Linear Regression: Use Data Analysis in Excel to conduct the Regression Analysis to reproduce the excel...
Linear Regression:
Use Data Analysis in Excel to conduct the Regression Analysis to reproduce the excel out put below (Note: First enter the data in the next page in an Excel spreadsheet)
Home Sale Price: The table below provides the Excel output of a regression analysis of the relationship between Home sale price(Y) measured in thousand dollars and Square feet area (x):
|
SUMMARY OUTPUT |
Dependent: |
Home Price |
($1000) |
|||
|
Regression Statistics |
||||||
|
Multiple R |
0.691 |
|||||
|
R Square |
0.478 |
|||||
|
Adjusted R Square |
0.465 |
|||||
|
Standard Error |
27.21 |
|||||
|
Observations |
80 |
|||||
|
ANOVA |
||||||
|
df |
SS |
MS |
F |
Significance F |
||
|
Regression |
2 |
52378.32252 |
26189.16126 |
35.36552756 |
1.27417E-11 |
|
|
Residual |
77 |
57020.65136 |
740.5279397 |
|||
|
Total |
79 |
109398.9739 |
||||
|
Coefficients |
Standard Error |
t Stat |
P-value |
Lower 95% |
Upper 95% |
|
|
Intercept |
11.581 |
29.82 |
0.388 |
0.698755345 |
-47.80910537 |
70.98276434 |
|
Size(Sqft) |
0.069 |
0.014 |
5.56 |
3.67568E-07 |
0.044314403 |
0.093711307 |
|
Bathrooms |
17.807 |
7.33 |
2.42 |
0.017548601 |
3.198869475 |
32.41601103 |
- The value of R2, the measure of goodness of fit of the model, is 0.46. Explain what that means. Is this a good fit?
Homework Answers
Answer: R2 is 0.46 which indicates that 46% of the variation in home sale price can be explained by the regression model.
It is not a good fit because 46% out of 100% is not high.
Add Answer to:
Linear Regression:
Use Data Analysis in Excel to conduct the Regression Analysis to
reproduce the excel...
HW # 5 Linear Regression: Use Data Analysis in Excel to conduct the Regression Analysis to...
HW # 5 Linear Regression: Use Data Analysis in Excel to conduct the Regression Analysis to reproduce the excel out put below (Note: First enter the data in the next page in an Excel spreadsheet) Home Sale Price: The table below provides the Excel output of a regression analysis of the relationship between Home sale price(Y) measured in thousand dollars and Square feet area (x): SUMMARY OUTPUT Dependent: Home Price ($1000) Regression Statistics Multiple R 0.691 R Square 0.478 Adjusted...
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Consider the following excel regression analysis output. Explain the significance of the r, p, F value;...
Consider the following excel regression analysis output. Explain the significance of the r, p, F value; Give the regression equation SUMMARY OUTPUT Regression Statistics MultipleR 0.875179 R Square 0.765938 Adjusted R Square 0.73668 3.802138 Standard Error Obserations 10 ANOVA MS egression Residual 8 115.65 14.45625 494.1 Total p-value ehzandard Error t Stat Lower 95% U e, 95% Lower950% Upper 9509e 75.4 2.08251736.2062 3.71058E-10 70.59770833 80.20229167 70.59770833 80 20229167 Interoept X Variable1 4.35 0.850184 -5.11654 0.000911066 6.310527365 -2.389472635 -6.310527365 -2.389472635
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Need help with 1-5
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1st regression analysis 2nd regression analysis 1. Analyze the two regression analysis's above ...
1st regression analysis
2nd regression analysis
1. Analyze the two regression analysis's above and make
a recommendation on if the organization should increase, decrease,
or retain their pricing and why?
2. What happens to the dependent variable Y if the price
X1 decreases in the second regression analysis?
SUMMARY OUTPUT Y=UNITS SOLD X=PRICE Regression Statistics Multiple R R Square Adiusted R S Standard Error Observations 0.874493978 0.764739718 0.756026374 159.2178137 29 quare ANOVA df MS Significance F 1 2224908.261 2224908.26187.76650338 5.64792E-10...
LA Real Estate Data. On a particular day in the spring, there were several properties for...
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0 Regression analysis Regression Statistics Multiple R 0.86 R Square 0.75 Adjusted R Square 0.70 Standard...
0 Regression analysis Regression Statistics Multiple R 0.86 R Square 0.75 Adjusted R Square 0.70 Standard Error 171.55 Observations 7 ANOVA of SS Significance F 0 .0120 Regression 1 M SF 435,336.22 29,429.90 435,336.22 147,149.49 14.79 Residual 6 582,485.71 Total Coefficients 709.81 0.29 Standard Error t 1,150.73 0.07 Stat 0.62 3.85 Lower P-value 95% 0.56 -2,248.24 0.010.09 Intercept X Variable 1 Upper 95% 3,667.85 0.48 Print Done E6-28A (similar to) Question Help Kim Meyer, owner of Tulip Time, operates a...
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The following is Excel output from a fitted linear regression model relating the sale price of...
