Elementary Statistics Laboratory Course Development

John Spurrier

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

Project Summary:

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.


The class sessions are held in the Department's Measurement and Analysis Laboratory. This large room contains space for data collection and space for computation. Data analysis is done in Minitab on 12 Macintosh Classics. This hardware and software was purchased with an NSF ILI grant. Data is collected using a variety of relatively inexpensive measurement equipment (micrometers, calipers, force gauges, measuring tapes, scales, etc.) purchased with a grant from the University's Instructional Innovation Program.

Description of Labs:

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)

Major Difficulty:

Generating enrollment among the target audience.

Minor Difficulties:

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.

Return to Cobb Paper | Return to Table of Contents | Return to the JSE Home Page