1. How do you include the variable TYPE of restaurant in a regression model? 2.
ID: 3055302 • Letter: 1
Question
1. How do you include the variable TYPE of restaurant in a regression model?
2. Comment on the goodness of fit of MODEL 1 (first image below)
3. interpret the coefficient for the variable Price (model 2 - second image below)
Curvature from the data is captured by a quadratic and a cubic model using only PRICE as explanatory variable. Regression analysis results are shown in table below. If considering previous MODEL 1, MODEL 2 and the quadratic and cubic models below, which one you think fits the data best? Explain why.
1. Write down the quadratic model equation.
2. What is the average SCORE in the quadratic model?
Model 1
SUMMARY OUTPUT Regression Statistics Multiple R 0.586670267 R Square0 Adjusted R 0.309665265 Square Standard 3.025748421 Error Observations 21 ANOVA df F Significance MS Regression Residual Total 91.29017857 91.290189.971453 0.005182 173.9479167 9.155154 265.2380952 19 20 CoefficientsStandard t Stat IP-value | Lower 9596 | Upper | Lower | Upper 95.0% | 95.0% Error ?% Intercept 69.27604167 3.400463147 20.37253 2.28E-14 62.15879 76.39329| 62.15879 76.39329 Price 0.55859375 0.17689553 3.157761 0.005182 0.188347 0.92884 0.188347 0.92884Explanation / Answer
Please see the answers as shown below: -
1.
In order to include the variable TYPE of restaurant into the model, we would have to include that as an binary variable wherein we would include a type of restaurant as 1 and the other as 0. The coefficient that we get would be for the restaurant we coded as 1.
2.
Here, we can see that Significance F is 0.005182 which means it is less than 0.05 and hence the variable is significant.
3.
For every increase of price by 1, dependent variable would go up by 0.5734.
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