To describe the properties of central tendency, variation and shape in numerical data To calculate descriptive summary measures for a population To construct and interpret a box-and-whisker plot To describe the covariance and coefficient of correlation as a measure of the strength of the relationship between two numerical variables
Formulate null (H0) and alternative hypotheses (H1) for applications involving one sample population mean or proportion Formulate a decision rule for testing a hypothesis Know how to use the critical value and p-value approaches to test the null hypothesis (for both mean and proportion problems) Know what Type I and Type II errors are
Test hypotheses for the difference between two independent population means (standard deviations known or unknown) Test two means from related samples for the mean difference Complete a Z test for the difference between two proportions Use the F table to find critical F values Complete an F test for the difference between two variances
Recognize situations in which to use analysis of variance Understand different analysis of variance designs Perform a single-factor hypothesis test and interpret results Conduct and interpret post-hoc multiple comparisons procedures Analyze two-factor analysis of variance tests
To use regression analysis to predict the value of a dependent variable based on an independent variable The meaning of the regression coefficients bo and b . To evaluate the assumptions of regression analysis and know what to do if the assumptions are violated . To make inferences about the slope and correlation coefficient . To estimate mean values and predict individual values
