Question

To investigate the effect of Age (in years) and Smoking Status (Group 1: Non-Smokers, Group 2: Smokers) on Systolic Blood Pressure (in mmHg), the following data were collected:

The text from the table in the image is provided below:

Non-smoker     Smoker    
Patient Age (X) Systolic Blood Pressure (Y) Patient Age (X) Systolic Blood Pressure (Y)
1 40 120 1 35 128
2 45 124 2 42 135
3 50 130 3 48 142
4 55 132 4 53 148
5 60 138 5 58 155
6 65 142 6 62 158
7 70 145 7 68 165
8 75 150      

 


 

(i) Fit a multiple regression model.

 

(ii) Obtain the individual regression models for Non-Smokers and Smokers separately based on the main model.

 

(iii) Test the significance of the overall fitted regression model at the 5% level of significance.

25 Mar 2026
Answer :
Word Count : 777
We are asked to solve a multiple regression problem numerically using the provided data for age, smoking status, and systolic blood pressure. Let's go step by step. --- Step 1: Organize the data for multiple regression We define: * (Y) = Systolic Blood Pressure * (X_1) = Age * (X_2) = Smoking Status (0 = Non-smoker, 1 = Smoker) From the table: | Patient | Age (X1) | SBP (Y) | Smoking (X2) | | ------- | -------- | ------- | ------------ | | 1 | 40 | 120 | 0 | | 2 | 45 | 124 | 0 | | 3 | 50 | 130 | 0 | | 4 | 55 | 132 | 0 | | 5 | 60 | 138 | 0 | | ___ ______ ________ _____ ___ ______ __________ _____ ______ ______.
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Related Questions

To investigate the effect of Age (in years) and Smoking Status (Group 1: Non-Smokers, Group 2: Smokers) on Systolic Blood Pressure (in mmHg), the following data were collected:

State whether the following statements are true or false and also give the reason in support of your answer.

(i) The model Y = BX + & is a linear model.

(ii) If total and residual sum of squares for a dataset are 124 and 15.5, respectively, then the coefficient of determination is 0.875.

(iii) We use Cook's distances to identify the autocorrelation of the error terms.

(iv) If the coefficient of determination for the auxiliary regression model of the explanatory variable X₁ is 0.8, the VIF for X₁ will be 5.

(v) We use baseline category logit models when we have ordered categories of a response variable.

To investigate the effect of Age (in years) and Smoking Status (Group 1: Non-Smokers, Group 2: Smokers) on Systolic Blood Pressure (in mmHg), the following data were collected:

The txt from the table in the image is provided below:

on-smoker     Smoker    
Patient Age (X) Systolic Blood Pressure (Y) Patient Age (X) Systolic Blood Pressure (Y)
1 40 120 1 35 128
2 45 124 2 42 135
3 50 130 3 48 142
4 55 132 4 53 148
5 60 138 5 58 155
6 65 142 6 62 158
7 70 145 7 68 165
8 75 150      

 

(i) Fit a multiple regression model.

(ii) Obtain the individual regression models for Non-Smokers and Smokers separately based on the main model.

(iii) Test the significance of the overall fitted regression model at the 5% level of significance.

To investigate the effect of Age (in years) and Smoking Status (Group 1: Non-Smokers, Group 2: Smokers) on Systolic Blood Pressure (in mmHg), the following data were collected:

The text from the table in the image is provided below:

Non-smoker     Smoker    
Patient Age (X) Systolic Blood Pressure (Y) Patient Age (X) Systolic Blood Pressure (Y)
1 40 120 1 35 128
2 45 124 2 42 135
3 50 130 3 48 142
4 55 132 4 53 148
5 60 138 5 58 155
6 65 142 6 62 158
7 70 145 7 68 165
8 75 150      

 


 

(i) Fit a multiple regression model.

 

(ii) Obtain the individual regression models for Non-Smokers and Smokers separately based on the main model.

 

(iii) Test the significance of the overall fitted regression model at the 5% level of significance.

Fit a simple linear regression model on the data given in Question 2 for non-smokers only and obtain the followings:

(i) Diagonal of the hat matrix. Also, check the leverage points, if any.

(ii) Cook's Distances. Also, verify the influence points, if any.

A logistics company wants to predict the delivery efficiency score of its drivers based on two primary factors: years of driving experience and average hours of sleep per night. The data for 10 drivers are recorded as follows:

Based on the image provided, here is the data transcribed into a table format:

State whether the following statements are true or false and also give the reason in support of your answer.

(i) The model Y = BX + & is a linear model.

(ii) If total and residual sum of squares for a dataset are 124 and 15.5, respectively, then the coefficient of determination is 0.875.

(iii) We use Cook's distances to identify the autocorrelation of the error terms.

(iv) If the coefficient of determination for the auxiliary regression model of the explanatory variable X₁ is 0.8, the VIF for X₁ will be 5.

(v) We use baseline category logit models when we have ordered categories of a response variable.

The following table shows the concentration of a new antibiotic, the total number of infected subjects, and the number of subjects who showed complete recovery:

 

S. No. Concentration ($x_i$) Recovered ($y_i$) Total Subjects ($n_i$)
1 2 10 50
2 4 15 45
3 6 22 40
4 8 35 60
5 10 68 80

 


(i) Fit a logistic regression model considering ẞ=-2.148 and ẞ = 0.36, as the initial values of the parameters. Perform calculations for only one iteration using the Newton-Raphson method.

(ii) Test the significance of the fitted model using the Hosmer-Lemeshow test at 5% level of significance.

 

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