Explain the concept of model selection criteria such as Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) in the context of linear regression. How can these criteria be used to compare and select among competing regression models?
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- Imagine you are trying to explain the effect of square footage on home sale prices in the United States. You collect a random sample of 100,000 homes that recently sold. a) Homes can be one of three types: single-family houses, townhomes, or condos. How would you control for a home’s type in a regression model? b) Write down a regression model that includes controls for home type, square footage, and number of bedrooms. c) How would you interpret the es3mated coefficients for each of the variables from part b? Be specific.You are the owner of a restaurant located in a beach resort in Hawaii and want to use regression analysis to estimate the demand for your fresh seafood dinners. You have collected data on the daily quantity of seafood dinners sold over the last summer season. In order to correctly specify your regression equation, which of the following variables should be considered? Select one: A. the prices charged for souvenirs in local stores B. the prices charged for scuba diving excursions at the resort C. the wages paid to your chef and servers D. the daily number of vacationers at the resortWhat are the four assumptions of linear regression (simple linear and multiple)?
- Can we compare the linear-log model and the log-log model? Which of the log regression models best fits the data?In multiple regressions, the correlation coefficient of each independent variable can be measured in addition to the multiple correlation coefficient. How do the values of individual correlation coefficients compare to the value of the multiple correlation coefficient?1) State in algebraic notation and explain the assumption about the classical linear regression models disturbances that are referred to by the term ‘homoscedasticity’.