set.seed(ENTER_YOUR_ID)

y<- round(runif(20, 2, 9), 3)

x1<- round(y*25*(1-runif(20, -0.7, 0.7)), 3)

x2<- c(rep("East", 5), rep("West", 5), rep("North", 5), rep("South", 5))

x3<- round(y*(1-runif(20, -0.5, 0.5)), 3)

the generated variables represent the following:

y - "selling price of a house in 2021" in 100,000 USD

x1 - "square footage" in sq. m.

x2 - "location within the city - East/West/North/South"

x3 - "selling price of a house in 2020" in 100,000 USD

Note: ID number that needs to be entered- 02514

1) Run the following code to fit the multiple linear regression model in R/Rstudio:

summary(lm(y~x1 + x2))

Copy the output from R in your report and answer the following questions:

a) What is the expected selling price of a house which has the size of 100 sq. m. and East location?

b) What are the null and alternative hypotheses for the slope beta1?NB:For the null and alternative hypotheses provide both the formula and explain the hypotheses in words (using the information of what the variables represent)

c) What does the p-value for the individual t-test for the slope beta1 show?