You are trying to produce an app to replace realtors and reap their commissions (typically 5 to 6% of theproperty selling price). Your first mission is to produce a tool that helps prospective house buyers estimatethe potential selling price of their houses, and you're working on making a regression model to serve as theengine behind the tool. You'd like to make the model as simple as possible, so you are wondering how wellyou can predict the selling price of a house from just the size of the living area (ignoring garage, etc).In RStudio, run the following code to install and/or library the package "openintro".1. install.packages ("openintro") # don't do this again if you already did this!2. library(openintro)3. amesDelete the install line of code if you are in an RMD file so that it doesn't install every time you knit. Thelast line of code will access the dataset of that name.The table 'ames' gives information about house prices in the fun college town of Ames, lowa. Use the dataset to make a model to predict the price of a house in Ames, lowa in dollars ("price") from the area of livingspace of a house in square feet ("area").a. Make a scatterplot of "price" (y-axis) vs "area" (x-axis). Which plot is the scatterplot?Graph A Graph B Graph C Graph DO ỏOAс-S0+94+05-2005-+005000-4000-30002000-1000-60000 100000 150000 200000area2000area4000i. The hypotheses are:OH: ₁0H₁: 106000Ho: ₁0H₁: 2₁ > 0BD5000-d. Interpret R².4000-30002000-1000-06+004+05-05-26+05-Ce+00-b. Does the relationship appear roughly linear?OYes, all the points fall on a straight line.O No, all the points do not fall on a straight line.O No, there are gaps in the graph along the x-axis.O Yes, there is no obvious curvature in the graphc. Do the data provide strong evidence that the price of a house in Ames, lowa in dollars and the areaof living space of a house in square feet are associated? State the null and alternative hypotheses,report the p-value, and state your conclusion.20+05OH 2₁ 111.694HA₁111.694ⒸH₁: B₁=0HA:B₁01000 2000 3000 4000 5000Area40-05 60+05areaii. The p-value for the test is (round to three decimal places--if that value is 0 then enter 0.):iii. The result of this hypothesis test is:O About 49.95% of the variation in the price of a house in Ames, lowa in dollars isexplained by the least-squares line.O About 49.95% of the variation in the area of living space of a house in square feet isexplained by the least-squares line.ⒸH₂B₁ 0HA:B₁0O the area of living space of a house in square feet is not predictive of the price of a housein Ames, lowa in dollars.O The total unexplained variation in the model.The total amount of variation in the model.Oprice 13289.634 area 111.694- .OHB₁111.694HA: B₁ 111.694O the area of living space of a house in square feet is predictive of the price of a house inAmes, lowa in dollars.e. What is the equation of the regression line?Oarea 111.694 price 13289.634Oprice - 111.694 area + 13289.634Oarea - 13289.634 price 111.694+O the amount of variation in the price of a house in Ames, lowa in dollars that is explained bythe least squares line.O The amount of variation in the area of living space of a house in square feet that is explainedby the least squares line.f. Interpret the slope in the context of the model. For each Select an answer V increase inSelect an answerSelect an answerSelect an answerSelect an answerg. Interpret the y-intercept in the context of the model or explain why it should not be interpreted.When the the price of a house in Ames, lowa in dollars of a house is 0 the expected value ofthe area of living space of a house in square feet is 111.694.O It does not make sense to interpret the y-intercept for this model because no house in thedata had a the area of living space of a house in square feet anywhere close to 0.O When the the price of a house in Ames, lowa in dollars of a house is 0 the expected value ofthe area of living space of a house in square feet is 13289.634.When the the area of living space of a house in square feet of a house is 0 the expected valueof the price of a house in Ames, lowa in dollars is 13289.634.

We shall use the R Programming Language to answer this question. At every step, I shall provide a…

You are trying to produce an app to replace realtors and reap their commissions (typically 5 to 6% of the property selling price). Your first mission is to produce a tool that helps prospective house buyers estimate the potential selling price of their houses, and you're working on making a regression model to serve as the engine behind the tool. You'd like to make the model as simple as possible, so you are wondering how well you can predict the selling price of a house from just the size of the living area (ignoring garage, etc). In RStudio, run the following code to install and/or library the package "openintro". 1. install.packages ("openintro") # don't do this again if you already did this! 2. library(openintro) 3. ames Delete the install line of code if you are in an RMD file so that it doesn't install every time you knit. The last line of code will access the dataset of that name. The table 'ames' gives information about house prices in the fun college town of Ames, lowa. Use the data set to make a model to predict the price of a house in Ames, lowa in dollars ("price") from the area of living space of a house in square feet ("area"). a. Make a scatterplot of "price" (y-axis) vs "area" (x-axis). Which plot is the scatterplot? Graph A Graph B Graph C Graph D O A с 05- 4+05- 2005- +00 1000- 4000- 3000 1000- 60000 100000 150000 200000 Area 2000 area 4000 OH:₁0 HA: 0 6000 OH:₁0 Ηλ: 21 > 0 B D 5000- d. Interpret R² 4000- 3000 2000- 1000- 0+00 20+05 40-05 area 4+05 26+05- b. Does the relationship appear roughly linear? Yes, all the points fall on a straight line. O No, all the points do not fall on a straight line. No, there are gaps in the graph along the x-axis. Yes, there is no obvious curvature in the graph Ce+00- c. Do the data provide strong evidence that the price of a house in Ames, lowa in dollars and the area of living space of a house in square feet are associated? State the null and alternative hypotheses, report the p-value, and state your conclusion. i. The hypotheses are: OH ₁111.694 HA₁111.694 ⒸH₂B₁ 0 HA:B₁0 1000 2000 3000 4000 5000 area 60405 ii. The p-value for the test is (round to three decimal places--if that value is 0 then enter 0.): iii. The result of this hypothesis test is: O About 49.95% of the variation in the price of a house in Ames, lowa in dollars is explained by the least-squares line. O About 49.95% of the variation in the area of living space of a house in square feet is explained by the least-squares line. ⒸH₂B₁ 0 HA:B₁0 O Ho B₁ 111.694 HAB₁111.694 O the area of living space of a house in square feet is not predictive of the price of a house in Ames, lowa in dollars. O the area of living space of a house in square feet is predictive of the price of a house in Ames, lowa in dollars. O The total unexplained variation in the model. The total amount of variation in the model. e. What is the equation of the regression line? O area 111.694 price. 13289.634 Oprice - 13289.634 area 111.694 + O price - 111.694 area + 13289.634 Oarea- 13289.634 price 111.694 O the amount of variation in the price of a house in Ames, lowa in dollars that is explained by the least squares line. O The amount of variation in the area of living space of a house in square feet that is explained by the least squares line. f. Interpret the slope in the context of the model. For each [Select an answer Select an answer Select an answer Select an answer V increase in Select an answer V g. Interpret the y-intercept in the context of the model or explain why it should not be interpreted. When the the price of a house in Ames, lowa in dollars of a house is 0 the expected value of the area of living space of a house in square feet is 111.694. O It does not make sense to interpret the y-intercept for this model because no house in the data had a the area of living space of a house in square feet anywhere close to 0. When the the price of a house in Ames, lowa in dollars of a house is 0 the expected value of the area of living space of a house in square feet is 13289.634. When the the area of living space of a house in square feet of a house is 0 the expected value of the price of a house in Ames, lowa in dollars is 13289.634.