Instructor: Prof. Lifang Hsu, RH 232, x-4374,

Office Hours


Course Description


Grading :







Links to Statistical Resources

Office Hours: W and F : 2:30 - 3:30 pm, and Th. 10:30 am - 12:30 am

Lecture: M. W. F. 12:30 pm - 1:20 pm

Course Description:
The objective of this course is to present an introduction to the methods of statistical analysis. Emphasis will be placed upon the application of well known theoretical principles to the solution of problems. Extensive investigation of the theory of probability and statistics will not be conducted. However, students are expected to be intimately familiar with the concepts presented in the text and the lectures. Evidence of this familiarity will be demonstrated by the student's analysis of problems. The course will include descriptive statistics, sampling theory, estimation, hypothesis testing, regression analysis, time series analysis, and quality assurance.

Since applied statistics utilizes the computer to perform computations, we will make extensive use of the computer for problem solving. To avoid excessive frustration you should understand in detail the Minitab-Version 11 for Windows 95 interactive statistical system which is part of the Le Moyne Computer Network

Applied Statistical Methods for Business, Economics, the Social Sciences
by Carlson, W. and Thorne.

Computer Software: Minitab for Windows.
This software is available in the computer clusters.

Participation 5%
Homework(6) 15%
3 Exams @ 20% each
Project 20%
Scale: [90 -100] A, [87 - 90) A-, [83 - 87) B+, [80 - 83) B, [77 - 80) B-, [73 - 77) C+, [70 - 73) C,
[67 - 70) C-, [60 - 67) D, Below 60 F

Cooperative learning approach will be used in this course. The class will be divided into groups of two or three students by the second class meeting. If you are not there when your group is called on, you will not get credit. Also you are responsible for all material discussed in class. Please do not come to office hours to discuss material you missed because you did not attend class. Attendance will be checked randomly 12 times during the semester. After the first two absences, each additional absence will count 1% off the total grade for the semester.

There will be three open-book and open-note exams, of length approximately 50 minutes. A great deal of material will be covered in this course. Therefore, it is recommended that each student develop a clear and concise set of reference notes that can be used as an aid to quickly applying the principles to problems. No makeup exams for whatever reason, will be allowed. <

Test dates: Test 1 - February 11, Wednesday
Test 2 - March 11, Wednesday
Test 3 - April 22, Wednesday

The reading assignment is in the syllabus. Try to keep ahead of me so that you can ask questions on the text material which confuses you.

Daily homework assignments, which refer to problems at the end of the chapter are indicated in the syllabus and/or in class. Homework will be collected every Friday for grading. Each group will turn in one assignment by each due date. Up to two late homeworks will be accepted without penalty if handed in by the following class. Homeworks handed in after this time and third (and subsequent) late home works will not be accepted. The probability of passing this course without having done the homework is low. You will learn better if you work the problems before looking at the answers. I encourage working with a partner on homework. Study in teams can help you master the material.
Note that answers for the practice problems are given in the end of the chapter, and the answers for the problem exercises are given in the back of the text. You are encouraged to attempt all of them.

There will be a term project where the class will be divided into teams. Each team will design, conduct and analyze a statistical experiment of how the theories discussed in class are put into practice.
In order to gain an appreciation of how the theory learned in this course is applied to actual problems you will design and conduct an actual statistical experiment. A statistical experiment can be broken into three main stages. They are as follows.
Stage I -- Planning
This is where most of the actual thought is done. Here it is determined what sort of data is to be collected and how it is to be collected. Here you must consider what specific analyses will be done in order to assure that data gathered are suitable for what you intend to do. The most counterproductive thing that can be done is to take a sample and then try to determine what to do with it. The answer is usually that the data are worthless. As part of the planning phase it must be determined what tests and estimates are appropriate to address the goal of the experiment. The specific form of the statistics used for testing and estimation usually depend on the distributional characteristics of the data. However, at this point you should determine precisely what population quantities you wish to estimate and what hypotheses you wish to test. If questionnaries are involved they should be designed and written in this stage. If quantitative measurements are to be taken you should know exactly what is to be measured and how the measurements will be taken. What equipment will be used? The other critical issue that must be addressed is that of sampling procedure. It is not easy thing to collect a truly random sample from a target population, but all statistical procedures that the data are a random sample. All population elements must have an equal chance of being included in the sample. For example, standing outside the Dolphin Den on Wednesday afternoon and selecting every tenth person entering will not give a random sample of Le Moyne students. Using random number tables and the Le Moyne College Directory is much better as long as the target population is the full-time students.
When this stage is completed you should know what it is that are going to do. All that is needed is some data to work with.

