John Spurrier

Department:Department of Statistics

University of South Carolina, Columbia

Project Team:

John Spurrier, Principal Investigator

Don Edwards, Faculty Associate

Lori Thombs, Faculty Associate

Laura Gooding, Graduate Assistant

Advisory Committee:

Scott Goode, Chemistry, University of South Carolina

Bob Hogg, Statistics, University of Iowa

Mary Ellen O'Leary, Mathematics, University of South Carolina

Dick Scheaffer, Statistics, University of Florida

Students in elementary statistics traditionally see experiments and data merely as words and numbers in a text. They plug numbers into formulas and make conclusions about briefly described experiments. They receive little or no exposure to the important statistical activities of sample selection, data collection, experimental design, randomization, development of statistical models, etc. In short, they leave the first course without a firm understanding of the role of applied statistics in scientific investigations. It is proposed to establish a prototype elementary statistics lab and to create a one semester hour lab course to be taken with or after the completion of the traditional freshman-sophomore level elementary statistics course. The lab will guide the student through simple, but meaningful experiments which illustrate important points of applied statistics. In each session the student will discuss and perform an experiment, collect and analyze data, and write a report. The lab will differ from traditional science labs in that the emphasis will be on statistical concepts. Students in the lab will be compared with a control group with regard to performance in elementary statistics and propensity to enroll in additional statistics courses. Student and teacher's manuals will be prepared so that the course can be used at other colleges and universities.

1. Introduction to Macintosh and Minitab

(Statistical
Computing)

2. Measurement of Pulse Rate

(Descriptive Statistics and
Variability)

3. Parking Lot Sampling

(Construction of Frame and
Random Sampling)

Begin Plant Experiment

(Randomization and Designed
Experiments)

4. Real and Perceived Distances

(Scatterplots and Variation)

5. Traffic Counts

(Time Series)

6. Coke Versus Ritz Taste Test

(Proportion, Binomial Distribution, Paired Comparison)

7. Variation in Carpet Tacks

(Variation, Quality
Improvement)

8. Sampling Distribution of Sample Mean and Median

(Simulation, Estimation, Central Limit Theorem)

9. Absorbency of Paper Towels

(Sampling, Confidence
Interval for Mean)

10. Breaking Strength of String and Fishing Line

(Confidence Intervals, Hypothesis Testing)

11. Airplane Flight Distance

(2 Factor Design, Selection
of Factors)

12. Normal Walking Versus Exaggerated Arm Movement

(Dependent Sample Comparison of Means)

13. Conclusion of Plant Experiment

(2 Factor Experiment,
Model Building)

14. Prediction of Hickory Nut Weight

(Regression,
Correlation, Plotting)

Generating enrollment among the target audience.

Knowing how much writing to require.

Giving the students guidance about what a written report should look like.

Consistent grading of written reports.

Dealing with late arrivals to lab.

Dealing with missed lab.

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