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Math & Physics
Statistical Analysis

 

MATH 3321 – Statistical Analysis

Course Syllabus

Spring 2010

 

Description: Concepts and methods of statistical research including simple regression and correlation, multiple regression, experimental design, analysis of variance, multiple comparisons and analysis of covariance.  Prerequisite: Math 1400, Math 3320, or CJ/PSY/SOC 2302.

Objectives: Each student will (1) Develop an understanding of the role of statistics in scientific research; (2) Develop the ability to apply appropriate statistical methods to one’s own research data.

Scope: This course builds on the fundamental concepts and tools developed in an elementary statistics course such as Math 1400, Math 3320, or CJ/PSY/SOC 2302: descriptive statistics, elementary probability, confidence intervals, and simple hypothesis testing for a population mean. The course begins with simple linear regression and correlation and analysis of bivariant data. It continues with multiple regression and the General Linear Model, exploring several case studies. Analysis of variance (ANOVA) and multiple comparisons are investigated, and various experimental designs are considered. The last phase of the course introduces analysis of covariance (ANCOVA).

Software:  The SPSS computer package will be used throughout the course.

 

Instructor:  Dr. Buxton L. Johnson, Sr.

 

Physics213_Syllabus_html_m2257b0b7.gif Present lectures that compliment the book and reinforce learning objectives; ensure that all components of the course are coordinated and sequenced according to the syllabus; provide help sessions and additional instruction as requested; ensure testing and subsequent grading is equitable and consistent; and guide students in statistical research and the subsequence preparation of the final research project.

 

Students:  You

 

Physics213_Syllabus_html_m2257b0b7.gif Study assigned sections prior to class; read and attempt to work through assigned problems prior to deadline; develop and maintain a statistical analysis reference sheet (one side of an 8.5” x 11” paper for each hour exam and both sides of one sheet for the final exam); seek addition help early--use instructor’s office hours or make appointments--use Plus Center tutors; participate in study groups within the guidelines of Kentucky Wesleyan’s rules on plagiarism; and test yourself before the exam!

 

Graded Events

 

Class

 

Physics213_Syllabus_html_m2257b0b7.gif Regular weekly homework assignments will be assigned.  Furthermore, six 1-hour exams will be administered. These exams will cover class material and homework assignments.

 

Statistical Research Project

 

Physics213_Syllabus_html_m2257b0b7.gif Each student will develop a statistical research project.  See description.  A description of the proposed statistical research project and supporting information is due by midterm, and the completed project is due during the final exam period.

 

Schedule

 

Class

 

Ch 11 – Linear Regression and Correlation

Exam 1

Ch 12 – Multiple Regression and the General Linear Model

Ch 13 – More on Multiple Regression

Exam 2

Ch 8 – Inferences about More Than Two Population Central Values

Ch 9 – Multiple Comparisons

Exam 3

Ch 14 – Analysis of Variance for Completely Randomized Designs

Ch 15 – Analysis of Variance for Standard Designs

Ch 16 – Analysis of Covariance

Exam 4

 

Detailed Class/Laboratory Schedule

Class(C)

Topic

 

Study/Homework

C1: M 1-25

Review: Collecting Data

Review: Data Description

 

Ch 2, Sec 2-5

Ch 3, Sec 2-7

C2: W 1-27

Review: Probability and Probability Distributions

 

Ch 4, Sec 9-12,14

C3: F 1-29

Review: Inferences about Population Central Values

 

Ch 5, Sec 2-8

C4: M 2-1

Introduction to Simple Linear Regression and Correlation

Handout: Linear Regression

 

Ch 11, Sec 1

C5: W 2-3

Estimating Model Parameters

 

Ch 11, Sec 2

Complete the SPSS basics tutorial

C6: F 2-5

Inferences about Regression Parameters

 

Ch 11, Sec 3

C7: M 2-8

Predicting New y Values Using Regression

 

Ch 11, Sec 4

Due: HW#1 (Rev., Ch 11, Sec 1-3)

