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- (Econmetrics) Q.1 How can you test for general misspecification of model if it would have only (any of) two independent variables?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.Define Interpretation of coefficients in polynomial regression models?
- (2)What would the consequence be for a regression model if theerrors were not homoscedastic?In 2017, Philadelphia launched a sweetened beverage tax of 1.5 cents per ounce, raising the cost of a 2-liter soda bottle from about $1.50 to $2.50. One year later, the Philadelphia mayor wants to evaluate if this "sugar tax" improves the health status of Philadelphia Propose ONE method (i.e. difference-in-difference, instrumental variables, or regression discontinuity) to address these questions. write down its implementation details (the type of data you need, potential sources to get the data, equations) its pros and cons Only Typing answer please I need ASAPWhat is a linear regression model? What is measured by the coefficients ofa linear regression model? What is the ordinary least squares estimator?
- 2. One topic from this module is heteroskedasticity.A. Convince me that you know what heteroskedasticity is.B. How can you use a scatterplot of the residuals from a regression model to look for heteroskedasticity?C. Explain briefly how to conduct a Park test for heteroskedasticity.I dont understand why my question is being rejected so i'll resend this one. The data is attached in the image. Please help me with these questions. Thank you in advanced!! a. How many observations are included in the data? Is the data balanced?b. Is the above result estimated from the fixed effects model or the random effects model?c. Explain the meaning of the estimate coefficient of the variable ???Please no written by hand The assumption of normally distributed errors means that... A. errors can be ignored when doing regression modelling. B. the OLS estimators can also be assumed to be normally distributed since they are a linear functions of the errors. C. the OLS estimators can also be assumed to be normally distributed since they are BLUE. D. the OLS estimators can also be assumed to be normally distributed since they are minimum variance. E. the regression model will not be subject to specification error.