(Q1) I have attached the dataset for Good Year Real estate home sales, with the selling prices in the first column. (a) Consider the variables "prices", bedrooms, "pool", size", "garage" and "baths". (b) Find out the correlation coefficients among all these variables. Do you see some high correlation between any two variables? correlation coefficients 0.467377 of prices and bedrooms correlation coefficients -0.20059 of pool and size correlation coefficients 0.221289 of garage and baths (c) Identify which variables are the dummy variables in your dataset. Pool and Garage (d) Next, you do multiple regression analysis. Using Excel, present a regression output between price as the dependent variable and all the other variables selected in (a) as independent variables. Include dummy variables from the dataset provided. 2 Price and bedroom porce and pool 3 price price and sze 24) price and garage price and baths 5 SUMMARY OUTPUT price and baths Regression Statistics Multiple R 0.382173 R Square 0.146056 Adjusted F 0.137765 Standard E 0.364917 Observatio 105 ANOVA df SS MS F ignificance F Regression 1 2.345936 2.345936 17.61679 5.75E-05 Residual 103 13.71597 0.133165 Total 104 16.0619 Intercept Coefficientstandard Err t Stat P-value Lower 95% Upper 95%.ower 95.0%/pper 95.0% 1.375992 0.171692 8.014289 1.8E-12 1.03548 1.716503 1.03548 1.716503 X Variable 0.003188 0.00076 4.197236 5.75E-05 0.001682 0.004695 0.001682 0.004695 (e) Interpret the estimated coefficients stating if they are statistically significant in explaining price of houses. Also interpret R squared. (You may write out the full estimated equation as shown in the lecture videos and then interpret coefficients). (f) Finally, present a neat report on the above findings. (For a nice report, follow some of the guidelines for report writing I have attached with this assignment).
(Q1) I have attached the dataset for Good Year Real estate home sales, with the selling prices in the first column. (a) Consider the variables "prices", bedrooms, "pool", size", "garage" and "baths". (b) Find out the correlation coefficients among all these variables. Do you see some high correlation between any two variables? correlation coefficients 0.467377 of prices and bedrooms correlation coefficients -0.20059 of pool and size correlation coefficients 0.221289 of garage and baths (c) Identify which variables are the dummy variables in your dataset. Pool and Garage (d) Next, you do multiple regression analysis. Using Excel, present a regression output between price as the dependent variable and all the other variables selected in (a) as independent variables. Include dummy variables from the dataset provided. 2 Price and bedroom porce and pool 3 price price and sze 24) price and garage price and baths 5 SUMMARY OUTPUT price and baths Regression Statistics Multiple R 0.382173 R Square 0.146056 Adjusted F 0.137765 Standard E 0.364917 Observatio 105 ANOVA df SS MS F ignificance F Regression 1 2.345936 2.345936 17.61679 5.75E-05 Residual 103 13.71597 0.133165 Total 104 16.0619 Intercept Coefficientstandard Err t Stat P-value Lower 95% Upper 95%.ower 95.0%/pper 95.0% 1.375992 0.171692 8.014289 1.8E-12 1.03548 1.716503 1.03548 1.716503 X Variable 0.003188 0.00076 4.197236 5.75E-05 0.001682 0.004695 0.001682 0.004695 (e) Interpret the estimated coefficients stating if they are statistically significant in explaining price of houses. Also interpret R squared. (You may write out the full estimated equation as shown in the lecture videos and then interpret coefficients). (f) Finally, present a neat report on the above findings. (For a nice report, follow some of the guidelines for report writing I have attached with this assignment).
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter4: Equations Of Linear Functions
Section4.5: Correlation And Causation
Problem 2AGP
Related questions
Question
Transcribed Image Text:(Q1) I have attached the dataset for Good Year Real estate home sales, with the selling prices in
the first column.
(a) Consider the variables "prices", bedrooms, "pool", size", "garage" and "baths".
(b) Find out the correlation coefficients among all these variables. Do you see some high
correlation between any two variables?
correlation coefficients
0.467377
of prices and bedrooms
correlation coefficients
-0.20059
of pool and size
correlation coefficients
0.221289
of garage and baths
(c) Identify which variables are the dummy variables in your dataset.
Pool and Garage
(d) Next, you do multiple regression analysis. Using Excel, present a regression output
between price as the dependent variable and all the other variables selected in (a) as
independent variables. Include dummy variables from the dataset provided.
2
Price and bedroom
porce and pool
3 price
price and
sze
24) price
and
garage
price
and baths
Transcribed Image Text:5
SUMMARY OUTPUT price and baths
Regression Statistics
Multiple R 0.382173
R Square
0.146056
Adjusted F 0.137765
Standard E 0.364917
Observatio
105
ANOVA
df
SS
MS
F
ignificance F
Regression
1 2.345936 2.345936 17.61679 5.75E-05
Residual
103 13.71597 0.133165
Total
104 16.0619
Intercept
Coefficientstandard Err t Stat P-value Lower 95% Upper 95%.ower 95.0%/pper 95.0%
1.375992 0.171692 8.014289 1.8E-12 1.03548 1.716503 1.03548 1.716503
X Variable 0.003188 0.00076 4.197236 5.75E-05 0.001682 0.004695 0.001682 0.004695
(e) Interpret the estimated coefficients stating if they are statistically significant in explaining
price of houses. Also interpret R squared. (You may write out the full estimated equation
as shown in the lecture videos and then interpret coefficients).
(f) Finally, present a neat report on the above findings.
(For a nice report, follow some of the guidelines for report writing I have attached with
this assignment).
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