The accompanying table provides data for tar, nicotine, and carbon monoxide (CO)

Question

The accompanying table provides data for tar, nicotine, and carbon monoxide (CO) contents in a certain brand of cigarette. Find the best regression equation for predicting the amount of nicotine in a cigarette. Why is it best? Is the best regression equation a good regression equation for predicting the nicotine content? Why or why not? Click the icon to view the cigarette content data. Find the best regression equation for predicting the amount of nicotine in a cigarette. Use predictor variables of tar and/or carbon monoxide (CO). Select the correct choice and fill in the answer boxes to complete your choice. (Round to three decimal places as needed.) O A. Nicotine-Taco B. NicotineTar O C. Nicotineco Why is this equation best? A. B. C. O D. t is the best equation o the three because it has he h ghest adjusted R2 the lowest P value, and removing e her predict or noticeably decreases equa t the model. It is the best equation of the three because it has the lowest adjusted R, the highest P-value, and only a single predictor variable It is the best equation of the three because it has the highest adjusted R2 the lowest P value, and only a single predictor variable It is the best equation of the three because it has the lowest adjusted R, the highest P-value, and removing either predictor noticeably decreases the quality of . the model. Is the best regression equation a good regression equation for predicting the nicotine content? Why or why not?

Explanation / Answer

The statistical software output for this problem is:

Multiple linear regression results:
Dependent Variable: Nicotine
Independent Variable(s): Tar, CO
Nicotine = 0.088361978 + 0.1036965 Tar + -0.036372631 CO

Parameter estimates:

Analysis of variance table for multiple regression model:

Summary of fit:
Root MSE: 0.12858166
R-squared: 0.7986
R-squared (adjusted): 0.7803

Hence,

Regression equation:

Option A;

Nicotine = 0.088 + 0.104 Tar + (-0.036) CO

Why is the equation best: Option A is correct.

Is it a good regression equation: Option B is correct.

Parameter Estimate Std. Err. Alternative DF T-Stat P-value Intercept 0.088361978 0.10301466 0 22 0.85776117 0.4003 Tar 0.1036965 0.013731006 0 22 7.5519956 <0.0001 CO -0.036372631 0.011052736 0 22 -3.2908261 0.0033

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