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Course Code  Course Title  Weekly Hours*  ECTS  Weekly Class Schedule  
T  P  
MATH203  Introduction to Probability and Statistics  3  2  6  TUE. 13:0014:50; THU. 11:0011:50  
Prerequisite  MATH101  It is a prerequisite to  
Lecturer  Office Hours / Room / Phone  Currently not available 

Assistant  TBD  Assistant Email  
Course Objectives  This course is designed to promote understanding and knowledge of statistical methods and concepts used in engineering and natural sciences. Students will be introduced to a wide range of statistical techniques for analyzing data. Students will learn how, when and why statistics are used and why it is necessary to understand them. The topics to be studied are conceptualization, operationalization, and measurement of phenomena from their applied area of studies. Students will learn how to summarize data with graphs and numbers, make generalizations about populations based on samples of the population, and describe the relationships between variables. Students are not expected to become expert statisticians, but they are expected to gain an understanding of how statistics can be used to contribute to their scientific argumentation and for other more general types of questions. Students will become knowledgeable and critical consumers of statistical information that appears in the media, in the workplace, and elsewhere. Students will also gain basic familiarity with the statistical software package R.  
Textbook  We will use and follow the standard textbook for introductory stats class, “Elementary Statistics A Step By Step Approach”, 9th ed, by Allan G. Bluman (freely available on the web).  
Learning Outcomes  After successful completion of the course, the student will be able to:  


Teaching Methods  Class discussions with examples. Active tutorial sessions for engaged learning and continuous feedback on progress. Homeworks that involve problems involving concepts covered in lectures, checks through computer simulations, interpretation of the results.  
WEEK  TOPIC  REFERENCE  
Week 1  Course description and presentation (Objectives, requirements, rules, students rights and responsibilities)  
Week 2  Introduction to R and RStudio; Importing data in R, Correspondence of basic types in R and variables and types of data in general;  
Week 3  Data collection and sampling techniques; Experimental design;  
Week 4  Constructing and interpreting a pie chart, bar graph, histogram, line graph, and timeseries chart; Analyzing and interpreting charts and graphs in the literature.  
Week 5  Defining all measures of central tendency, explaining their differences, relative strengths and weaknesses; (the mode, the median, the mean and percentiles; Determining the shape of a distribution.  
Week 6  Understanding the importance of measuring variability (range, interquartile range, the variance, and the standard deviation); Percentiles; Outliers;  
Week 7  Sample spaces and probability; Addition and Multiplication rules;  
Week 8  Midterm  
Week 9  Counting Techniques; From Counting Techniques to Probability;  
Week 10  Probability distributions; Discrete Prob. Distributions (mean, variance, standard deviation and expectation)  
Week 11  Binomial Distribution; Multinomial Distribution;  
Week 12  Normal Distribution; Finding area under the Normal Distribution curve; Central Limit Theorem;  
Week 13  Estimation with Confidence; Confidence interval for the mean;  
Week 14  Hypothesis Testing  general concepts  
Week 15  Concept and examples of statistical tests (Could the data analysis result be a coincidence?): ZTest; Ttest, Paired Ttest; 
Assessment Methods and Criteria  Evaluation Tool  Quantity  Weight  Alignment with LOs 
Final Exam  1  40  3,5  
Semester Evaluation Compenents  
Midterm Exam  1  30  2  
Homework  7  30  1,4  
*** ECTS Credit Calculation *** 
Activity  Hours  Weeks  Student Workload Hours  Activity  Hours  Weeks  Student Workload Hours  
Lecture Hours  3  15  45  Interm exam study  12  1  12  
Active tutorials  2  14  28  Final Exam study  16  1  16  
Home study  4  14  56  
Total Workload Hours =  157  
*T= Teaching, P= Practice  ECTS Credit =  6  
Course Academic Quality Assurance: Semester Student Survey  Last Update Date: 04/03/2020 
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