![]() ![]() ![]() Additionally, the task requests confidence intervals for the estimates, a collinearity analysis, and a scatter plot of the residuals. This task includes performing a linear regression analysis to predict the variable oxygen from the explanatory variables age, runtime, and runpulse. Thus, in order to predict oxygen consumption, you estimate the parameters in the following multiple linear regression equation: oxygen = b 0 + b 1age+ b 2runtime+ b 3runpulse Suppose that previous studies indicate that oxygen consumption is dependent upon the subject's age, the time it takes to run 1.5 miles, and the heart rate while running. You can choose any of the other quantitative variables ( age, weight, runtime, rstpulse, runpulse, and maxpulse) as your explanatory variables. Thus, the dependent variable for the analysis is the variable oxygen. The goal of the study is to predict fitness as measured by oxygen consumption. See " Computing Correlations" in Chapter 7, " Descriptive Statistics," for a complete description of the variables in the Fitness data set. ![]() The data set analyzed in this example is named Fitness, and it contains measurements made on three groups of men involved in a physical fitness course at North Carolina State University. Where Y is the response, or dependent, variable, the Xs represent the p explanatory variables, and the bs are the regression coefficients.įor example, suppose that you would like to model a person's aerobic fitness as measured by the ability to consume oxygen. You can write the multiple linear regression equation for a model with p explanatory variables as Y = b 0 + b 1X 1 + b 2X 2 +. You perform a multiple linear regression analysis when you have more than one explanatory variable for consideration in your model. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |