You have a hot grill and an empty hamburger bun, but you have sworn off greasy hamburgers. Would a meatless hamburger do? The data for 12 brands of meatless hamburger were collected. Calories, fat, and sodium in these meatless hamburgers were used to predict taste score (between 0 and 100). What does the p-value for fat, 0.034, tell us?
Predictor Coef SE Coef t-value P-value
Constant 69.42 23.84 2.91 0.020
calories -0.5772 0.2727 -2.12 0.017
fat 8.359 3.263 2.56 0.034
Answer
a. If calories is already in the model, we should still add fat to the model since the p-value for fat is less than 0.05.
b. Since the p-value is less than 0.05, we don’t need to keep fat in the model
c. If calories is already in the model, we should still add fat to the model since the p-value for calories is less than 0.05.
d. If calories is not in the model, we have to include fat in the model.
HI, I have a similar problem, did you solve this by any chance?
To understand the significance of the p-value for the predictor "fat," we need to first understand the concept of p-value in statistical analysis. The p-value represents the probability of obtaining results as extreme as the observed results or even more extreme, assuming the null hypothesis is true.
In this case, the null hypothesis would be that the predictor "fat" has no significant effect on the taste score of the meatless hamburgers. The alternative hypothesis would be that there is a significant effect of "fat" on the taste score.
The p-value for "fat" is given as 0.034. Since this p-value is less than the significance level typically set at 0.05, we reject the null hypothesis. This means that there is evidence to suggest that "fat" does have a significant effect on the taste score of the meatless hamburgers.
Therefore, the correct answer based on this information would be:
a. If calories is already in the model, we should still add fat to the model since the p-value for fat is less than 0.05.