Simple Regression Analysis Results

REGRESSION STATISTICS
Multiple R	0.943
R square	0.889
Adjusted R square	0.878
Standard error	8.360
Observations	12.000
ANALYSIS OF VARIANCE (ANOVA)
	Degrees of Freedom	Sum of Squares	Mean Square	F -statistic	Significance of F -statistic
Regression	1.000	5601.111	5601.111	80.143	0.000
Residual	10.000	698.889	69.889
Total	11.000	6300.000
	Coefficients	Standard Error	t -statistic	P -value	Lower 95 percent	Upper 95 percent
Intercept	210.444	12.571	16.741	0.00000001	182.435	238.454
Price	1.578	0.176	8.952	0.00000434	1.970	1.185
RESIDUAL OUTPUT
	Observation	Predicted Q	Residuals	Actual Q (lbs.)	Actual P (cents/lb.)
	1	52.667	2.333	55	100
	2	68.444	1.556	70	90
	3	84.222	5.778	90	80
	4	100.000	0.000	100	70
	5	100.000	10.000	90	70
	6	100.000	5.000	105	70
	7	100.000	20.000	80	70
	8	107.889	2.111	110	65
	9	115.778	9.222	125	60
	10	115.778	0.778	115	60
	11	123.667	6.333	130	55
	12	131.556	1.556	130	50

Table 4.1 contains the results of a regression of Q (lbs.) on P (cents/lb.). Treat the variable Q (lbs.) as dependent variable, and P (cents/lb.) as independent variable, Using Table 4.1 to answer the following questions:

1. Present regression analysis results in a table form. Please round all numbers to 3 decimal places for easy reading. 2. Briefly explain: a) how different statistical measures (R square, Adjusted R square, and F-statistic) can be used to evaluate these regression results, and b) what do these measures indicate? 3. What is the estimated equation? 4. What is the estimate of intercept? Is the intercept estimate significant at the 95% level? How did you conclude this? 5. What is the P coefficient estimate? Is the P coefficient estimate significant at the 95% level? How did you conclude this? What does this P coefficient estimate tell us? 6. Calculate the price elasticity of demand for this demand curve at mean level. Please show your work. What does this price elasticity of demand indicate? 7. What is the predicted value of Q when P = 75? Use the estimated equation in to find the predicted value