MATH306 Statistical Modeling
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Course Code  Course Title  Weekly Hours*  ECTS  Weekly Class Schedule  
T  P  
Prerequisite  It is a prerequisite to  None 

Lecturer  Emin Tahirović  Office Hours / Room / Phone  Monday: 13:0014:00 (via MS Teams) Tuesday: 13:0014:00 (via MS Teams) Thursday: 13:0016:00 (via MS Teams) 

etahirovic@ius.edu.ba  
Assistant  Assistant Email  
Course Objectives  The aims of this course are to study common statistical techniques. The emphasis will be upon the understanding and use of statistical methodology, and the written communication of the results of data analysis. Students should gain practical experience in elementary data management and analysis techniques. Students will become knowledgeable and critical consumers of statistical information that appears in the media, in the workplace, and elsewhere. Upon completion of the course students should be able to use data to make recommedations and make infromed decisions regarding any process or phenomenon for which it is possible to collect data. Students will also gain basic familiarity with the statistical software package R. 

Textbook  Applied Statistics and Probability for Engineers, 6th ed., by D. C. Montgomery and G. C. Runger, Wiley, 2014 . This is the course text. I expect to cover Chapters 6–13; some in part, some whole.  
Additional Literature  
Learning Outcomes  After successful completion of the course, the student will be able to:  


Teaching Methods  Lecture slides that serve as a tartig point for class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress that involve real data, computer analysis, summary, interpretation and reporting.  
Teaching Method Delivery  Teaching Method Delivery Notes  
WEEK  TOPIC  REFERENCE  
Week 1  Introduction to statistics; Population and Sample; Random Sampling; Some important statistics; Data description and visualization techniques.  6.1, 6.3, 6.4, 6.5, 6.6  
Week 2  R essentials (import, export, manipulate, data); R data visualization functions;  Omnipresent webresources on R  
Week 3  Sampling distributions; Central Limit Theorem; Standard error of the mean; Quantile and probability plots; Sampling distribution of the sample mean;  6.7, 4.6, 7.1, 7.2. 7.3  
Week 4  Sampling Distribution of the Difference Between Two Sample Means; Sampling Distribution of the Sample Variance;  8.1, 8.2, 8.5  
Week 5  Interval estimation around the mean (applying CLT); Student's Tdistribution; Estimation of the finite sample exact confidence interval for the mean; Interval estimation around the difference between means;  8.1, 8.2, 8.5, 9.1, 9.2, 9.3, 9.5  
Week 6  Bernoulli and Binomial distribution; Interval estimation around the population proportion;  9.1, 9.2, 9.3, 9.5  
Week 7  Sample size and margin of error dependency; Tolerance and prediction intervals;  Lecture Slides  
Week 8  Midterm  
Week 9  Hypothesis (one /twosided); Power of the statistical test; Significance level of a statistical test;  Lecture Slides  
Week 10  Tests of hypotheses about a single population mean, Onesample test for the population proportion;  9.1, 9.2, 9.3, 9.5  
Week 11  Tests of hypotheses for two samples (test for the difference in means (variance known / unknown), Twosample test for the difference between population proportion;  10.1, 10.2, 10.6  
Week 12  Nonparametric tests (MannWhitney, KruskalWallis); Paired Ttest;  10.3, 10.4  
Week 13  OneWay Analysis of Variance (ANOVA); Multiple comparison testing (Bonferroni, Tukey, FDR...); Familywise type 1 error rate;  13.1, 13.2, Lecture Slides  
Week 14  Simple linear regression; (assumptions, least squares, optimality condition for OLS)  11.2, 11.3, 11.4  
Week 15  Model validation; Residual analysis; Fit statistics;  11.6, 11.7 
Assessment Methods and Criteria  Evaluation Tool  Quantity  Weight  Alignment with LOs  
Final Exam  1  40  2  
Semester Evaluation Compenents  
Active Tutorials  12  20  1,4  
Quizzes  2  40  3,5  
*** ECTS Credit Calculation *** 
Activity  Hours  Weeks  Student Workload Hours  Activity  Hours  Weeks  Student Workload Hours  
Lecture Hours  3  15  45  Active Tutorials  2  14  28  
Home Study  2  14  28  Interm Exam Study  17  2  34  
Final Exam Study  15  1  15  
Total Workload Hours =  
*T= Teaching, P= Practice  ECTS Credit =  
Course Academic Quality Assurance: Semester Student Survey  Last Update Date: 16/04/2021 
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