C and D on the worksheet, Statistics Assignment Homework Help

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Just do questions C and D on the worksheet attached.

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Real Estate Regression Exercise QNT/351 Version 5 University of Phoenix Material Real Estate Regression Exercise Directions: Use the real estate data you used for your Week 2 learning team assignment. Analyze the data and explain your answers. You are consulting for a large real estate firm. You have been asked to construct a model that can predict listing prices based on square footages for homes in the city you’ve been researching. You have data on square footages and listing prices for 100 homes. 1. Which variable is the independent variable (x) and which is the dependent variable (y)? The independent variable (x) is the square footage and the dependent variable (y) is the listing price. This is because the price should change (i.e. is dependent on) based on the square footage. 2. Click on any cell. Click on Insert→Scatter→Scatter with markers (upper left). To add a trendline, click Tools→Layout→Trendline→Linear Trendline Does the scatterplot indicate observable correlation? If so, does it seem to be strong or weak? In what direction? There appears to be a positive correlation but due to two outliers not as strong as we would expect. 3. Click on Data→Data Analysis→Regression→OK. Highlight your data (including your two headings) and input the correct columns into Input Y Range and Input X Range, respectively. Make sure to check the box entitled “Labels”. Copyright © 2016 by University of Phoenix. All rights reserved. 1 Real Estate Regression Exercise QNT/351 Version 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.313437 R Square Adjusted R Square 0.098243 Standard Error 133343.7 0.089041 Observations 100 ANOVA df Regression SS MS 1 1.9E+11 1.9E+11 Residual 98 1.74E+12 1.78E+10 Total 99 1.93E+12 Coefficients Standard Error F 10.67673 t Stat P-value Significance F 0.001496 Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 116038.7 17191.68 6.749704 1.05E-09 81922.42 150155.1 81922.42 150155.1 SqFt 18.18248 5.5646 3.267526 0.001496 7.139708 29.22524 7.139708 29.22524 (a) What is the Coefficient of Correlation between square footage and listing price? The correlation coefficient is .313437. (b) Does your Coefficient of Correlation seem consistent with your answer to #2 above? Why or why not? Yes, it is consistent. The correlation coefficient (r) is positive which indicates a positive relationship between square footage and price. However, r is not very high. If it were close to 1, then it would indicate a strong positive relationship, therefore a correlation coefficient of .313437 indicates a weaker relationship. If the two outliers were removed, we would see a much stronger correlation. (c) What proportion of the variation in listing price is determined by variation in the square footage? What proportion of the variation in listing price is due to other factors? (d) Check the coefficients in your summary output. What is the regression equation relating square footage to listing price? Copyright © 2016 by University of Phoenix. All rights reserved. 2 Real Estate Regression Exercise QNT/351 Version 5 (e) Test the significance of the slope. What is your t-value for the slope? Do you conclude that there is no significant relationship between the two variables or do you conclude that there is a significant relationship between the variables? (f) Using the regression equation that you designated in #3(d) above, what is the predicted sales price for a house of 2100 square feet? Copyright © 2016 by University of Phoenix. All rights reserved. 3 Real Estate Regression Exercise QNT/351 Version 5 University of Phoenix Material Real Estate Regression Exercise Directions: Use the real estate data you used for your Week 2 learning team assignment. Analyze the data and explain your answers. You are consulting for a large real estate firm. You have been asked to construct a model that can predict listing prices based on square footages for homes in the city you’ve been researching. You have data on square footages and listing prices for 100 homes. 1. Which variable is the independent variable (x) and which is the dependent variable (y)? The independent variable (x) is the square footage and the dependent variable (y) is the listing price. This is because the price should change (i.e. is dependent on) based on the square footage. 2. Click on any cell. Click on Insert→Scatter→Scatter with markers (upper left). To add a trendline, click Tools→Layout→Trendline→Linear Trendline Does the scatterplot indicate observable correlation? If so, does it seem to be strong or weak? In what direction? There appears to be a positive correlation but due to two outliers not as strong as we would expect. 3. Click on Data→Data Analysis→Regression→OK. Highlight your data (including your two headings) and input the correct columns into Input Y Range and Input X Range, respectively. Make sure to check the box entitled “Labels”. Copyright © 2016 by University of Phoenix. All rights reserved. 1 Real Estate Regression Exercise QNT/351 Version 5 SUMMARY OUTPUT Regression Statistics Multiple R 0.313437 R Square Adjusted R Square 0.098243 Standard Error 133343.7 0.089041 Observations 100 ANOVA df Regression SS MS 1 1.9E+11 1.9E+11 Residual 98 1.74E+12 1.78E+10 Total 99 1.93E+12 Coefficients Standard Error F 10.67673 t Stat P-value Significance F 0.001496 Lower 95% Upper 95% Lower 95.0% Upper 95.0% Intercept 116038.7 17191.68 6.749704 1.05E-09 81922.42 150155.1 81922.42 150155.1 SqFt 18.18248 5.5646 3.267526 0.001496 7.139708 29.22524 7.139708 29.22524 (a) What is the Coefficient of Correlation between square footage and listing price? The correlation coefficient is .313437. (b) Does your Coefficient of Correlation seem consistent with your answer to #2 above? Why or why not? Yes, it is consistent. The correlation coefficient (r) is positive which indicates a positive relationship between square footage and price. However, r is not very high. If it were close to 1, then it would indicate a strong positive relationship, therefore a correlation coefficient of .313437 indicates a weaker relationship. If the two outliers were removed, we would see a much stronger correlation. (c) What proportion of the variation in listing price is determined by variation in the square footage? What proportion of the variation in listing price is due to other factors? (d) Check the coefficients in your summary output. What is the regression equation relating square footage to listing price? Copyright © 2016 by University of Phoenix. All rights reserved. 2 Real Estate Regression Exercise QNT/351 Version 5 (e) Test the significance of the slope. What is your t-value for the slope? Do you conclude that there is no significant relationship between the two variables or do you conclude that there is a significant relationship between the variables? (f) Using the regression equation that you designated in #3(d) above, what is the predicted sales price for a house of 2100 square feet? Copyright © 2016 by University of Phoenix. All rights reserved. 3
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Real Estate Regression Exercise
QNT/351 Version 5

University of Phoenix Material
Real Estate Regression Exercise
Directions: Use the real estate data you used for your Week 2 learning team
assignment. Analyze the data and explain your answers.
You are consulting for a large real estate firm. You have been asked to construct a model that can
predict listing prices based on square footages for homes in the city you’ve been researching. You have
data on square footages and listing prices for 100 homes.
1.

Which variable is the independent variable (x) and which is the dependent variable (y)?
The independent variable (x) is the square footage and the dependent variable (y) is the listing
price. This is because the price should change (i.e. is dependent on) based on the square
footage.

2. Click on any cel...


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