The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 - 1.207517218 Standard Error Stat P-value 1.450657792 147.0819 0 0.029970869 -40.2897 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148749195 Refer to the information above on the regression using age to predict selling price. Which of the following gives the 95% confidence interval for by...
Use Excel to develop a regression model for the Hospital Database (using the “Excel Databases.xls” file...
Use Excel to develop a regression model for the Hospital
Database (using the “Excel Databases.xls” file on Blackboard) to
predict the number of Personnel by the number of Births. What can
you conclude from the study?
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.697463374
R
Square
0.486455158
Adjusted R Square
0.483861497
Standard Error
590.2581194
Observations
200
ANOVA
df
SS
MS
F
Significance F
Regression
1
65345181.8
65345181.8
187.5554252
1.79694E-30
Residual
198
68984120.2
348404.6475
Total
199
134329302
Coefficients
Standard Error
t Stat...
Consider the following excel regression analysis output. Explain the significance of the r, p, F value; Give the regression equation SUMMARY OUTPUT Regression Statistics MultipleR 0.875179 R Square 0.765938 Adjusted R Square 0.73668 3.802138 Standard Error Obserations 10 ANOVA MS egression Residual 8 115.65 14.45625 494.1 Total p-value ehzandard Error t Stat Lower 95% U e, 95% Lower950% Upper 9509e 75.4 2.08251736.2062 3.71058E-10 70.59770833 80.20229167 70.59770833 80 20229167 Interoept X Variable1 4.35 0.850184 -5.11654 0.000911066 6.310527365 -2.389472635 -6.310527365 -2.389472635
Need help with 1-5
× 215-QL Q Microsoft Word-QM215 Quiz t 4: SU2018 Q 0M2 15%20Quiz%204.pdf QM215 Quiz 4 Use the following output for Questions 1-5 A realtor built a regression model to explain the selling price of homes in a large Midwestern city. The variables are: PRICE -The sales price is measured in thousands of dollars. For example, a value of home that sold for $269,000 will have a value of 269 in this dataset. SQFT- The size of...
1st regression analysis
2nd regression analysis
1. Analyze the two regression analysis's above and make
a recommendation on if the organization should increase, decrease,
or retain their pricing and why?
2. What happens to the dependent variable Y if the price
X1 decreases in the second regression analysis?
SUMMARY OUTPUT Y=UNITS SOLD X=PRICE Regression Statistics Multiple R R Square Adiusted R S Standard Error Observations 0.874493978 0.764739718 0.756026374 159.2178137 29 quare ANOVA df MS Significance F 1 2224908.261 2224908.26187.76650338 5.64792E-10...
LA Real Estate Data. On a particular day in the spring, there were several properties for sale in Los Angeles. The dataset LARealEstate.xlsx on Blackboard contains the data used for this analysis (See Exhibit 1 for output). The relevant variables for this analysis are: 1. List Price: Saft Price the property is currently listed for Square footage of the living space To create the output yourself: .Excel: Data - Data Analysis- Regression, select the Y and X columns, including variable...
0 Regression analysis Regression Statistics Multiple R 0.86 R Square 0.75 Adjusted R Square 0.70 Standard Error 171.55 Observations 7 ANOVA of SS Significance F 0 .0120 Regression 1 M SF 435,336.22 29,429.90 435,336.22 147,149.49 14.79 Residual 6 582,485.71 Total Coefficients 709.81 0.29 Standard Error t 1,150.73 0.07 Stat 0.62 3.85 Lower P-value 95% 0.56 -2,248.24 0.010.09 Intercept X Variable 1 Upper 95% 3,667.85 0.48 Print Done E6-28A (similar to) Question Help Kim Meyer, owner of Tulip Time, operates a...
A real estate agent wants to use a multiple regression model to predict the selling price of a home in thousands of dollars) using the following four x variables. Age: age of the home in years Bath: total number of bathrooms LotArea: total square footage of the lot on which the house is built TotRms_AbvGrd: total number of rooms (not counting bathrooms) in the house The agent runs the regression using Excel and gets the following output. Some of the...
The following is Excel output from a fitted linear regression model relating the sale price of a home (Y, in thousands of dollars) to age of the home (X) in years. Intercept Age Coefficients 213.365436 - 1.207517218 Standard Error Stat P-value 1.450657792 147.0819 0 0.029970869 -40.2897 1.2E-278 Lower 95% Upper 95% 210.5209309 216.2099411 -1.26628524 -1.148749195 Refer to the information above on the regression using age to predict selling price. Which of the following gives the 95% confidence interval for by...
Use Excel to develop a regression model for the Hospital
Database (using the “Excel Databases.xls” file on Blackboard) to
predict the number of Personnel by the number of Births. What can
you conclude from the study?
SUMMARY OUTPUT
Regression Statistics
Multiple R
0.697463374
R
Square
0.486455158
Adjusted R Square
0.483861497
Standard Error
590.2581194
Observations
200
ANOVA
df
SS
MS
F
Significance F
Regression
1
65345181.8
65345181.8
187.5554252
1.79694E-30
Residual
198
68984120.2
348404.6475
Total
199
134329302
Coefficients
Standard Error
t Stat...
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