C8: W 2-10

Examining Lack of Fit in Linear Regression

 

Ch 11, Sec 5

C9: F 2-12

Correlation

Research Study: Two Methods for Detecting E. coli

 

Ch 11, Sec 7-8

Study Data: E. coli Data

C10: M 2-15

Introduction to Multiple Regression and the General Linear Model

Handout: Multiple Regression I

Handout: Multiple Regression II

Handout Data: Subs Data

 

Ch 12, Sec 1

Due: HW#2 (Ch 11, Sec 4-5,7-8)

C11: W 2-17

The General Linear Model

Estimating Multiple Regression Coefficients

 

Ch 12, Sec 2-3

C12: F 2-19

Interferences in Multiple Regression

 

Ch 12, Sec 4

C13: M 2-22

Exam 1 (Ch 11)

 

50-minute exam

Exam Data: Road Vehicle Data

C14: W 2-24

Testing a Subset of Regression Coefficients

Forecasting Using Multiple Regression

 

Ch 12, Sec 5-6

Complete the SPSS syntax tutorial

C15: F 2-26

Research Study: Evaluation of the Performance of an Electric Drill

 

Ch 12, Sec 7,10

Study Data: Electric Drill Data

C16: M 3-1

Introduction to Building Multiple Regression Models

Selecting the Variables (Step 1)

Handout: Selecting Models

 

Ch 13, Sec 1-2

Due: HW#3 (Ch 12, Sec 1-7,10)

C17: W 3-3

Formulating the Model (Step 2)

 

Ch 13, Sec 3

C18: F 3-5

Checking Model Assumptions (Step 3)

 

Ch 13, Sec 4

C19: M 3-8

Research Study: Construction Costs for Nuclear Power Plants

 

Ch 13, Sec 5

Study Data: Nuclear P.Plant Data

C20: W 3-10

Introduction to Analysis of Variance

A Statistical Test about More Than Two Population Means: ANOVA Review

Handout: One-Way ANOVA

 

Ch 8, Sec 1-2

C21: F 3-12

The Model for Observations in a Completely Randomized Design

Checking on the AOV Conditions

An Alternative Analysis: Transformations of the Data

 

Ch 8, Sec 3-5

Due: HW#4 (Ch 13, Sec 1-5)

3-15 to 3-19

Spring Break

 

Have a good break!

C22: M 3-22

Research Study:  Effect of Timing on the Treatment of Port-Wine Stains with Lasers

 

Ch 8, Sec 7

Study Data: Port-Wine Stain Data

C23: W 3-24

Exam 2 (Ch 12-13)

 

50-minute exam

Exam Data: Road Vehicle Data

C24: F 3-26

Introduction to Multiple Comparisons

Linear Contrasts

Which Error Rate Is Controlled?

Handout: Multiple Comparisons

 

 Ch 9, Sec 1-3

C25: M 3-29

Fisher’s Least Significant Difference

Tukey’s W Procedure

Scheffé’s Method

 

Ch 9, Sec 4-5,8

Due: HW#5 (Ch 8, Sec 1-5,7)

C26: W 3-31

Research Study: Which Cuckoo Egg Sizes Are Different?

 

Handout: Cuckoos Comparisons

Handout Data: Cuckoos Data

F 4-2

Good Friday

 

Have a good holiday!

C27: M 4-5

Factorial Treatment Structure

 

Ch 14, Sec 3

Sample Data: CY No Interaction

Sample Data: CY Interaction

Due: HW#6 (Ch 9, Sec 1-5,8)

C28: W 4-7

Factorial Treatment Structure

 

Ch 14, Sec 3

Sample Data: Pesticide Data

Sample Data: Worker Data

C29: F 4-9

Research Study: Development of a Low-Fat Processed Meat

 

Ch 14, Sec 7

Study Data: Low-Fat Meat Data

C30: M 4-12

Introduction to ANOVA for Blocked Designs

Handout: ANOVA for Blocked Designs

 

Ch 15, Sec 1

C31: W 4-14

Exam 3 (Ch 8-9)

