Overall Learning Outcomes:

1. Determine the aspects of a question, if any, for which statistics can provide relevant information.
2. Analyze statistical studies, particularly regarding appropriate sampling and experimental design.
3. Select and use appropriate statistical analyses to obtain useful information from data.
4. Communicate knowledge using standard statistical language and also interpret it in non-technical language.

1. Interpret ideas of population versus sample, random variables, and techniques of descriptive statistics including frequency distributions, histograms, stem and leaf plots, boxplots, and scatterplots.
2. Calculate and interpret measures of central tendency and dispersion, including mean, median, standard deviation, and quartiles.
3. Apply the 68-95-99.7 rule to normal distributions and use the normal tables to answer questions about the proportion of scores in a certain range or find various percentiles.
4. Analyze relationships between two quantitative variables using correlation and linear regression. Analyze residual plots and determine how to handle outliers and influential points.
5. Analyze data presented in two-way and three-way tables to provide information about relationships between categorical variables, including understanding and interpreting situations to which Simpson's Paradox applies.
6. Apply ideas of appropriate sampling techniques and experimental design to data production.
7. Use the basic ideas of probability and apply them to statistics.
8. Use the sampling distributions of sample proportions and sample means to answer appropriate questions.
9. Estimate single means, difference of two means, single proportions and difference of two proportions using confidence intervals. Interpret the results.
10. Demonstrate skills in hypothesis testing for means and proportions, for single populations and comparison of two populations.
11. Demonstrate skills in hypothesis testing using the chi-squared test to compare several proportions and to test independence.
12. Demonstrate skills in inference for regression or ANOVA techniques.

Throughout the course, students will learn to do almost all the calculations by hand with a scientific calculator on small data sets and also students will learn to use a substantial statistical computer software package to do the statistical calculations quickly and on larger data sets. Some of the more advanced regression analyses will not be done by hand.

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Last updated August 14, 1999. Comments, questions, suggestions?