MAT 162 - Prin. of Business Statistics

1. Evaluate data.
   1. Define data and sources of data.
   2. Explain the various types of data and levels of measurement.
   3. Describe how to visualize, describe, and display categorical data.
   4. Establish how to visualize, describe, and display quantitative data.
   5. Determine shape and measures of center and spread of quantitative data.
   6. Use a five-number summary to construct a boxplot.
   7. Discuss measures of relative standing.
   8. Give examples of various sampling methods and survey designs.
   9. Compare and contrast observational studies and experiments.
   10. Describe aspects of designs of experiments to include blocking, blinding, and treatment and control groups.
2. Interpret probability.
   1. Define probability and odds in terms of likelihood, frequency, and proportion.
   2. Discuss basic probability concepts. 
   3. Explain the Law of Large Numbers.
   4. Compute probability using complement, multiplication, and addition rules.
   5. Analyze multivariate probability by means of marginal distributions.
   6. Determine conditional probability using intuitive and formal rules.
   7. Decide whether two or more events are independent.
   8. Use Bayes’ rule to compute reverse conditional probability.
3. Evaluate probability using parametric distributions.
   1. Solve problems involving expected value of random variable.
   2. Determine mean and standard deviation of a discrete probability distribution.
   3. Use a binomial, Poisson, geometric, or other discrete model to compute probability.
   4. Compute probability using a normal distribution or other continuous model.
4. Support statistical inference by using confidence intervals.
   1. Discuss sampling distributions of proportions, means, and standard deviations.
   2. Determine confidence intervals for proportions, means, and standard deviations.
   3. Construct a confidence interval for the difference of two independent means.
5. Interpret the results of hypothesis testing regarding one population parameter.
   1. Test claims about population proportions, means, and standard deviations.
   2. Evaluate claims about population proportions, means, and standard deviations using p-values.
   3. Discuss potential Type I and Type II errors of hypothesis tests.
   4. Determine the power of a hypothesis test.
6. Critique claims about parameters from two or more populations.
   1. Test claims about the difference of two independent means and proportions.
   2. Perform a matched pairs t-test.
   3. Assess claims about two population variances or standard deviations using the F-distribution.
   4. Use a chi-square distribution to conduct a goodness-of-fit test, test of homogeneity, and test of independence.
7. Evaluate linear correlation.
   1. Determine the slope, y-intercept, and correlation coefficient for least-squares linear model.
   2. Interpret the slope, y-intercept, and correlation coefficient for least-squares linear model.
   3. Test claims about the correlation coefficient, slope, and y-intercept.
   4. Construct the confidence interval for the slope and y-intercept of the least-squares line.
   5. Find predicted values and prediction intervals from the linear model.
   6. Determine residuals from the linear model.
   7. Analyze the residual plot to determine suitability of linear model.
   8. Interpret the R-squared statistic.
   9. Compute explained, unexplained, and total variation.
   10. Discuss transforming and re-expressing data.
8. Incorporate appropriate technology.
   1. Use appropriate technology to perform statistical computations and analysis.
   2. Use software to visualize and analyze data.