STAT 200 Learning Objectives PDF

STAT 200 Learning Objectives STAT 200 Learning Objectives 1. Explain, recognize, and cite examples of categorical and quantitative variables. 2. Interpret numerical summaries of center/location (including five-number summaries) and spread (including the empirical rule); predict how particular...

STAT 200 Learning Objectives STAT 200 Learning Objectives 1. Explain, recognize, and cite examples of categorical and quantitative variables. 2. Interpret numerical summaries of center/location (including five-number summaries) and spread (including the empirical rule); predict how particular changes in data will influence these summaries (i.e. sensitive versus resistant). 3. Derive information from univariate and bivariate graphical displays of data, including bar graphs, dot-plots, boxplots, histograms, and scatterplots. 4. Use technology to convert values to percentiles or z-scores for distributions known to be normal. 5. Identify and interpret slope, intercept, and R-squared values from simple linear regression output; calculate predicted values and residuals. Interpret T-statistic and p-value for the test of the slope coefficient in a regression. 6. Match bivariate plots and descriptions with approximate corresponding correlation coefficients; calculate correlation from R-squared and slope in regression output. 7. Produce and interpret descriptive statistics for tabular data, including conditional probabilities, risk, relative risk, and odds. 8. Perform all of the steps in a hypothesis test of association of two categorical variables from contingency table data, including formulating proper hypotheses, calculating and interpreting expected counts, using the chi-square statistic to calculate a p-value, and interpreting the p- value and chi-square contributions. 9. Recognize the difference between a population and a sample, and between a parameter and a statistic. 10. For large populations, understand that precision of estimation is a function of sample size but not population size; understand the inverse square root relationship between sample size and standard error. 11. Recognize whether a study is a randomized experiment or an observational study and explain why this has implications for inferring causation; define simple random sampling and recognize it in context in terms of making generalizations. 12. Explain, recognize, and cite examples of explanatory, response, and confounding (lurking) variables. 13. Recognize similarity among all confidence intervals; exploit this similarity to derive intervals with arbitrary confidence level for common population quantities involving proportions and means. 14. Give correct informal and formal explanations of any confidence interval; understand how factors like sample size (proportional to 1/root n), confidence level, and standard error affect the width of a confidence interval. 15. Correctly construct null and alternative hypotheses about population quantities using context of real-life situations. 16. Recognize similarity among all test statistics of the standardized score variety; exploit this similarity to derive test statistics for common tests involving proportions and means. 17. Calculate a p-value using the test statistic along with the alternative hypothesis; correctly define p-value and recognize common erroneous definition of p-value. 18. Interpret p-values and/or confidence intervals to make decisions about hypotheses; identify type-1 and type-2 error possibilities. 19. Recognize and distinguish among the various specific inference situations one may commonly encounter, including one mean/proportion, difference of two means/proportions, and paired means; apply and interpret confidence intervals and tests in these situations as appropriate. 20. Explain how to generate and use a bootstrap distribution to construct confidence intervals for means, difference in means, proportions, and difference in proportions using both the formula (statistic ± 2xSE) and percentiles. 21. Describe the process of creating and use technology to generate a randomization distribution for a given sample and null hypothesis. Use it to calculate a p-value. Connect the definition of a p-value to the motivation behind a randomization distribution.