An important problem in real estate is determining how to price homes to be sold There are so many factors size, age, and style of the home number of bedrooms and bathrooms size of the lot and so on which makes setting a price a challenging task In this project, we will investigate the relationships among typical characteristics of homes and home prices, identify key variables related to pricing, and build linear regression models to predict prices based on property characteristics Our analysis will be based on the Mount Pleasant Real Estate Data (available on stat hawkeslearning com) This data set includes information about 245 properties for sale in three communities in the suburban town of Mount Pleasant, South Carolina, in 2017 1 Download the Mount Pleasant Real Estate Data from stat hawkeslearning com and open it with Microsoft Excel 2 To ensure the data contains comparable properties, eliminate duplexes and properties whose prices are outliers What limitations does this impose on our analysis 3 The statistical tools from the current chapter focus on numeric data, so eliminate non numeric variables from the data Does this remove potentially useful information 4 Are there any redundant variables we could eliminate Phase 2 Discovering Relationships 5 How strongly does each remaining variable correlate to the price 6 Which variable correlates most strongly with price 7 Are any variables weakly correlated with price Practically speaking, why do you think this is true Enable the Analysis ToolPak add in to Excel The regression tool will be used 9 Find the regression line b 0 b 1 x predicting home price by the variable most highly correlated to it Assess the fit of the line in terms of error and the proportion of variation explained by the model 10 For which properties do the model's predictions have the greatest errors What is an intuitive reason for this 8 Do scatter plots reveal any nonlinear pattern between price and the weakly correlated variables Price vs Stories 0 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 b Price vs Year Built 2020 201 5 201 0 2005 2000 1 995 1990 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 Phase 3 Constructing Predictive Models Enable the Analysis ToolPak odd in to Excel Square Footage Line Fit plot The regression tool will be used 1200300 9 Find the regression ineji bD bx predicting 1000 000 3' ' home price by the variable most highly $800,000 correlated to it Assess the fit of the line in $500 000 L P n 0 ISI rice terms of error and the proportion of variation $400,000 explained by the model I Predicted List Price List Price $200,000 10 For which properties do the model's 0 predictions have the greatest errors What is 0 1000 2000 3000 4000 5000 5000 an intuitive reason for this

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An important problem in real estate is determining how to price homes to be sold. There are so many factors?size, age, and style of the

An important problem in real estate is determining how to price homes to be

sold. There are so many factors?size, age, and style of the home; number of

bedrooms and bathrooms; size of the lot; and so on?which makes setting a price

a challenging task. In this project, we will investigate the relationships among

typical characteristics of homes and home prices, identify key variables related

to pricing, and build linear regression models to predict prices based on property

characteristics. Our analysis will be based on the Mount Pleasant Real Estate Data

(available on stat.hawkeslearning.com). This data set includes information about 245 properties for sale in

three communities in the suburban town of Mount Pleasant, South Carolina, in 2017.

Download the Mount Pleasant Real Estate

Data from stat.hawkeslearning.com and open

it with Microsoft Excel.

To ensure the data contains comparable

properties, eliminate duplexes and properties

whose prices are outliers. What limitations

does this impose on our analysis?

The statistical tools from the current chapter

focus on numeric data, so eliminate non-

numeric variables from the data. Does this

remove potentially useful information?

Are there any redundant variables we

could eliminate?

Phase 2: Discovering Relationships

How strongly does each remaining variable

correlate to the price?

Which variable correlates most strongly

with price?

Are any variables weakly correlated with price?

Practically speaking, why do you think this

is true?

Enable the Analysis ToolPak add-in to Excel.

The regression tool will be used.

Find the regression line

predicting

home price by the variable most highly

correlated to it. Assess the fit of the line in

terms of error and the proportion of variation

explained by the model.

10.

For which properties do the model's

predictions have the greatest errors? What is

an intuitive reason for this?

8. Do scatter plots reveal any nonlinear pattern between price and the weakly correlated variables? Price vs. Stories 0 . . . ..... . ..... $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 b. Price vs. Year Built 2020 201 5 201 0 2005 2000 1 995 1990 $0 $200,000 $400,000 $600,000 $800,000 $1,000,000 $1,200,000 Phase 3: Constructing Predictive Models. Enable the Analysis ToolPak odd-in to Excel. Square Footage Line Fit plot The regression tool will be used. \"1200300 9. Find the regression |ineji= bD + bx predicting \"1000.000 3' ' home price by the variable most highly $800,000 . correlated to it. Assess the fit of the line in $500 000 L. P _ n 0 ISI rice terms of error and the proportion of variation . $400,000 explained by the model. I Predicted List Price List Price $200,000 10. For which properties do the model's 0 predictions have the greatest errors? What is 0 1000 2000 3000 4000 5000 5000 an intuitive reason for this