satisfaction. The restaurant employs three chefs: Richard, Thomas, and Linda. Sarah decides to compare the following two candidate models: (A) y = Bo + B₁x₁1 +8, and (B) y = ßo + B₂x₂ + B₂x2 + 3x3 +8, where y denotes the tip (in euros), x, denotes the total price on the bill (in euros), X₂ is a dummy variable whose value is 1 if Thomas prepared the meal(s) and 0 otherwise, and x3 is another dummy whose value is 1 if Linda prepared the meals(s) and 0 otherwise.

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Sarah studies Business and Management and she also has a part-time job as a waitress at a
restaurant. The restaurant does not include service charges in their bills, and therefore Sarah often
receives tips from the customers. Some customers give her very generous tips, while others leave
hardly anything or nothing at all. Sarah began to wonder what the size of the tip may depend on. Just
out of curiosity, she started to keep a record of the total prices on the bills and the tips she received.
At first, she fit a simple linear regression model to the data and found that the size of the tip was
significantly related to the total price. But at this point, she speculated that the tips may be affected
by other factors as well, and an even better model could possibly be constructed by including further
independent variables. For instance, the chef who prepares the dish(es) might affect the customers'
satisfaction. The restaurant employs three chefs: Richard, Thomas, and Linda. Sarah decides to
compare the following two candidate models: (A) y = Bo + B₁x₁ +8, and (B) y = B + ₁x₁ +
B₂x2 + 3x3 +8, where y denotes the tip (in euros), x₁ denotes the total price on the bill (in euros),
X₂ is a dummy variable whose value is 1 if Thomas prepared the meal(s) and 0 otherwise, and x; is
another dummy whose value is 1 if Linda prepared the meals(s) and 0 otherwise.
Sarah's data set consists of 64 orders. She would like to conduct a statistical test at a = .01 to
determine if there is evidence that including information about the chef in the model significantly
reduces the errors of the predictions. Name the statistical procedure she will use, specify the null and
alternative hypotheses, and provide the critical value of the test statistic.
Transcribed Image Text:Sarah studies Business and Management and she also has a part-time job as a waitress at a restaurant. The restaurant does not include service charges in their bills, and therefore Sarah often receives tips from the customers. Some customers give her very generous tips, while others leave hardly anything or nothing at all. Sarah began to wonder what the size of the tip may depend on. Just out of curiosity, she started to keep a record of the total prices on the bills and the tips she received. At first, she fit a simple linear regression model to the data and found that the size of the tip was significantly related to the total price. But at this point, she speculated that the tips may be affected by other factors as well, and an even better model could possibly be constructed by including further independent variables. For instance, the chef who prepares the dish(es) might affect the customers' satisfaction. The restaurant employs three chefs: Richard, Thomas, and Linda. Sarah decides to compare the following two candidate models: (A) y = Bo + B₁x₁ +8, and (B) y = B + ₁x₁ + B₂x2 + 3x3 +8, where y denotes the tip (in euros), x₁ denotes the total price on the bill (in euros), X₂ is a dummy variable whose value is 1 if Thomas prepared the meal(s) and 0 otherwise, and x; is another dummy whose value is 1 if Linda prepared the meals(s) and 0 otherwise. Sarah's data set consists of 64 orders. She would like to conduct a statistical test at a = .01 to determine if there is evidence that including information about the chef in the model significantly reduces the errors of the predictions. Name the statistical procedure she will use, specify the null and alternative hypotheses, and provide the critical value of the test statistic.
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