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How to calculate this question ? 2.15. The following data give the monthly machi

ID: 3355746 • Letter: H

Question

How to calculate this question ?

2.15. The following data give the monthly machine maintenance cost (y) in hundreds of dollars and the number of machine hours per month (x), taken over the last 7 months. Cost (y Hours (x) 26 25 20 18 30 40 110 98 121 116 90 84 a. Fit a linear regression. Construct the ANOVA table. Find R and test the hypothesis that A =0 using the F ratio. b. Obtain the standard errors of and A . Using the t distribution, test the hypothesis: (i) A) = 0; (ii) A = 0. Construct a 99% confidence interval for Bi c. Find the fitted value at x = 100 and estimate its standard error. Calculate the 95% confidence interval for Ao + 00. d. Repeat (c), only this time take x = 84. Explain the change in the interval length.

Explanation / Answer

a)

The linear regression equation of y on x is estimated as,

Y = 68.4459 + -0.4104x.

R-squared = 0.6767. The ANOVA table of the regression analysis is given below:

Source       Df   Sum Sq   Mean Sq   F value    Pr(>F)

x                   1    217.9     217.90       10.47     0.0231 *

Residuals    5   104.1       20.82                

From the above test it is evident that the regression of y on x is significant at 5% level of significance and hence beta_1 (coefficient of x) is significant.

b) The estimates and the standard errors are given below. Also the t-statistic (estimate/s.e.) and the corresponding p-value are also listed in the following table:

Coefficients       Estimate      Std. Error     t value      Pr(>|t|)  

Beta_0                 68.4459        12.9270    5.295       0.00321 **

Beta_1                -0.4104          0.1268    -3.235       0.02308 *

Hence the hypothesis beta_0 = 0 is rejected at 1% level of significance and the hypothesis beta_1=0 is significant at 5% level of significance.

The 99% confidence interval for beta_1 is given by,

[ -0.4104 – 3.25*0.1268 / sqrt(10), -0.4104 + 3.25*0.1268 / sqrt(10) ] = [-0.5407175, -0.2800825 ].

c) The fitted value at x=100 is 68.4459-0.4104*100 = 27.4059.

The standard error of the fitted value is given by,

sqrt(sy*(1+1/n+(x1-mx)^2/((n-1)*sx))), where sy & sx is the variances of y and x respectively, mx is the mean of x and the the value is calculated as 7.362462. The 95% CI for the estimate is,

[ est - 2.26*7.36246/sqrt(10), est + 2.26*7.36246/sqrt(10) ] = [ 22.14413, 32.66767].

d) The estimatedvalue at x=84 is 33.9723 and the standard error is 7.411461.

The 95% CI is given by the interval [ 28.67552, 39.26908 ].

The length of the interval is slightly wider in the second case since mean of x (101) is much nearer to 100 than that of 84.

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