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# What would be your best guess of the asking price for a 6000-square-foot house

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My brother would like to retire and move to a quieter place, probably away from me. I suggested area around Lake Louise.
But he had a more remote location in mind......

He did some housing market research in a secret destination, and would like to have me taking a look at his data.
2100 464990
8790 759990
2980 529990
850 125990
2450 519990
1650 369990
1050 179990
950 285990
1250 349990
900 99990
1450 249990
1560 339990
1990 449990
2250 599990
1490 124990
2580 579990
3560 589990
1650 369990
3880 599990
1870 399990
4050 549990
4200 619990
4380 589990
5100 639990
7340 714990
2750 499990
9550 799990
7990 729990
8550 799990
1390 619990
9240 829990
9550 839990
1350 169990
1560 139990
970 59990
790 234990
5550 689990
3150 219990
6990 699990
2150 495990
My hunch was that he collected quite a few house sale ads and took down the asking price and the usable square footage of each house offered. He might have performed a linear regression on the data, but he did not quite believe the price for a 6000-square-foot house he obtained from his analysis. He asked me to take a look at his data.

Take a hard look at the trend and behavior of his data. Perform whatever linear regression on the data you want. What can you say about the data set?
What would be your best guess of the asking price for a 6000-square-foot house?
By the way, since we are pursuing a rather concept understanding of the given data set, you should feel free to exploit available technology in the examination of the data. Maybe the linear regression applets given in the Additional Web Resources section can prove to be helpful. I guess my brother did not want me to free load on him, and that was why he did not tell me where his data came from.

Well, his peace and quiet was broken upon my visit in my family sedan. Yes, he could run, but he could not hide. I took a bottle of reserved Bordeaux from his cellar and enjoyed the northern light that evening.