COMM
Asst1_2013

# Are list price type of dwelling apartment townhouse

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are: list price, type of dwelling (apartment, townhouse or house), number of bedrooms, number of full baths, number of half baths, and total number of square feet. I have prepared two versions of the spreadsheet to save you the time required to reformat and sort the data so that you can work on the statistical issues. Halifax_RealEstate = one combined set of data for all 98 listings, sorted from highest to lowest list price Halifax_ByType = separate data for each of the three types of dwellings. Variables are list price, bedrooms and total area; variables called full baths and half baths are deleted. For parts a), b) and c), use Halifax_RealEstate. a) Create a histogram for the list price of the combined set all 98 listings. b) For the list price , compute the mean, standard deviation, median, minimum and maximum of the combined set. What do you notice about the relative values of the mean and median? What is the reason for this difference? c) For the list price , compute the lower and upper quartiles of the combined set. Identify any outliers (i.e. points above the upper inner fence or below the lower inner fence). For parts d), e), f), and g) use  Halifax_ByType. d) For list price , compute the mean, standard deviation, median, minimum and maximum for each type of dwelling. Report the data in table format, with three rows corresponding to the dwellings and five columns corresponding to the numerical summaries. e) Repeat part d), for total area . f) Create side-by-side box plots of list price only for the three types of dwellings. You may use an Excel macro or a hand-drawn plot if it is neatly drawn! g) Create a table of Type of Dwelling as the row variable by Number of Bedrooms as the column variable, with the frequencies (i.e. counts) as the cell entries. Convert this table to a table of percentages; it is up to you to decide which is the more relevant – row or column percentages. *** END *** 4
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• Spring '10
• E.Fowler
• list price

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