﻿ MATH203 Introduction to Probability and Statistics | E-Campus

# MATH203 Introduction to Probability and Statistics

Course Code Course Title Weekly Hours* ECTS Weekly Class Schedule
T P
MATH203 Introduction to Probability and Statistics 3 2 6 TUE. 13:00-14:50; THU. 11:00-11:50
Prerequisite MATH101 It is a prerequisite to
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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:
1. Use basic counting techniques (multiplication rule, combinations, permutations) to compute probability.
2. Set up and work with discrete random variables. In particular, calculate and inteopret characteristics of the Bernoulli, Binomial, Geometric and Poisson distributions.
3. Work with continuous random variables. In particular, know the properties of Uniform, Normal and Exponential distributions
4. Demonstrate understanding what expectation, variance, covariance and correlation mean and be able to compute and interpret them.
5. Use the theoretical implications of the law of large numbers and the central limit theorem correctly and appropriately in practice on real-world data sets.
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 time-series 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?): Z-Test; T-test, Paired T-test;
 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 In-term exam study 12 1 12 Active tutorials 2 14 28 Active tutorials 2 14 28 Home study 4 14 56 Total Workload Hours = 150 *T= Teaching, P= Practice ECTS Credit = 6 Course Academic Quality Assurance: Semester Student Survey Last Update Date: 04/03/2020 