Keshav Pokhrel

Syllabus

Calender
Homework
R-labs
Extra Resources

Introduction to Probability and Statistics, 14th Edition by Mendenhall, Beaver and Beaver.

Meeting Information

Instructor: Keshav P. Pokhrel, Ph.D.
Meeting Times: MW 2:00 PM - 3:15PM
Email: kpokhrel(at)umich.edu
Meeting Location: 2046CB
Office: 2087CB
Office Hours:
Monday 10:30 AM- 12:00PM
Wednesday 5:00 PM- 6:00PM
Friday 10:30 AM- 12:00PM
and by appointments

Course Objectives

Test Hypothesis for the diffrence between two population means
Find diffrence between two population meand: paired differences
Estimate coefficients of fitted line
Fit regression model using Qualitative and quantitative variables
Calculate goodness of fit test for categorical data
Use distribution free tests: nonparametric tests

Textbook

Introduction to Probability and Statistics , 14th Edition, William Mendenhall III, Robert J. Beaver, Barbara M. Beaver; ISBN-13:978-1-133-10375-2,Brooks/Cole. We will be covering chapters 9, 10, 11, 12, 13, 14, and 15. Apart from text book we will use different resources for the classroom activities and homeworks.
Major Reference Books

Peck, Olsen, and Devore (2012). Introduction to Statistics & Data Analysis. 4th Edition. Publisher: Duxbury.
Diez, Barr, and Cetinkaya-Rundel, OpenIntro Statistics.
McClave and Sincich (2012). Statistics, 12th edition, by Pearson.

Homework

At least five sets of homework problems will be assigned. Some addition homework problems will periodically be assigned during the lecture. Good news! lowest homework grade will be dropped. For better exam results you need to master all the homework problems. The single most important part of this course is doing your homework and review questions.

Exams

There will be two mid-term exams, and a comprehensive final. To answer the exam questions, you are expected to have clear mathematical reasoning of the statistical methods used to solve the subject problem.

Project

There will be two mini-projects during the semester. For a good project, you need to describe the data, pose reasonable hypotheses, select appropriate statistical tests, compute the test results, and explain the results in both statistical terms and in plain English. Primary objective of these projects is to apply statistical methods in the real life situations.

Software

We will use a software called "R". R is a programming language for statistical computing and visualizing data. It can be downloaded for free from http://www.r-project.org. We will R Studio for regular classroom activities. R studio is an open source Integrated development Environment(IDE) for R. To download R click here for windows and here for Mac. After Installing R: click R Studio to download R studio.

Evaluations/Important Dates:
Exam I (20%)	Monday, February 15
Exam II (20%)	Monday, April 04
Mini Project I(10%):	Due Feb 24
Mini Project II(10%):	Due April 13
Homeworks (15%)	TBD
Final Exam (25%)	Monday, April 25(3:00PM-6:00PM)

Grade Distribution

Your final grade will be based on two mid-term exams, at least five set of graded homeworks, two mini-projects, and a comprehensive final. Lowest homework grade will be dropped. Your performance is measured by the weighted average of exams, homeworks, and projects. The lowest midterm grade will be replaced by final if your final examination score, in percentage, happens to be more than the lowest midterm score. The table below shows percentage intervals for the distributions of letter grades:

Letter Grade	E	D-	D	D+	C-	C	C+	B-	B	B+	A-	A	A+
Percentage	0-59	60-62	63-66	67-69	70-72	73-76	77-79	80-82	83-86	87-89	90-92	93-96	97-100

Disability Statement

The University will make reasonable accommodations for persons with documented disabilities. Student need to register with Disability Resource Services (DSR) every semester they are enrolled for classes. DRS is located in counseling & Support Services, 2157 UC. To be assured of having services when they are needed, students should register no later than the end of add/ drop deadline of each term. Visit the DSR website at: webapps.umd.umich.edu/aim. If you have disability that necessitates an accommodation or adjustment to the academic requirements stated in this syllabus, you must register with DRS as directed above and notify me. Upon receipt of your notification, we will make accommodation as directed by DRS.

Academic Integrity

The University of Michigan-Dearborn values academic honesty and integrity. Each student has a responsibility to understand, accept, and comply with the University's standards of academic conduct as set forth by the Code of Academic Conduct (mdearborn.edu/policies_st-rights), as well as policies established by each college. Cheating , collusion, misconduct, fabrication, and plagiarism are considered serious offenses, and may be monitored using tools including but not limited to TurnItIn. Violations can result in penalties up to and including expulsion from the University. At the instructor's direction, the penalty may be a grade zero on the assignment up to and including recommending that student be expelled from the University. It is the sole responsibility of the student to understand and follow academic guidelines regarding plagiarism. The University of Michigan-Dearborm has an online academic integrity tutorial that can be accessed at: umdearborn.edu/umemergencyalert

