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This question was created from hw6(1).pdf https://www.coursehero.com/file/26903949/hw61pdf/

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3. In this example we will revisit the house price data we have used previously. Below is
the code for loading the data and taking a log transformation of the price. house_data <— read . csv( "http: //m. lockfistat . com/datasets/HomesForSale.
log_price <— log(house_data$Price, 10) For these problems you will be using log_price as the response. You will fit 3 models: m1 Predictors: Size + Beds
m2 Predictors: Size + Beds + Baths m3 Predictors: Size + Beds + Baths + State (a) Do an F—test comparing models 1 (m1) and 2 (1112). State the null and alternative
hypothesis and include your interpretation of the results. Below is the R code anova (m1 , m2) (b) Do an F-test comparing models 2 (m2) and 3 (1113). State the null and alternative
hypothesis and include your interpretation of the results. (c) The ANOVA table returns Df (degrees of freedom), which is the difference in the
size of the two models. For the second comparison the Df is 3. Why is this, if the models only difl'er by one variable?

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