Your team should meet with me at least once to discuss your ideas on the project during this stage. A written draft of the plan for your experiment is to be completed to my satistification on or before March 27, 1998.

Stage II -- Data Collection
This phase is exactly what you might think. The sampling and measurement procedure that was so carefully designed in the planning stage is actually conducted.

Your team should meet with me at least once during this stage and the data should be reviewed by me on or before April 15,1998.

Stage III -- Data Analysis
During this stage the estimates are actually computed and hypotheses are tested. A project report on the data analysis is written. Your team is expected to need guidance during this stage. You should meet with me as a team as often as possible to finish the project on time. The due day for the final report is April 29, 1998. On May 1 and May 4, each team will give an oral presentation of their project to the class.

Syllabus: MTH 313, Section 01

Date Topics ReadingsHomework
1/21 WIntroduction Chapter 1 and Minitab Reference Chapter Problems. 1.1, 1.2
1/23 FData Models pg 12-35,38-44,47-54Chap Prob. 2.2, 9, 17, 23
1/26 M Data Models pg 36-38,45, 55-63Prob. 2.16, 25, 31, 43
1/28 W Descriptive Relationships Chapter 3 Prob. 3.5, 7, 9, 10, 22
1/30 FReview Chapter 1-3
2/2 MSampling Chapter 8Prob. 8.9, 13, 16, 21
2/4 WReview for Exam I
2/6 F Exam I
2/9 M Sampling DistributionsChapter 9.1-9.5 Prob. 9.3, 9, 15, 19
2/11 WSampling Distributions 9.6-9.7 Prob. 9.23, 32, 25, 37, 29
2/13 F Sampling Distributions9.8-9.10Prob. 32, 35, 37, 39, 44
2/16 MNo Class(Long Weekend)
2/18 WEstimation 10.1-10.3 Prob. 10.3, 5, 9, 10
2/20 FEstimation 10.4-10.6Prob. 10.11, 14, 17, 19, 20
2/23 M Estimation 10.7-10.12 Prob. 10.26, 33, 39, 43, 45
2/25 WHypothesis Testing 11.1- 11.4 Prob. 11.4, 5, 7, 9, 11
2/27 F Hypothesis Testing11.5 - 11.7 Prob. 11.13, 15, 19, 20, 23
3/2 MHypothesis Testing 11.8 - 11.9 Prob. 11.27, 31, 36, 44, 45
3/4 WHypothesis Testing11.10 Prob. 11.37, 40, 41, 43, 47
3/6 FChi Square TestsChapter 12Prob. 12.5, 9, 19, 21
3/9 M Review for Exam II
3/11 W Exam II
3/13 F Simple Regression14.1 - 14.3 Prob. 14.2, 3, 5, 8
3/16 MNo Class (Spring Break)
3/18 WNo Class (SpringBreak)
3/20 FNo Class(SpringBreak)
3/23 MSimple Regression 14.4 - 14.5 Prob. 14.11, 15, 17
3/25 WSimple Regression 14.6 - 14.7Prob. 14.19, 21, 23, 24
3/27 FMultiple Regression15.1 - 15.4 Prob. 15.3, 6, 7, 11
3/30 MMultiple Regression15.5, 7, 8 Prob. 15. 9, 13, 15, 17
4/1 WTransformation & Dummy Variables; 15.6, 16.1 16.5 Prob. 15.29, 16.5, 10, 15, 16
4/3 FSerial Correlation16.6. 16.7 Prob. 16.18, 20, 21, 23
4/6 M Time Series & Forecasting17.1 - 17.4
4/8 WTime Series & Forecasting17.5-17.6
4/10 FNo Class
4/13 MTime Series & Forecasting 17.7
4/15 WQuality Control 18.1, 18.5Prob.18.2, 5
4/17 FQuality Control18.6 - 18.8 Prob.18.8, 12, 13, 17, 18
4/20 M Review for Exam III
4/22 W Exam III
4/24 FSpecial Topic: Analysis Of Variance Handout
4/27 MSpecial Topic: Regression Using Binary Dependent Variables Handout
4/29 WSpecial Topic: Logistic RegressionHandout
5/1 FProject Presentation
5/4 MProject Presentation

This syllabus should be considered as a guide only. The actual pace of the class may vary.

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