Safety

All students are strongly encouraged to register in the campus Emergency Alert System, used to communicate with campus community during emergency. More information on the system and how it works, along with enrollment information can be found at: webapps.umd.umich.edu/aim

Tentative Academic Calender

Date	Sections	Topics covered	Remarks
January 6		Review of Confidence Interval
January 11	9.3	A large-sample test about population mean	Rlab
January 13	9.4	Difference between population means
January 18	No class	MLK DAY
January 20	10.3,10.4	Small -sample inference: independent sample test
January 25	10.4. 10.5	Small -sample inference: independent and paired-difference test
January 27	10.6, 10.7	Comparing two population variances
February 1	11.1-11.4	Introduction: Analysis of Variance
February 3	11.5	Completely randomized design
February 8	11.7	Randomized block design
February 10	11.8	ANOVA for a randomized block design
February 15		Exam 1
February 17	11.10	Factorial experiment
February 22	12.1-12.4	Linear regression
February 24	12.5	The coefficient of determination
February 29- March 04		Break
March 07	12.6	Estimationa and prediction using the fitted line
March 09	13.1-13.3	Multiple linear regression
March 14	13.4	Polynomial regression model
March 16	13.5	Qualitative predictor variables in a regression
March 21	13.6	Testing and training sets of regression coefficients
March 23	14.1-14.3	Goodness of fit-test
March 28	14.4	A two-way classification
March 29	14.5	Comparing several multinomial populations
April 4		Exam 2
April 6	15.1-15.3	The wilcoxon rank sum test
April 11	15.5	The wilcoxon rank sum test: paired experiment
April 13	15.6	Kruskal-Wallis test
April 18		Review
April 25		Final Exam (3:00AM- 6:00 PM)

Homework

	Description	Remarks
	Ex 9.3 : 9.1, 9.3, 9.4, 9.6, 9.8, 9.15, 9.16 Ex 9.4: 9.18, 9.22, 9.28	Due Jan 25
	Ex 9.5: 9.31, 9.35, 9.38, 9.41 9.42, 9.45, 9.50, 9.51	Due Wed Feb 03
	Ex9.6: 9.42, 9.46 Ex 10.3: 10.2, 10.3, 10.6 Ex 10.4: 10.18, 10.19, 10.27, 10.30 Ex 10.5: 10.36, 10.41, 10.46	Wed, feb 10th
	Ex 10.6: 10.49, 10.51, 10.53, 10.55, 10.56 Ex 10.7: 10.58, 10.10.59, 10.61, 10.10.63, 10.66	Exam 1 covers up to section 10.7
	Ex 11.5:: 11.1, 11.2, 11.7,11.12, 11.1411.18 Ex11.8:: 11.28, 11.33, 11.36, 11.37,11.4011.43 Ex11.10:: 11.45, 11.49, 11.5011.52	Quiz from exercise 11.5 on Match 07th.
	Ex 12.4:: 12.1, 12.3, 12.7, 12.9, 12.12, 12.15, Ex12.5:: 12.19, 12.20, 12.23, 12.24, 12.28, 12.30 Ex12.6 :: 12.34, 12.36, 12.38 EX12.7:: 12.40, 12.42, 12.44, 12.45, 12.46	Quiz on march 16th and HW Due March 16th
	Ex 13.4: 13.1, 13.3, 13.5, 13.10, 13.12, 13.15 ExEx13.5: 13.17, 13.18, 13.20, 13.22, 13.24	this will be a part of exam 2

R-labs

	Description	Remarks
	review: Hypothesis Testing
	Data Visualization
	Measures of Data
	Normal Distribution
	Central Limit Theorem
	Book Data	Download

Extra Resources

StatSci.org. A good resource for varieties of data sets. These data sets are open to public and you can use these data sets for your own projects. If you happen to use these data please do not forget to mention the source.
Online Statistics Education This a very helpful resource for introductory statistics.
OpenItro This is an excellent resource for introductory statistics. Apart from lecture notes they also have well explained examples with R code.
Distribution Calculator An excellent App to understand binomial distribution normal approximation to binomial
Install R and R Commander Guideline to download and install R
R commander Perhaps one of the most valuable R-alternative to other statistical packages like SPSS.
Exploratory Data Analysis Wide range of statistical topics are covered in this web page with video lectures and other supplementary materials.
Statistics Online Computational resources interactive apps to calculate different statistical measures.
More Stat Apps Wonderful collection of Statistics Apps for data visualization
Unlocking the power of data a very rich resource for introductory statistics with apps.
Twenty Big Data Sources A list of some big data repositories through Data Science Central.
a href= " http://www.theguardian.com/sport/datablog/2012/aug/10/olympics-2012-list-medal-winners#height">Data Journalism Open data sets by British newspaper the guardian.
Markdown Themes Appearance and Style themes to create HTML document using R Studio.