3-3 Assignment: Real Estate Analysis Part II - Statistics
This assignment picks up where the Module Two assignment left off and will use components of that assignment as a foundation. You have submitted your initial analysis to the sales team at D.M. Pan Real Estate Company. You will continue your analysis of the provided Real Estate County Data spreadsheet using your selected region to complete your analysis. You may refer back to the initial report you developed in the Module Two Assignment Template to continue the work. This document and the National Statistics and Graphs spreadsheet will support your work on the assignment. Note: In the report you prepare for the sales team, the dependent, or response, variable (y) should be the median listing price and the independent, or predictor, variable (x) should be the median square feet. Using the Module Three Assignment Template, specifically address the following: Regression Equation: Provide the regression equation for the line of best fit using the scatterplot from the Module Two assignment. Determine r: Determine r and what it means. (What is the relationship between the variables?) Determine the strength of the correlation (weak, moderate, or strong). Discuss how you determine the direction of the association between the two variables. Is there a positive or negative association? What do you see as the direction of the correlation? Examine the Slope and Intercepts: Examine the slopeb1{version:1.1,math:<math xmlns=http://www.w3.org/1998/Math/MathML><msub><mi>b</mi><mn>1</mn></msub></math>} and intercept b0{version:1.1,math:<math xmlns=http://www.w3.org/1998/Math/MathML><msub><mi>b</mi><mn>0</mn></msub></math>}. Draw conclusions from the slope and intercept in the context of this problem. Does the intercept make sense based on your observation of the line of best fit? Determine the value of the land only. Note: You can assume, when the square footage of the house is zero, that the price is the value of just the land. This happens when x=0, which is the y-intercept. Determine the R-squared Coefficient: Determine the R-squared value. Discuss what R-squared means in the context of this analysis. Conclusions: Reflect on the Relationship: Reflect on the relationship between square feet and sales price by answering the following questions: Is the square footage for homes in your selected region different than for homes overall in the United States? For every 100 square feet, how much does the price go up (i.e., can you use slope to help identify price changes)? Use the regression equation to estimate how much you would list your house for if it was 1,200 square feet. What square footage range would the graph be best used for?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 Kayla Murphy Selling Price and Area Analysis for D.M. Pan National Real Estate Company 1 Southern New Hampshire University Introduction This study was carried out by real estate industry to determine competitive advantage of smart businesses. Statistical test such as regression analysis and descriptive statistics were used where regression analysis was used to determine the relationship between the selling price of properties and their size in square feet while descriptive statistics was used to summarize the data. A random sample of 30 real estates were selected where their median listing price and median square feet were recorded to carry out the study. Representative Data Sample Results from table 1 showed the results of 30 random samples from Texas region Table 1 median listing price($) median square feet 226000 1500 314000 1300 215000 1600 354000 1650 245000 1700 257000 1200 305000 1350 317000 2000 267000 1850 268000 1750 271000 1640 229000 1625 321000 1550 216000 1150 290000 1350 279000 1700 320000 1850 211000 1400 269000 1450 225000 1250 316000 1540 328000 1260 335000 1360 327000 1900 238000 1870 240000 2100 237000 1270 302000 1670 301000 1980 224000 2140 Descriptive statistics According to Nick (2007), descriptive statistics is a statistical measure that summarizes data into measures of center and central tendency table below showed the summary of median listing price and median square feet. Table 2 median listing price ($)   median square feet           Mean 274900 Mean 1598.5 Median 270000 Median 1612.5 Standard Deviation 42842.4 Standard Deviation 278.336 Results from the above table showed that the mean of the median listing price was $274900 while that of median square feet was 1598.5. The median value of the listing price was $270000 with the median of square feet being 1612.5. Standard deviation can be described as a measure of variation of a given data value from the mean. The standard deviation of median listing price was 42842.4 while that of median square feet was 278.336. Data Analysis From the descriptive statistics it was clear that the average median listing price of $274900 from the study was lower compared to that of national market that was approximately $284600.The sample is a reflective of the national market since the mean value is almost the same to that of the national market. The sample was made random by selecting the various samples from different strata’s. This included dividing the target population into different groups called strata’s and randomly selecting the 30 samples from the various groups. A samMedian Housing Price Prediction Model for D.M. Pan National Real Estate Company 2 [Note: To complete this template, replace the bracketed text with your own content. Remove this note before you submit your report.] Median Housing Price Prediction Model for D.M. Pan Real Estate Company [Your Name] Southern New Hampshire University Median Housing Price Prediction Model for D.M. Pan Real Estate Company 1 Module Two Notes [Copy and paste any relevant information from your Module Two assignment here to assist you in completing this assignment. This section is not graded and is only provided to help you easily review Module Two assignment information while completing this assignment.] Regression Equation [Insert the regression equation for the line of best fit using the scatterplot from your Module Two assignment.] Determine r [Determine r and what it means, including determining the strength of the correlation and discussing how you determine the direction of the association between the two variables.] Examine the Slope and Intercepts [Draw conclusions from the slope and intercept in the context of this problem and determine the value of only the land.] R-squared Coefficient [Explain what R-squared means in the context of this analysis.] Conclusions [Reflect on the relationship between square feet and sales price by addressing key considerations such as the comparison between your selected region and overall homes in the United States, as well as analyzing how the slope can help identify price changes, how the regression equation can help identify appropriate listing prices, and which graph would be best suited to informing square footage ranges.]
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