 

50-minute exam

Exam Data: Vehicle Mileage Data

C32: F 4-16

Randomized Complete Block Design

 

Ch 15, Sec 2

Example Data: Insecticide Data

Sample Data: Allergy Data

C33: M 4-19

Latin Square Design

 

Ch 15 , Sec 3

Example Data: Air Cleaners Data

Sample Data: Gas Data

Due: HW#7 (Ch 14, Sec 3,7)

C34: W 4-21

Factorial Treatment Structure in a Randomized Complete Block Design

 

Ch 15 , Sec 4

Example Data: Protein Content Data

C35: F 4-23

Research Study (Randomized Complete Block Design): Control of Leatherjackets

Research Study (Factorial Treatment Structure):  Hot Dog Taste

Research Study (Factorial Treatment Structure in a Randomized Complete Block Design):

 

Ch 15 , Sec 6

Study Data: Leatherjacket Data

 

Study Data: Hot Dog Data

 

Study Data: Tomato Variety Data

C36: M 4-26

Introduction to Analysis of Covariance

A Completely Randomized Design with One Covariant

 

Ch 16, Sec 1-2

C37: W 4-28

A Completely Randomized Design with One Covariant

 

Ch 16, Sec 2

C38: F 4-30

A Completely Randomized Design with One Covariant – Example

 

Handout: Analysis of Covariance

Handout Data: Fertilizer Data

Due: HW#8 (Ch 15, Sec 1-4,6)

C39: M 5-3

The Extrapolation Problem

Multiple Covariates and More Complicated Designs

 

 

Ch 16, Sec 3-4

C40: W 5-5

Research Study:  Evaluation of Cool-Season Grasses for Putting Greens

 

Ch 16, Sec 5

Study Data: Cool-Season Grass Data

C41: F 5-7

No class

 

Due: HW#9 (Ch 16, Sec 1-5)

T 5-11

Exam 4 (Ch 14-16)

 

1:00 pm to 3:00 pm

T 5-11

RESEARCH PROJECT DUE

 

Sample Project Report

 

ATTENDANCE – Your grade in this course is likely to be strongly influenced by your class attendance. Exam questions are designed to cover very specifically the topics as they are discussed in class. In addition, quiz questions will often appear on exams.  Six absences are allowed without affecting the student's grade.  Beyond this, each additional absence will result in a 20-point reduction in the final grade.

MAKEUP EXAMS – Students who are forced to miss an exam due to unavoidable circumstances (illness, death in the family, athletic tournament, etc.) must contact the instructor. The instructor will determine if the absence is to be excused in accordance with Kentucky Wesleyan regulations on excused absences. Students with an excused absence will be allowed to take a makeup exam. No more than one makeup exam will be approved for any student except in unusual and very well documented cases.

EXAM DISCREPANCIES – Students with exam discrepancies should record, specifically, those items they would like considered for re-evaluation and return their exam to the instructor before leaving the classroom. All other students can retain their exams. No consideration will be given to exam discrepancies submitted after the student has left the classroom.

LATE WORK – Only one late homework assignment will be accepted for credit.  Only one late laboratory report will be accepted for credit.

CHEATING – The first time a student is found cheating, copying, etc., a zero will be given on the compromised work.  The second time will result in failure of the course.

 

Grading Policy

Graded Event

Points

Exams(4)

 480

Homework Assignments

150

Statistical Research Project

150

Course Total

780

Final Grade Assignments are based on the percentage of course points earned according to the following schedule:
  • 90% and above – A
  • 80% and above – B
  • 70% and above – C
  • 60% and above – D
  • below 60% – F

 

Academic Accommodations

Kentucky Wesleyan College is committed to providing access to programs and services for qualified students with disabilities. If you are a student with a disability and require accommodations to participate and complete requirements for this class, notify the instructor immediately and contact Dr. Leah Hoover at the Office of Disability Services for verification of eligibility and determination of specific accommodations.

 

Academic Help

Several Kentucky Wesleyan offices can be helpful in aiding students.