Suppose we use a person\'s dad\'s height to predict how short or tall the person

Question

Suppose we use a person's dad's height to predict how short or tall the person will be by building a regression model to investigate if a relationship exists between the two variables. Suppose the regression results are as follows: Least Squares Linear Regression of Height Variables Coefficient Std Error T P Constant 20.28338.70520 2.33 0.0223 DadsHt 0.674990.12495 5.40 0.0002 R'0.2673 Adjusted R 0.2581Standard Deviation What conclusion would you make at o .05 when conducting a test to determine if there is a positive linear relationship between the variables? Mean Square Error (MSE) 23.9235 49000 There is sufficient evidence to indicate that dad's height is a negative linear predictor of height There is insufficient evidence to indicate that dad's height is a positive linear predictor of height There is sufficient evidence to indicate that dad's height is not a positive linear predictor of height There is sufficient evidence to indicate that dad's height is a positive linear predictor of height 5 6 7 8

Explanation / Answer

To check the significance of a variable in regression, we check the p-value for that variable significant t test. The p-value for Dad's height is given to be 0.0002 < 0.05 which is the level of significance, therefore the test is significant and we can reject the null hypothesis here and conclude that there is sufficient evidence to indicate that dad's height is a positive linear indicator for predicting height. Therefore D is the correct answer here. Note that it is positive because the coefficient is positive.

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