Question

Consider the following data sample from the Consumer Reports Restaurant Satisfaction Survey where variable Type indicates whether the restaurant is Italian or a Seafood/steakhouse restaurant. Price indicates average amount paid per person for dinner and drinks. Score reflects diner’s overall satisfaction, with higher values indicating greater satisfaction (100 is completely satisfied). A regression analysis is conducted using several steps to gauge the impact of the explanatory variables on Score (diner’ satisfaction).

Restaurant	Price ($)	Score	Type
Bertucci's	16	77	Italian
Black Angus	24	79	Seafood/Steak
Bonefish Grill	26	85	Seafood/Steak
Bravo!cuccina italiana	18	84	Italian
Buca di Beppo	17	81	Italian
Bugaboo Steak House	18	77	Seafood/Steak
Carrabba's Italian grill	23	86	Italian
Brown's Steakhouse	17	75	Seafood/Steak
Il Fornaio	28	83	Italian
Joe's crab Shack	15	71	Seafood/Steak
Johnny Carino's Italian	17	81	Italian
Lone Star SteakHouse	17	76	Seafood/Steak
Longhorn steakhouse	19	81	Seafood/Steak
Maggio's little Italy	22	83	Italian
McGrath's Fish House	16	81	Seafood/Steak
Oliven Graden	19	79	Italian
Outback Steakhouse	20	82	Italian
Red Lobster	18	81	Seafood/Steak
Romano's macorroni grill	18	82	Italian
The old spaguetti factory	12	79	Italian
Uno Chicago Grill	16	80	Italian

MODEL 1- includes only the Price to explain Score

1. (1pt) Comment on the goodness of fit of MODEL 1.

   Fully explain here:

2. (1pt) Report the statistical significance of MODEL 1

   Fully explain here:

Answer 1

Sol:

For fitting the model in SPSS just enter data into spss then click

Then select the dependent and independent variable according to given in question. after doing this you provide several tables of results for that perticular model

1.

You can see the statistical significance of model 1 in spss that P-valu for model 1 is 0.005 which is less than .05 hence this model is significant.

2.

From the summary table of model 2 you can see that value of R-square is 0.5262 and adjusted R-square is 0.4736 which is better than of model1 but not that better.

3

. From the ANOVA table of model 2 you can see that P-value of both variables PRICE and DTYPE is less tha .05 so both of the variabes are significant for our regression model

4.

Inclusion of variable DTYPE is increasing the value of adjusted R-Squared from 0.3097 to 0.4736 which shows that variable DTYPE is significant variable in presence of PRICE but not alone.

5.

estimated regression equation for

MODEL : Score = 69.27 + 0.56 * PRICE , MODEL 2 : Score =  67.40 + 0.57 * PRICE + 3.03*DTYPE

6.

intecept of model means the SCORE of a resturant when PRICE is zero and resturant type is Seafood/Steak

7.

coefficient of variable DTYPE means change in score of a resturant when we change resturant type from Italian to Seafood/Steak keeping PRICE fixed