Hi I need help with the following question please (I have attached the formula sheet below) Question 4

A councilor in a new residential area of outer Western Sydney is reviewing apartment floor areas and sale prices to determine if he could estimate the sale price from the apartment floor area. The area (square metres) and selling price (thousands of dollars) was collected for a sample of 10 recent sales of apartments as follows:

Area (m2)	460	500	540	540	580	610	620	680	730	790
Price ($'000s)	230	236	242	246	200	250	256	262	250	274

The following partial Excel output was determined.

		Coeff	Stand Error	t Stat	P-value
	Intercept	170.701	32.628	5.232	0.001
	Area (m)	0.122	0.053	2.295	0.051

(a) What is the equation of the regression line to predict sale price from floor area?

(b) What does the model predict will be the sale price of a comparable apartment in the same location with a floor area of 750 m?

TOPIC 7A Simple Linear Regression Analysis Variance of x Variance of y Covariance between x and y Ex- - nx- SSx SS. Exy - nxy Sxy SSxy n - 1 n - 1 n - 1 n - 1 n - 1 n - 1 The Simple Linear Regression Line 9 = Bo + Bix Slope coefficient estimate BI = 2 SSxy or Bi = SS, y-intercept estimate Po = y - BIT Sum of squares for error SSE = (n - 1) $3 5xv) or SSE = $5, SSx Standard Error of the Estimate SSE n - 2 Standard error of B1 or Test statistic for the slope t = Bi-B d.f = n- 2 Confidence Interval for the population slope Bittn-20/25Bi Coefficient of Determination SSE R2 = 1 - SxY) R2 = - SS 2 (n - 1)5% 2 2 (SSx) (SSy) SS r= Sample Coefficient of correlation or SPST (SS )(SSy) Confidence Interval for the 1 (xg - x)2 D+ tajSe Expected Value of y (n - 1)S Prediction Interval for the y + tase It- (29 - 1)2 value of y n (n - 1)sExample of an Excel Output SUMMARY OUTPUT Regression Statistics Multiple R 0.763 coefficient of correlation R Square 0.582 coefficient of determination Adjusted R Square 0.581 Standard Error 7.306 standard error of the estimate Observations 675 Coefficients Standard Error Stat P-value Intercept -5.086 .086 4.682 0.000 Assessment 0.987 0.032 30.611 0.000 Bo BI TOPIC 7B Index Numbers P1 100 Simple Price Index Po Average relative price index I, = Pli x 100 Poi Simple Aggregate Price Index I= 100 Poi Laspeyres Index 1 1 = - 100 Paasche Index .100 Real Price = Current Price X Base Year CPI Real Price Current Year CPI 200032 Statistics for Business Page 13