week 13 (24-29/11) Construct Mind map for the terms that you learn in chapter 2 and 3
Unit 4. Multivariate Techniques: Partial Correlation, Multiple Regression and Correlation and Logistic Regression
Session Date Objectives [LearningCapabilities] Contents to study in classroom classroom (Assignments)Learning outside the
analyses.
Estimation of Coefficients. Prediction. Coefficient of Multiple Determination. Multiple Correlation.
on the week 15 (08-13/12). Evaluate whether an experimental design was appropriate for answering the research question.
14 04/09
Differentiate when use regression analysis and logistic regression to analyze and interpret data collected and be aware of the basic principles or assumptions of experimental design.
Inferences in Multiple Linear Regressions. Evaluating the
assumptions of Multiple Regression: Normality, Linearity and
Multicollinearity and Independent Observation. Partial Correlation. Logistic Regression.
Continue solving assignment 5 due on the week 08-13/12. Conduct and correctly interpret multiple regression analysis and Logistic Regression.
15 04/16
Demonstrate mastery of the concepts and techniques learned in the class through the course project.
Presentation of the course project: Data base in SPSS, analysis and presentation in power point by groups.
The student will analyze a set of data. The data can come from a source available to the student or can be obtained from the Instructor. Deadline to present the complete course project.
16 04/23
The purpose of taking the final exam is that the students demonstrate their knowledge of the subject matter and as applied and evaluates the results.
Final Exam. Covers all material since the beginning of the course. Bring your student ID, a pen, scientific calculator and computer if you have.
The feedback is very important in the process of teaching and learning, it is why the student has the opportunity to review their examination after being evaluated.
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 teacher:Guidance and counseling teacher for clarification of doubts and
assistance in carrying out their assignments.
The 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.
1. Making decisions based on Christian principles axiological.
2. Daily Bible Study (Sabbath School lesson)
3. Using biblical references in academic.
4. Conservation and promotion of the environment, etc.
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 8.1.2; 8.1.3; 8.1.1
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 with weak performance in accounting, 2 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.
Lecturers will be available for individual consultations. 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).
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).
Keller, G. & Warrack, B. (2000). Statistics for Management and Economics. 5th Ed. Canada: Duxbury.
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
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/
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
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
Kwabena Gyimah-Brempong* and Anthony 0. Gyapong. Characteristics of education Production Functions: An Application of Canonical Regression Analysis. Economics of Education Review, Vol. 10, No. I, pp. 7-17. 1991. Retrieved from: https://wweb.uta.edu/management/Dr.Casper/Fall10/BSAD6314/BSAD%206314-Student%20Articles/Cononical%20Articles/Canonical%20Regression.pdf
How2stats. (2011). MANOVA - SPSS (part 4). Retrieved from: http://www.youtube.com/watch? v=ZBA8SXBrFgg&feature=related
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
