Higher Education

# Practice of Statistics: Putting the Pieces Together, 1st Edition

• John D. Spurrier University of South Carolina
• ISBN-10: 053436490X  |  ISBN-13: 9780534364908
• 216 Pages
• College Bookstore Wholesale Price = \$125.50

### Overview

Capstone courses in statistics teach students how to apply their learned skills as if they were professional statisticians. It enables them to tie together ideas and methods from their undergraduate course work to solve problems. Students are presnted with a series of 'experiences.' They are required to work in teams to collect data, then individually to solve the problem and present written and oral reports. The 'experiences' expose students to additional challenges they might encounter on the job.

PART I: CAPSTONE EXPERIENCES
1. PREPARING DATA FOR ANALYSIS
Computing Concepts and Procedures: Coding Protocol, Data Editing, Data Entry, SAS PROC COMPARE / Mathematical Concepts: None / Statistical Concepts: Five-Number Summary, Frequency Table, Scatter Plot, Systematic Random Sample / Materials Required: None
2. DESIGNING A TELEPHONE SURVEY
Computing Concepts and Procedures: Data Entry Computer Screen / Mathematical Concepts: None / Statistical Concepts: Data Recording Form, Survey Instrument / Materials Required: None
3. DETERMINING THE SAMPLE SIZE
Computing Concepts and Procedures: Noncentral t Distribution Function, Numerical Search, SAS Function GAMMA, SAS Function PROBT, SAS Function TINV, Student''s t Distribution Probability Point / Mathematical Concepts: Gamma Function, Nonlinear Inequality / Statistical Concepts: Confidence Interval, Expected Value, Function of a Random Variable, Hypothesis Test, Power of a Test, Non-Central t Distribution, Student''s t Distribution / Materials Needed: None
4. DESIGNING AN EXPERIMENT TO COMPARE TWO HORN ACTIVATION BUTTONS
Computing Concepts and Procedures: Random Number Generation, SAS Function PROBT, Two-Dimensional Plot / Mathematical Concepts: Derivative, Inverse Function / Statistical Concepts: Change of Variable, Density Function, Dependent Sample Design, Independent Sample Design, Normality Assumption, Power of a Test, Comparing Two Treatments / Materials Required: None
5. USING REGRESSION TO PREDICT THE WEIGHT OF ROCKS
Computing Concepts and Procedures: SAS PROC PLOT, SAS PROC PRINT, SAS PROC REG, Two-Dimensional Plot / Mathematical Concepts: Ellipsoid, Rectangular Solid / Statistical Concepts: Regression, Residual, R-Square, Scatter Plot / Materials Required: For each team, a caliper capable of measuring to the nearest 0.01 inch, a scale capable of measuring to the nearest gram, 20 rocks of various sizes but of similar composition, and muffin pans with holes numbered from 1 to 20. The rocks must have dimensions and weights within caliper and scale capacities
6. ESTIMATING VARIANCE COMPONENTS IN TACK MEASUREMENTS
Computing Concepts and Procedures: SAS PROC VARCOMP / Mathematical Concepts: System of Linear Equations / Statistical Concepts: Analysis of Variance, Factorial Design, Nested Design, Random Effect, Replicate, Unbiased Estimator, Variance Component / Materials Required: For each team, 4 nominal ½-inch carpet tacks, 3 micrometers capable of measuring to .001 inch, 2 objects with a premeasured dimension of less than 1 inch, 12-inch length of masking tape
7. CLASSIFYING PLANT LEAVES
Computing Concepts and Procedures: SAS PROC DISCRIM, Two-Dimensional Plot / Mathematical Concepts: Linear Inequality, Matrix Operations / Statistical Concepts: Bivariate Normal Distribution, Classification, Density Function, Discriminant Analysis, Scatter Plot, Unbiased Estimator / Materials Required: For each team, a ruler marked in millimeters
8. USING A RESPONSE SURFACE TO OPTIMIZE PRODUCT PERFORMANCE
Computing Concepts and Procedures: SAS PROC GLM, SAS PROC REG, Three-Dimensional Plot / Mathematical Concepts: Maximization, Quadratic Surface, System of Linear Equations / Statistical Concepts: Experimental Design, Factor Selection, Multiple Regression, Randomization, Response Surface / Materials Required: For each team, a balsa wood airplane with moveable wings, a ruler, 4 paper clips, and a 50-foot measuring tape
9. MODELING BREAKING STRENGTH WITH DICHOTOMOUS DATA
Computing Concepts and Procedures: Programming Newton''s Method, SAS PROC LOGISTIC / Mathematical Concepts: Maximization, Newton''s Method, Partial Derivative, System of Non-Linear Equations / Statistical Concepts: Bernoulli Trial, Dichotomous Data, Goodness of Fit, Likelihood Function, Likelihood Ratio Test, Logistic Distribution, Logistic Regression, Maximum Likelihood Estimation, Scatter Plot / Materials Required: For each team, 99 two-ply facial tissues with a minimum dimension of at least 8 inches, two 7-inch embroidery hoops, three full 12-ounce soft drink cans, a ruler marked in centimeters, and a 1-ounce egg-shaped fishing weight
10. ESTIMATING VOTER PREFERENCES
Computing Concepts and Procedures: Random Number Generation, SAS PROC SORT / Mathematical Concepts: Minimization Subject to an Equality Constraint / Statistical Concepts: Population Proportion, Sample Size Allocation, Sampling Error, Simple Random Sample, Stratified Random Sample, Standard Error / Materials Required: None
11. ESTIMATING THE PROBABILITY OF A HIT IN BASEBALL
Computing Concepts and Procedures: Two-Dimensional Plot / Mathematical Concepts: Integration, Non-Linear Inequality / Statistical Concepts: Bias, Bayesian Estimation, Bernoulli Trial, Binomial Distribution, Beta Distribution, Maximum Likelihood Estimation, Mean Squared Error, Method of Moments, Minimum Variance Unbiased Estimation, Squared Error Loss / Materials Required: None
PART II: SHARPENING NON-STATISTICAL SKILLS
12. STRATEGIES FOR EFFECTIVE WRITTEN REPORTS
13. STRATEGIES FOR EFFECTIVE ORAL PRESENTATIONS
14. PRODUCING VISUAL AIDS WITH POWERPOINT
15. STRATEGIES FOR EFFECTIVE CONSULTING
16. STRATEGIES FOR FINDING A JOB
INDEX

## Efficacy and Outcomes

### Reviews

The fast-paced, straight-forward approach is excellent for a book of this nature.

— Thomas Barker, Rochester Institute of Technology

## Meet the Author

### John D. Spurrier

After Graduating from high school, John D. Spurrier attended the Columbia campus of the University of Missouri. He received a bachelor's degree in mathematics and a master's degree and a doctorate in statistics from that school. Dr. Spurrier joined the faculty of the University of South Carolina in 1974 and currently holds the rank of Professor in the Department of Statistics. While on the faculty, he has served terms as department chair, assistant department chair, graduate director, and director of the departments statistical consulting service. Dr. Spurrier has taught a wide variety of undergraduate and graduate courses. He has published over 70 papers on the applications and theory of statistics and co-authored a book of laboratory experiences in elementary statistics. He has served as an officer of the Section of Physical and Engineering Sciences and the South Carolina Chapter of the American Statistical Association. Dr. Spurrier is a Fellow of the American Statistical Association and a recipient of the University of South Carolina's Michael J. Mungo Teaching Award.