MATH306 Statistical Modeling

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Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
MATH306 Statistical Modeling 3 2 6 Tuesday 13:00-14:50; Thursday 11:00-11:50
Prerequisite MATH203 It is a prerequisite to
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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.
Learning Outcomes After successful  completion of the course, the student will be able to:
  1. Demonstrate ability to decide on appropriateness and the type of descriptive statistical techniques, tools and statistical vizualization software.
  2. Estimate the important characteristics (parameters) of populations using data from properly selected samples.
  3. State, test and interpret hypotheses about parameters of common population models.
  4. Apply regression and correlation analysis techniques correctly using R statistical software.
  5. Use the one-way analysis of variance model, perform multiple comparisons, and interpret the results for decision makers.
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.
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 web-resources 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 T-distribution; 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, One-sample 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), Two-sample test for the difference between population proportion; 10.1, 10.2, 10.6
Week 12 Non-parametric tests (Mann-Whitney, Kruskal-Wallis); Paired T-test; 10.3, 10.4
Week 13 One-Way Analysis of Variance (ANOVA); Multiple comparison testing (Bonferroni, Tukey, FDR...); Family-wise 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 In-term Exam Study 17 2 34
Active Tutorials 2 14 28 Active Tutorials 2 14 28
Home Study 2 14 28
        Total Workload Hours = 150
*T= Teaching, P= Practice ECTS Credit = 6
Course Academic Quality Assurance: Semester Student Survey Last Update Date: 04/03/2020
QR Code for https://ecampus.ius.edu.ba/course/math203-introduction-probability-and-statistics

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