writing a script in R and submit both R codes and a Word document.

1. 1. Load the dataset in kc_house_data.csv into R. Call the loaded data kc_house_data. Make sure that you have the directory set to the correct location for the data.

   2. Build a linear model to forecast the price using bedrooms, bathrooms and sqft_living.

a. Then write down the corresponding math formula.

b. Is it a good model based on R square or adjusted R square?

3. Build a linear model to forecast the price using bedrooms, bathrooms and sqft_living and all the cross effects between them.

a. Then write down the corresponding math formula.

b. Is it a better model than the model in Question 2 based on adjusted R square?

4. Build a linear model to forecast the price using bedrooms, bathrooms and sqft_living,waterfront and grade.

a. Then write down the corresponding math formula.

b. Is it a better model than the model in Question 3 based on adjusted R square?

5. Build a linear model to forecast the price using all other columns except id, date, zipcode, lat, and long without a y-intercept. If we only consider the models defined in Q2, Q3, Q4 and Q5, which model do you recommend based on the adjusted R squared value?

6. You are asked to build a linear model to forecast price using bedrooms, bathrooms, sqft_living, sqft_lot, floors, waterfront, view, condition, and grade. Then you are given the flowing new house info:

bedrooms	bathrooms	sqft_living	sqft_lot	floors	waterfront	view	condition	grade
4	2	2560	7650	1.5	1	3	5	10

a. Predict the average sales price for this house.

b. Predict the 95% predicted interval for this house.

the kc_house_data_cvs , could be pulled by searching on google "house sale in king county usa kaggle"

it should the second website by kaggle harlfoxem between may 2014 and may 2015

R script