Introduction to Statistics
“Until the phenomena of any branch of knowledge have been submitted to a measurement and number it cannot assume the status and dignity of a science” -Francis Galton
Instructor: Dilek Guvenc
Room: SA-105(Faculty of Science
E-mail : firstname.lastname@example.org
Office Hours: Monday 13:40-15:30 and Friday 10:40-11:30
Section 01 Tuesday 10:40 - 12:30 Room # G 236
Friday 9:40 - 10:30 Room # G 236
and Statistics for Engineers
Richard L. Scheaffer, Madhuri S. Mulekar, James T. McClave; 5-th Edition Cengage Learning, 2011
Exams & Grading : 1st Midterm (27%)
Date: November 3, 2017 (Friday )
2nd Midterm (27%)
Date: December 12, 2017 (Tuesday )
Final Exam (34%) Will be scheduled...
Attendance: Attendance will be taken regularly.
Homeworks ( 12% ) There will be 6 homeworks throughout the semester. Homeworks will be emailed on Tuesday of homework-weeks (See syllabus below), they will be collected one week later and solutions of the homework questions will be emailed to the students.
· Student will be qualified to take the final exam if his/her total is at least 20 over 66, by the last day of the classes.
· Students who do not have least total score of 20 over 66 will be given an FZ grade.
· Students who have missed a midterm exam and have valid, well-documented excuse can take a makeup of it. A single makeup exam will be given few days after the second midterm exam.
· Reserved 4-th hour will be used in some weeks, this will be announced in the class.
§ Usage of a statistical software such as MINITAB is encouraged.
· This course is not a regular math course! It involves qualitative thinking and more words than other mathematics courses.
§ Following and understanding the lectures is necessary but not sufficient for understanding the material. The best way for that is to do more problems, especially "supplementary exercises" from the textbook.
1. Sept. 18 Tools for Describing Data.
2. Sept. 25 The Sampling Distribution of Sample Mean, Sample Proportion and Sample Variance. (Hw 1)
3. Oct. 2 Point Estimators. Properties of Point Estimators. The Method of Maximum Likelihood.
4. Oct. 9 Confidence Intervals: The Single-Sample Case, Prediction Intervals. (Hw2)
5. Oct. 16 Confidence Intervals: The Multiple-Sample Case.
6. Oct. 23 Hypothesis Testing: The Single-Sample Case (Hw 3)
7. Oct. 30 Review, Applications & the First Midterm
( Friday, November 3, 2017 @ 17:45 )
8. Nov.. 6 Hypothesis Testing: Multiple-Sample Case
9. Nov. 13 Chi-Squared Tests on Frequency Data. (Hw 4)
10. Nov. 20 Simple Regression. Probabilistic Models Fitting the Model: The Least-Squares Approach , The Probability Distribution of the Random Error Component.
11. Nov. 27 Making Inferences about the Slope . The Coefficient of Correlation. The Coefficient of Determination. (Hw 5)
12. Dec. 4 Review, Applications and the Second Midterm.
(Friday, December 8, 2017 @ 17:45)
13. Dec. 11 Using the Model for Estimation and Prediction. Residual Analysis. Transformations. Multiple Regression Analysis. (Hw 6 )
14. Dec. 18 Multiple Regression Analysis (Cont.).