1. Explain how you can determine how well the model fits the data (i.e., explain the...

  

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1. Explain how you can determine how well the model fits the data (i.e., explain the meaning of Coefficient of Determination in this Table) 2. Explain how you can test the validity of the model (i.e., explain how you will look up F-table to get the critical value of F at the significance level, 0.05 and determine the validity). 3. Explain how you can test the individual coefficients. (i.e., explain how you will look up t-table to get the critical value of t at the significance level, 0.01 and determine the statistical significance of individual variables). 4. Draw a conclusion of this model (i.e., interpret the results). Model 1 Model 1 R .760a Model 1 Regression Residual Model Summary .559 a. Predictors: (Constant), gender, age, heart_rate, weight R Square .577 (Constant) Adjusted R Square Sum of Squares 4196.483 3076.778 7273.261 ANOVA 87.830 age -.165 weight -.385 heart rate -.118 gender 13.208 a. Dependent Variable: VO2max df 4 95 Total a. Dependent Variable: VO2max b. Predictors: (Constant), gender, age, heart_rate, weight 99 Unstandardized Coefficients B Std. Error Std. Error of the Estimate 5.69097 6.385 .063 .043 .032 1.344 Mean Square 1049.121 32.387 Coefficients Standardized Coefficients Beta -.176 -.677 -.252 .748 F 32.393 13.756 -2.633 -8.877 -3.667 9.824 Sig. Sig. .000b .000 .010 .000 .000 .000 95.0% Confidence Interval for B Lower Bound Upper Bound 75.155 100.506 -.290 -.041 -.471 -.299 -.182 -.054 10.539 15.877 1. Explain how you can determine how well the model fits the data (i.e., explain the meaning of Coefficient of Determination in this Table) 2. Explain how you can test the validity of the model (i.e., explain how you will look up F-table to get the critical value of F at the significance level, 0.05 and determine the validity). 3. Explain how you can test the individual coefficients. (i.e., explain how you will look up t-table to get the critical value of t at the significance level, 0.01 and determine the statistical significance of individual variables). 4. Draw a conclusion of this model (i.e., interpret the results). Model 1 Model 1 R .760a Model 1 Regression Residual Model Summary .559 a. Predictors: (Constant), gender, age, heart_rate, weight R Square .577 (Constant) Adjusted R Square Sum of Squares 4196.483 3076.778 7273.261 ANOVA 87.830 age -.165 weight -.385 heart rate -.118 gender 13.208 a. Dependent Variable: VO2max df 4 95 Total a. Dependent Variable: VO2max b. Predictors: (Constant), gender, age, heart_rate, weight 99 Unstandardized Coefficients B Std. Error Std. Error of the Estimate 5.69097 6.385 .063 .043 .032 1.344 Mean Square 1049.121 32.387 Coefficients Standardized Coefficients Beta -.176 -.677 -.252 .748 F 32.393 13.756 -2.633 -8.877 -3.667 9.824 Sig. Sig. .000b .000 .010 .000 .000 .000 95.0% Confidence Interval for B Lower Bound Upper Bound 75.155 100.506 -.290 -.041 -.471 -.299 -.182 -.054 10.539 15.877

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1 The Coefficient of Determination denoted as R2 provides a measure of how well the model fits the data It represents the proportion of the variance i... View the full answer