Learning Outcomes Relationships

PLO 2: Utilize the industry common tools to mine large data sets for relationships and other insights.

CLO 5: Construct and interpret a confidence interval for a population parameter (such as proportion, mean, difference in proportions and difference in means) by using percentiles of a bootstrap distribution.
CLO 6: Use a randomization distribution for a population proportion, mean, difference of proportions, and difference of means to perform the corresponding hypothesis test for a given sample and null hypothesis.
CLO 7: Use the normal distribution to interpret z-scores and compute probabilities.
CLO 10: Use either a normal or t-distribution, as appropriate, to obtain a margin of error and construct the corresponding confidence interval. Interpret the results.
CLO 11: Perform a full hypothesis test for a population proportion and mean using either a normal or t-distribution, as appropriate. This process should include: formulating a null and alternative hypothesis from a given claim, choosing a test statistic, describing the rejection criteria, making a decision using a p-value or critical value, drawing an appropriate conclusion, and describing Type I and Type II errors.

PLO 3: Utilize visualization techniques to discover useful information within large data sets and communicate them to an appropriate audience.

CLO 3: Summarize bivariate data using graphical and numerical summaries including scatter plots, correlation coefficients and least squares regression lines.

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