A: Predict the average price of a 3-bedroom home that is 10 km from the city with 300 square metres of land that sold in 2014 if it is appropriate to do so. Show the predicted regression equation. (1 Mark)

B: Do the results suggest that the data satisfy the assumptions of a linear regression: Linearity, Normality of the Errors, and Homoscedasticity of Errors? Show using scatter diagrams, normal probability plots and/or histograms and Explain. (2 Marks)

C: Would these results tell us anything about the affordability for non-investor purchasers of these properties? If not, describe a scenario in how you would construct a sample of households that are looking to occupy a home. (1 Mark

here is table describing the variables in the data set:

Variable	Definition
Price	Transaction price of home sale
Bedrooms	Number of bedrooms in home
land_area	Area of land of property in square metres
Meldist	Distance of home to Melbournes CBD in km.
2014	Home sold in 2014
2013	Home sold in 2013
2012	Home sold in 2012
2011	Home sold in 2011
2010	Home sold in 2010

this is excel Microsoft 2011 and use this in your excel

price	bedrooms	land_area	meldist	2014	2013	2012	2011	2010
320000	3	585	62.2	0	0	0	1	0
700000	4	2885	42	0	0	1	0	0
440000	4	559	24.6	1	0	0	0	0
220000	1	1294	5.2	0	0	1	0	0
610000	3	669	58.9	1	0	0	0	0
380000	3	650	41.9	1	0	0	0	0
330000	3	559	20.9	1	0	0	0	0
1460000	3	784	12.9	0	1	0	0	0
490000	4	643	21.1	0	0	1	0	0
605000	2	230	6.8	0	0	1	0	0
265000	3	568	30.6	0	0	0	0	1
440000	3	165	42.4	0	0	1	0	0
297500	3	576	22.3	1	0	0	0	0
418000	3	640	32	0	0	0	0	1
580000	3	655	11.1	0	1	0	0	0
340000	3	534	18.7	1	0	0	0	0
300000	3	603	14.1	1	0	0	0	0
485000	3	828	35.3	0	0	0	1	0
1252000	3	651	13.5	1	0	0	0	0
500000	1	1179	3.7	0	1	0	0	0
580000	3	626	7.8	0	0	1	0	0
520000	4	588	23.5	0	1	0	0	0
475000	2	556	10.1	0	0	0	1	0
570000	5	825	46.9	0	1	0	0	0
525000	4	537	14.4	1	0	0	0	0
569000	2	299	16	0	0	0	0	1
898000	4	697	13.2	0	0	0	1	0
575000	4	748	59.1	0	0	1	0	0
347000	1	604	6	0	0	0	1	0
730000	3	791	65.7	0	1	0	0	0
460000	3	352	62.3	0	0	0	1	0
500000	4	555	60.9	0	1	0	0	0
550000	3	1128	44.9	0	1	0	0	0
295000	3	470	30.9	1	0	0	0	0
540000	4	583	47.6	0	1	0	0	0
370000	3	273	20.1	0	0	0	0	1
770000	3	674	13.7	1	0	0	0	0
1025000	3	172	5.3	0	0	0	0	1
355000	3	593	62.7	0	0	0	1	0
578000	5	540	22.1	0	0	1	0	0
380000	3	550	21.4	0	0	0	0	1
318000	3	574	25.1	0	0	0	0	1
870000	3	354	5.6	0	0	0	0	1
292000	3	196	6.7	0	0	1	0	0
1205000	3	467	12.2	0	1	0	0	0
495000	3	6832	43.7	1	0	0	0	0
380000	4	641	36.5	0	0	0	1	0
380000	3	172	37.3	0	0	0	1	0
492000	4	640	27	1	0	0	0	0
507000	3	591	23.6	0	0	0	0	1
828000	3	129	2.5	1	0	0	0	0
395000	3	522	37.4	0	1	0	0	0
469550	3	744	23.7	0	0	0	0	1
805000	3	372	47.3	1	0	0	0	0
807500	4	1000	26.2	0	1	0	0	0
648000	4	663	25.2	1	0	0	0	0
599000	3	654	17	0	0	0	1	0
492000	3	779	32.4	1	0	0	0	0
475000	3	685	27.5	0	0	1	0	0
1100000	4	7882	26.8	0	0	1	0	0
307500	2	227	44	0	1	0	0	0
485000	3	373	14.3	0	1	0	0	0
880000	4	695	21.8	1	0	0	0	0
761000	2	134	3	0	0	1	0	0
230000	3	396	16.6	0	1	0	0	0
1400000	3	753	14.3	0	0	0	1	0
438000	3	865	27	0	0	1	0	0
140000	3	268	20	0	0	0	0	1
560000	5	1192	32.3	0	0	0	0	1
500000	3	383	11.8	0	0	0	0	1
577500	3	161	47	0	0	0	0	1
741500	3	156	6.7	0	0	0	1	0
1642900	4	5827	22.7	0	0	0	0	1
570000	3	615	10.7	0	0	0	1	0
645000	4	832	50.2	0	1	0	0	0
600000	5	723	25	0	1	0	0	0
1170000	4	776	9.5	0	0	0	0	1
630000	4	1370	21.2	0	0	0	1	0
650000	2	185	5.4	0	0	0	0	1
384500	3	490	17.2	0	0	1	0	0
466000	3	247	13.7	0	0	1	0	0
470000	3	728	57.1	0	0	0	0	1
410000	3	1021	34.8	0	0	1	0	0
305000	3	549	40.4	0	0	1	0	0
592000	3	804	21.6	1	0	0	0	0
1051000	4	717	21.3	1	0	0	0	0
502000	3	104	12.3	0	0	0	0	1
850000	4	500	54.9	0	1	0	0	0
180000	4	958	37	0	0	0	0	1
519000	3	621	15.3	0	1	0	0	0
744000	4	381	16	1	0	0	0	0
520000	4	863	28.2	0	0	1	0	0
520000	3	1754	42.5	0	1	0	0	0
274500	3	582	36.3	0	0	0	1	0
512500	2	587	6.4	0	0	0	0	1
662000	4	344	16.4	0	0	0	1	0
1600000	4	797	5	0	0	0	1	0
482000	3	241	30.3	0	0	0	1	0
485000	1	149	3	0	0	1	0	0
361000	1	622	4.8	1	0	0	0	0