Cost of Living 2020. Numbeo website (https://www

.numbeo.com/cost-of-living/rankings.jsp) has provided an estimate of the cost of living in 440 cities worldwide for the year 2020. This is done by considering the overall Cost of Living Index with the Rent Index, Groceries Index, Restaurant Index, and the Local Purchasing Power Index, which measures the relative purchasing power of buying goods and services in a city compared to the average wage in that city. Can we reconstruct the overall Cost of Living Index from the four component indices? Here is some multiple regression output:
Response variable is: cost of living R-squared = 0.9895, Adjusted R-squared: 0.9790 SE = 0.9788 with 440 observations Variable Coefficient SE(Coeff) t-ratio P-value Intercept 7.9316 0.4148 19.1197 0.0000 Rent Index 0.0228 0.0151 1.5105 0.1316 Groceries Index 0.5412 0.0164 32.9913 0.0000 Restaurant Price Index 0.4190 0.0128 32.7576 0.0000 Local Purchasing Power Index -0.0116 0.0067 -1.7166 0.0868 Assume that the assumptions and conditions for this model are satisfied.

a) What might you recommend as a next step for developing a regression model? Why?
The researcher looking at this problem has proposed the following model:
Response variable is: cost of living R-squared = 0.9788, Adjusted R-squared: 0.9787 SE = 2.9538 with 440 observations Variable Coefficient SE(Coeff) t-ratio P-value Intercept 7.5686 0.3752 20.1707 0.0000 Groceries Index 0.5454 0.0149 36.5683 0.0000 Restaurant Price Index 0.4168 0.0118 35.4591 0.0000

b) What are the advantages of this model compared to the previous model with four predictors?

c) Why did the researcher remove the Rent Index and Local Purchasing Power Index from the model?

d) Do you think the model with two predictors is better?
Explain brief ly.