Let assume that you are going to purchase a house you know that the more space...

Let assume that you are going to purchase a house you know that the more space you have the more the house is going to cost. You decide to collect some data.

square feet. Price in kilo dollars

1400.                         200
1900.                         245
1900.                         260
2100.                         300
2250.                         310
1450.                         190
2300.                         400
3100.                         525

Simple linear regression: regression between one independent variable and a single independent variable.

We can say the data has a positive correlation but what does that mean?

What would you expect to pay for a 1600 square foot house?

Expert Solution

Solution:

We can use the excel regression data analysis tool to find the least-squares regression equation. The excel output is given below:

SUMMARY OUTPUT

Regression Statistics
Multiple R	0.968968092
R Square	0.938899163
Adjusted R Square	0.92871569
Standard Error	29.82279427
Observations	8

ANOVA
	df	SS	MS	F	Significance F
Regression	1	82001.10565	82001.10565	92.19832751	7.29798E-05
Residual	6	5336.394349	889.3990581
Total	7	87337.5

	Coefficients	Standard Error	t Stat	P-value	Lower 95%	Upper 95%
Intercept	-107.7610565	44.13481929	-2.441633573	0.050359792	-215.7550687	0.232955652
Square feet	0.200737101	0.020905768	9.601996017	7.29798E-05	0.149582529	0.251891672

Therefore, the regression equation is:

We can say the data has a positive correlation but what does that mean?

Answer: It means as the square feet for the house increases, the price in Kilo dollars also increases

What would you expect to pay for a 1600 square foot house?

orchestra answered 2 years ago

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