Topic: Data Analysis (R language)

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Q. Consider a dataset which contains three variables, Age (in months), Height (in cm), Number_of_Siblings. Two different linear regression models have been developed using R and the following figures shows the output of the linear regression models.

1. Which of the following correctly explain Linear Regression Model 1?

A.Height = 121.2600 + (27.2490)*(Age) (0.3740)(Number_of_Siblings)

B.64.9055 + (0.6375)*(Age) (0.0177)(Number_of_Siblings) Height = 0

C.64.9055 + (0.6375)*(Age) + (0.0177)(Number_of_Siblings) Height = 0

D.0 = 0.5353*(Height) + (0.0234)*(Age) + (0.0474)(Number_of_Siblings)

2.

Which of the following correctly explain Linear Regression Model 2?

A. Height = 127.7100 + (29.6600)*(Age)

B. Height = 0.5084 + (0.0214)*(Age)

C. 64.9283 + (0.6350)*(Age) Height = 0

D. 64.9283 (0.6350)*(Age) Height = 0

3.

Which of the following are the input variables of Linear Regression Model 1?

I. Height

II. Age

III. Number_of_Siblings

A. I, and II

B. I, and III

C. II, and III

D. I, II, and III

4.

Which of the following is the output variable of Linear Regression Model 1 and Linear Regression Model 2?

A. Height

B. Age

C. Number_of_Siblings

D. None of the above

5.

Which of the following statement is correct for Linear Regression Model 1?

I. It is safe to ignore Number_of_Siblings variable as it has a negative coefficient.

II. It is safe to ignore Number_of_Siblings variable as it has the p-value which is far higher than 0.05.

III. Number_of_Siblings variable must be included in the model as it has the highest p-value.

IV. Age variable must be included in the model as it has p-value less than 0.05.

A. I, and II

B. I, and III

C. II, and IV

D. III, and IV

Call: Im(formula = Height Age + Number_of_Siblings, data = heightdf) = Residuals: Min 10 Median -0.26297 -0.22462 -0.02021 30 Max 0.16102 0.49752 Coefficients: Estimate Std. Error t value Pr>It (Intercept) 64.90554 0.53526 121.260 8.96e-16 *** Age 0.63751 0.02340 27.249 5.85e-10 *** Number_of_Siblings -0.01772 0.04735 -0.374 0.717 Signif. codes: 0***' 0.001 **' 0.01 '*' 0.05 '.'0.1''1 Residual standard error: 0.2677 on 9 degrees of freedom Multiple R-squared: 0.9889, Adjusted R-squared: 0.9865 F-statistic: 402.2 on 2 and 9 DF, p-value: 1.576e-09 Linear Regression Model 1 Call: Im(formula = = Height Age, data = , = heightdf) Residuals: Min 10 Median 30 Max -0.27238 -0.24248 -0.02762 0.16014 0.47238 Coefficients: Estimate Std. Error t value Pr>t] (Intercept) 64.9283 0.5084 127.71