Predicting Maximal Aerobic Capacity (VO₂max): Researchers are trying to find a way to predict fitness and health without using expensive laboratory equipment. Prior research has shown that age and gender are related to VO₂max scores. They want to know if adding weight would improve the prediction. They want to know if measuring heart-rate after a 20 minute walking test would also improve prediction.

Regression - Hierarchical, Block Enter

1.How many models were produced? 2.Are any of the slopes in the final model significantly different from zero (the sig of the F)?3.Which X variable slopes are significant (the Sig. of the t) in Model 3? Which are not significant and why did this happen?4.Did adding "Weight" add significantly to the prediction of Model 1? 5.Did adding "Heartrate" add significantly to the prediction of Model 2?6.In the last model, using the Standardized Coefficients Beta column for comparisons, which X variable will be related to the biggest change in VO₂max (Y)?7.What percentage of the variance in fitness (Y) can be accounted for if you add Weight and Heartrate to measures of Age and Gender.

+ Regression Descriptive Statistics Mean Std. Deviation N VO2max 13.620 8.4928 100 Age 31.29 8.579 100 Gender 6300 48524 100 Weight 79.139 14.9536 100 Heartrate 141.33 31.174 100 Correlations VO2max Age Gender Weight Heartrate Pearson Correlation VO2max 1.000 .245 375 .293 831 Age .245 1.000 .073 .124 .249 Gender 375 -.073 1.000 368 .351 Weight .293 .124 368 1.000 -.328 Heartrate .831 -.249 351 -.328 1.000 Sig. (1-tailed) VO2max .007 :.001 002 <.001 age .234 gender weight .000 heartrate n vo2max variables entered model removed method enter w a. dependent variable: b. all requested entered.model summary change statistics adjusted r std. error of square sig. f the estimate di d1 .434a .1i9 .1i .199 .554 .42 .459 .239 .942=".715" .599 .293 predictors: c. mama sum squares dt mean regression residual total t145.5i5 v52max d. coefficients standardized unstandardized b beta sig .091 .218 .300 .071 .020 .711 excluded collinearity partial in correlation tolerance .529 .543 .787 .794 . .702 predictors model:>