MAT 158 - Statistics II

Unit Objectives:
Outcome 1:    Construct confidence intervals.
Task 1:    Construct a normal probability plot with the aid of Table III.
Task 2:    Identify distribution shapes from normal probability plots.
Task 3:    Detect outliers from normal probability plots.
Task 4:    Construct and interpret confidence intervals for the means of two normal populations using independent samples when the population standard deviations are unknown but assumed equal.
Task 5:    Construct and interpret confidence intervals for the means of two normal populations using independent samples when the population standard deviations are unknown and assumed unequal.
Task 6:    Construct and interpret confidence intervals to compare the means of two populations using paired samples when the population of paired differences is normally distributed.
Task 7:    Calculate sample proportions.
Task 8:    Construct and interpret confidence intervals for one population proportions.
Task 9:    Calculate the margin of error in estimating a population proportion by a sample proportion.
Task 10:    Compute the sample size required to meet the specifications of the margin of error and confidence level of a confidence interval for population proportion.
Task 11:    Construct and interpret confidence intervals for the difference between two population proportions using independent samples.
Task 12:    Calculate the margin of error in estimating the difference between two population proportions.
Task 13:    Compute the sample size required to meet the specifications of the margin of error and confidence level of a confidence interval for the difference in population proportions.

Outcome 2:    Evaluate hypothesis tests.
Task 1:    Define and apply the concepts of Type I and Type II errors.
Task 2:    Compute Type II error probabilities.
Task 3:    Calculate the power of a hypothesis test.
Task 4:    Draw a power curve.
Task 5:    Determine the p-value of a hypothesis test.
Task 6:    State and apply the steps for performing a hypothesis test using the p-value approach to hypothesis testing.
Task 7:    Perform a hypothesis test for a population mean when the population being sampled has a symmetric distribution.
Task 8:    Perform a hypothesis test for the means of two normal populations using independent samples when the population standard deviations are unknown but assumed equal.
Task 9:    Perform a hypothesis test for the means of two normal populations using independent samples when the population standard deviations are assumed unequal.
Task 10:    Perform a hypothesis test to compare the means of two populations using independent samples when the populations have the same shape.
Task 11:    Perform a hypothesis test to compare the means of two populations using paired samples when the population of paired differences is normally distributed.
Task 12:    Perform a hypothesis test to compare the means of two populations using paired samples when the population of paired differences has a symmetric distribution.
Task 13:    Determine which procedure to use to compare the means of two populations.
Task 14:    Perform hypothesis tests for one population proportions.
Task 15:    Perform hypothesis tests for two population proportions using independent samples.

Outcome 3:    Apply chi-squared procedures.
Task 1:    Perform the chi-square goodness-of-fit test.
Task 2:    Determine chi-square values from the table.
Task 3:    Perform the chi-square independence test.
Task 4:    Perform the chi-square test for population standard deviation.
Task 5:    Construct and interpret a confidence interval for population standard deviation

Outcome 4:    Apply analysis of variance.
Task 1:    Determine F-values from the table.
Task 2:    Perform the one-way analysis of variance test.
Task 3:    Perform the Tukey multiple-comparison method.
Task 4:    Perform the Kruskal-Wallis test.

Outcome 5:    Apply inferential methods in regression and correlation.
Task 1:    Explain what it means for a set of data to satisfy the assumptions for the regression model.
Task 2:    Calculate the standard error estimate.
Task 3:    Calculate an estimate for the mean of the population of y-values that correspond to a particular x-value.
Task 4:    Perform a residual analysis.
Task 5:    Perform prediction for an individual y-value corresponding to a particular x-value.
Task 6:    Perform inferences for the slope of the population regression line.
Task 7:    Perform inferences in correlation.