You conduct a study to determine whether there is a relationship between how old a person is when they divorce and the amount of time before he or she remarries. Some computer output from the analysis and a residual plot of the data are presented below. (Values are in years).Regression Analysis The regression equation is Time = 0.03 + 0.124 Age Predictor Constant Age Coef 0.034 0.123780.04963 2.49 Stdev 1.962 t-ratio 0.02 0.987 0.037 s= 1.659 R-sq=43.7% R-sq(adj)=36.7% 4.0+ i 2.0+ s 0.0+ 2.0+ X --+Age 24 32 40 48 56What is the predicted number of years a 40 year old will wait before remarrying? Given that the study found that the average 40 year old waits 4 years before remarrying, what is the value of the residual for a 40 year old?
Based on the printout and the residual plot, does it appear that time and age have a linear relationship? If so, how strong does the predictive relationship appear to be?

Complete Question

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The rest of the question

What is the predicted number of years a 40 year old will wait before remarrying?

Given that the study found that the average 40 year old waits 4 years before remarrying, what is the value of the residual for a 40 year old?

Based on the printout and the residual plot, does it appear that time and age have a linear relationship? If so, how strong does the predictive relationship appear to be?

 a

  The predicted number of years a 40 year old will wait before remarrying is    

  b

  The value of the residual for a 40 year old  is  

c

   Based on the print out , we can say that the time and the age has a linear relationship

  And looking the R-sq we can say the 43.7% of time variation is due to age

Step-by-step explanation:

   From the regression equation

And from the question we are told that Age is  40 years so

Therefore the predicted time 40 year old will wait for 4.99 years before remarrying

    From the question,the study found that the average 40 year old waits 4 years before remarrying,

     So the residual for a 40 year old is  mathematically evaluated as

Based on the print out , we can say that the time and the age has a linear relationship

  And looking the R-sq we can say the 43.7% of time variation is due to age

40 x 50 = 2000

step-by-step explanation:

investment b is an ordinary annuity with $3,909.64 in the account after one year.

step-by-step explanation: