Chat with us, powered by LiveChat Smart businesses in all industries use data to provide an intuitive analysis of how they can get a competitive advantage. The real estate industry heavily uses linear regression to estim | Writeden
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## Scenario

Smart businesses in all industries use data to provide an intuitive analysis of how they can get a competitive advantage. The real estate industry heavily uses linear regression to estimate home prices, as cost of housing is currently the largest expense for most families. Additionally, in order to help new homeowners and home sellers with important decisions, real estate professionals need to go beyond showing property inventory. They need to be well versed in the relationship between price, square footage, build year, location, and so many other factors that can help predict the business environment and provide the best advice to their clients.

## Prompt

You have been recently hired as a junior analyst by D.M. Pan Real Estate Company. The sales team has tasked you with preparing a report that examines the relationship between the selling price of properties and their size in square feet. You have been provided with a Real Estate Data Spreadsheet (included in attachment) spreadsheet that includes properties sold nationwide in recent years. The team has asked you to select a region, complete an initial analysis, and provide the report to the team.

Note: In the report you prepare for the sales team, the response variable (y) should be the listing price and the predictor variable (x) should be the square feet.

Specifically you must address the following rubric criteria, using the Module Two Assignment Template Word Document (included in attachment, MUST USE TO COMPLETE ASSIGNMENT):

• Generate a Representative Sample of the Data
• Select a region and generate a simple random sample of 30 from the data.
•  Report the mean, median, and standard deviation of the listing price and the square foot variables.
• Analyze Your Sample
• Discuss how the regional sample created is or is not reflective of the national market.
• Explain how you have made sure that the sample is random.
• Explain your methods to get a truly random sample.
• Generate Scatterplot
• Create a scatterplot of the x and y variables noted above and include a trend line and the regression equation
• Observe patterns
• Answer the following questions based on the scatterplot:
• Define x and y. Which variable is useful for making predictions?
• Is there an association between x and y? Describe the association you see in the scatter plot.
• What do you see as the shape (linear or nonlinear)?
• If you had a 1,800 square foot house, based on the regression equation in the graph, what price would you choose to list at?
• Do you see any potential outliers in the scatterplot?
• Why do you think the outliers appeared in the scatterplot you generated?
• What do they represent?

You can use the following tutorial that is specifically about this assignment. Make sure to check the assignment prompt for specific numbers used for national statistics and/or square footage. The video may use different national statistics or solve for different square footage values.

LINK FOR STATISITCS AND GRAPHS:

Selling Price Analysis for D.M. Pan National Real Estate Company 2

## Report: Selling Price and Area Analysis for D.M. Pan National Real Estate Company

Selling Price and Area Analysis for D.M. Pan National Real Estate Company 1

Southern New Hampshire University

### Introduction

[Include in this section a brief overview, including the purpose of the report.]

### Representative Data Sample

[Present your simple random sample of 30, including the region you selected for your sample. Then identify the mean, median, and standard deviation of the listing price and the square foot variables.]

### Data Analysis

[Discuss how the regional sample created is reflective of the national market. Compare and contrast your regional sample with the national population using the National Statistics and Graphs document found in the Module Two Assignment Guidelines and Rubric.

Explain how you have made sure that the sample is random. Explain your methods to get a truly random sample.]

### Scatterplot

[Insert a scatterplot graph of the sample using the x and y variables. Include a trend line and regression equation.]

The Pattern

[Based on your graph, define each variable, and explain which variable will be useful for making predictions and why.]

[Describe the association between x and y in the scatterplot and determine its shape. Identify any outliers you see in the graph and explain why these occur and what they represent.]

[If you had a 1,800 square foot house, based on the regression equation in the graph, what price would you choose to list at? Explain.]

,

## project 1 data

Real Estate County Data for 2019
2019 Data (n=1000)
Region State County listing price \$'s per square foot square feet
East North Central in grant 219,500 \$116 1,898
East North Central il vermilion 254,500 \$156 1,632
East North Central in henry 235,000 \$148 1,588
East North Central in wayne 203,800 \$141 1,441
East North Central il coles 220,800 \$117 1,893
East North Central il macoupin 197,600 \$111 1,783
East North Central in vigo 165,800 \$122 1,362
East North Central oh jefferson 246,500 \$136 1,814
East North Central il jackson 154,300 \$105 1,463
East North Central oh marion 149,700 \$116 1,296
East North Central mi bay 145,100 \$117 1,239
East North Central il whiteside 283,700 \$136 2,087
East North Central oh trumbull 243,000 \$133 1,827
East North Central in madison 229,100 \$187 1,224
East North Central il knox 205,100 \$118 1,740
East North Central il stephenson 235,600 \$140 1,682
East North Central il macon 212,900 \$128 1,659
East North Central in delaware 221,600 \$134 1,651
East North Central il henry 257,700 \$123 2,087
East North Central oh seneca 211,900 \$168 1,263
East North Central oh darke 160,800 \$114 1,416
East North Central oh scioto 204,200 \$131 1,562
East North Central oh belmont 172,500 \$101 1,710
East North Central oh sandusky 253,900 \$146 1,738
East North Central il rock island 166,300 \$127 1,305
East North Central oh clark 240,500 \$137 1,752
East North Central oh columbiana 241,400 \$164 1,469
East North Central in howard 304,300 \$152 1,996
East North Central oh richland 248,900 \$132 1,880
East North Central il peoria 187,900 \$131 1,434
East North Central il la salle 311,100 \$154 2,015
East North Central il madison 254,500 \$156 1,628
East North Central mi wayne 213,800 \$172 1,243
East North Central in vanderburgh 214,100 \$134 1,596
East North Central oh mahoning 207,500 \$123 1,688
East North Central il williamson 171,600 \$141 1,218
East North Central il winnebago 236,700 \$140 1,692
East North Central il adams 266,100 \$166 1,599
East North Central mi saginaw 171,800 \$118 1,452
East North Central oh montgomery 225,300 \$151 1,493
East North Central oh allen 227,600 \$147 1,550
East North Central oh lucas 228,300 \$115 1,978
East North Central oh ashtabula 177,000 \$107 1,658
East North Central oh lawrence 248,300 \$156 1,587
East North Central oh huron 199,700 \$147 1,359
East North Central il tazewell 278,700 \$165 1,693
East North Central oh summit 185,800 \$101 1,847
East North Central il sangamon 213,500 \$130 1,643
East North Central oh ashland 188,000 \$151 1,246
East North Central oh tuscarawas 270,700 \$149 1,815
East North Central oh ross 257,200 \$127 2,018
East North Central mi shiawassee 192,400 \$129 1,494
East North Central mi calhoun 266,200 \$130 2,042
East North Central il kankakee 148,700 \$115 1,293
East North Central in lawrence 270,600 \$137 1,978
East North Central wi manitowoc 181,400 \$140 1,294
East North Central il st. clair 201,400 \$164 1,225
East North Central mi ingham 222,500 \$125 1,777
East North Central il mclean 203,800 \$134 1,526
East North Central mi jackson 139,200 \$116 1,201
East North Central mi isabella 163,000 \$125 1,307
East North Central wi wood 266,500 \$144 1,853
East North Central mi montcalm 218,300 \$105 2,081
East North Central wi grant 243,200 \$121 2,014
East North Central oh cuyahoga 265,100 \$136 1,947
East North Central oh stark 201,000 \$163 1,230
East North Central oh athens 246,400 \$158 1,560
East North Central wi milwaukee 184,900 \$111 1,666
East North Central mi lenawee 191,500 \$118 1,628
East North Central wi fond du lac 135,300 \$103 1,312
East North Central in st. joseph 193,000 \$111 1,736
East North Central mi ionia 193,600 \$137 1,416
East North Central mi genesee 194,800 \$166 1,173
East North Central oh muskingum 188,300 \$94 1,999
East North Central il ogle 236,600 \$208 1,138
East North Central oh washington 324,400 \$156 2,081
East North Central oh wayne 256,700 \$129 1,986
East North Central mi muskegon 230,400 \$131 1,757
East North Central oh pickaway 265,700 \$143 1,853
East North Central mi st. joseph 188,500 \$135 1,397
East North Central il champaign 246,700 \$121 2,031
East North Central oh knox 192,200 \$127 1,510
East North Central oh lorain 226,200 \$126 1,789
East North Central wi calumet 226,400 \$111 2,033
East North Central mi midland 174,500 \$151 1,157
East North Central mi marquette 172,500 \$120 1,433
East North Central in elkhart 202,300 \$182 1,113
East North Central mi monroe 228,600 \$136 1,679
East North Central oh lake 225,900 \$135 1,676
East North Central mi eaton 189,900 \$96 1,976
East North Central wi douglas 461,400 \$129 3,581
East North Central wi marathon 431,200 \$119 3,638
East North Central il dekalb 347,500 \$97 3,574
East North Central in marion 323,300 \$95 3,408
East North Central in allen 398,000 \$113 3,525
East North Central oh hancock 380,300 \$94 4,028
East North Central in lake 470,600 \$109 4,316
East North Central wi portage 531,000<