341

Instructor: Dr. Kelly Cline E-mail: kcline@carroll.edu Office: SH 119 Office phone: 447-4451

Course Description:
A calculus-based course in applied probability and statistics. Many types of univariate and multivariate probability distributions are derived and applications are studied. Exposes the student to various decision-making techniques when working with statistical information. Prerequisite: MA 334 or consent of instructor.

Textbook:
Applied Statistics and Probability for Engineers Third Edition by Montgomery and Runger.

Grading:
Homework and In Class Work: 30% Term Project: 20% Exam 1: 15% Exam 2: 15% Final Exam: 20% There will be three exams in this class, a midterm on Friday, September 27, a midterm on Wednesday, November 6, and a final at the end of the term. In order to pass this class, all students must take all three exams and complete the term project. I will use a no-curve grading policy to assign final grades: above 90% = A, 80% - 89% = B, 70% - 79% = C, 60% - 69% = D, below 60% = F.

Course Material:
This course will be broken into three units, sepearated by midterms. In Unit 1, we will review the probability that you studied in 334 and build upon this, studying joint probability distributions (Chapter 5). In Unit 2 we will study the use of samples to learn about larger populations, estimating parameters (Chapter 7) and putting confidence intervals on our estimations (Chapter 8). In Unit 3 we will learn how to use samples to test hypotheses (Chapter 9), and methods for dealing with two populations (Chapter 10, 11).

Homework:
There will be homework due at the beginning of almost every class. I will not accept any late homework unless you make some arragement with me before the class period when the homework is due. I will drop your two lowest homework scores, so you can miss two homeworks without any penalty.

In Class:
I will assign a section of the text for you to read before each class. Rather than lecturing, I prefer to ask you questions about what we've read, and guide a class discussion about the material, so if you haven't done the reading it is very obvious! There will also be many short assignments that you will do in small groups during class.

Projects:
In this course you will do a term project in applied statistics in which you will ask a question, gather a sample of data, use the methods of this course to analyze your data, and draw some conclusion. First you will write up a project proposal (about one page) in which you will propose a question you would like to answer, and the methods you will use to gather data in order to answer this question (Due Monday, September 8). After your proposal has been accepted, you will conduct the study, write up your project in a formal paper (Due Monday, October 27) and make a brief presentation to the class describing your study and the results (Wednesday, October 29 and Friday, October 31) . You will then read several of the papers written by your peers and you will write an evaluation paper (Due Monday, December 8) in which you will discuss the strengths and weaknesses of each project, ranking them (1, 2, 3 $\cdots$ starting with the best paper). Finally, you will meet as committees (Wednesday, December 10), discuss any differences in your rankings, and vote on a joint committee ranking.

Introduction and Review Problems		M 8/25
Review Problems		W 8/27
Review: Discrete Random Variables and the Binomail Distribution	Chapt 3.1-3.6	F 8/29
Geometric, Negative Binomial, Hypergeometric, and Poisson Distributions	3.7-3.9	W 9/3
Review: Continuous Random Variables and the Normal Distribution	4.1-4.6	F 9/5
Normal Approximations and the Exponential Distribution	4.7-4.9	M 9/8
Erlang, Gamma, Weibull, and Lognormal Distributions	4.10-4.12	W 9/10
Random Sampling Activity		F 9/12
Multiple Discrete Random Variables	5.1-5.2	M 9/15
Two Continuous Random Variables	5.3	W 9/17
Coviariance and Correlation	5.4-5.5	F 9/19
Bivariate Normal Distributions and Linear Combinations	5.6-5.7	M 9/22
Review	Chaps 3-5	W 9/24
Midterm Examination #1		F 9/26
Data Summary, Random Sampling, Stem-and-Leaf Diagrams	6-1, 6-2, 6-3, 6-4	M 9/29
Time Sequence Plots and Probability Plots	6-5, 6-6, 6-7	W 10/1
Activity		F 10/3
General Concepts of Point Estimation	7-1, 7-2	M 10/13
Methods of Point Estimation of Parameters	7-3	W 10/15
Sampling Distributions and Sampling Distribution of Means	7-4, 7-5	F 10/17
Confidence Interval on the Mean of a Normal Distribution	8-1, 8-2	M 10/20
Confidence on the Variance and Standard Deviation of a Normal Distribution	8-3, 8-4	W 10/22
Large Sample Confidence Interval on a Population Proportion	8-5	F 10/24
Prediction Inverval for Future Observations and Tolerance Intervals	8-6, 8-7	M 10/27
Project Presentations		W 10/29
Project Presentations		F 10/31
Review	Chapts 6-8	M 11/3
Midterm Examination #2		W 11/5
Hypothesis Testing	9-1	F 11/7
Tests on the Mean of a Normal Distribution	9-2	M 11/10
Hypothesis Tests on the Variance of a Normal Distribution	9-3, 9-4	W 11/12
Tests on a Population Proportion	9-5, 9-6, 9-7, 9-8	F 11/14
Testing for a Difference in the Means of Two Normal Distributions	10-1, 10-2	M 11/17
The Paired -Test	10-3, 10-4	W 11/19
Inference on the Variance of Two Normal Distributions	10-5	F 11/21
Inference on Two Population Proportions	10-6, 10-7	M 11/24
Simple Linear Regression and Least Squares Estimators	11-1, 11-2, 11-3	M 12/1
Hypothesis Tests in Linear Regression	11-5	W 12/3
Confidence Intervals in Linear Regression	11-6, 11-7	F 12/5
Adequacy of the Regression Model	11-8, 11-9, 11-11	M 12/8
Project Evaluation Meetings		W 12/10
Review	Chapts 2-11	F 12/12
Final Examination	10:00 - 11:45 a.m.	R 12/18