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Datasets Analysis Report Assignment

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Added on  2020-04-07

Datasets Analysis Report Assignment

   Added on 2020-04-07

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This report looks at two datasets on rented properties taken by students in Australia. It uses primary and secondary data to determine a few determinants of rent paid by students, in termsof the chosen suburb, the number of bedrooms as a proxy for size, type of property (flat or house) and bond amount paid for the property. As we are using a sample its usefulness is limited by the sampling technique, and the data collected.Section 1We first consider a primary dataset, obtained by an interview with students to know their weekly rent. This dataset is problematic as It is very small (only 5 points in it) It is not comparable to other secondary data that provides information on suburb, the number of bedrooms, category - flat or house, and bond amount paid.The other data is a secondary data- with 500 observations. It is rich in terms of additional information and can be used to make estimates campus wise or to determine association among variables. It can also be used for regression purposes to determine what affects rents in a significant way. Such conclusions can help students search for the right property based on their own requirements. A snapshot of this set is as follows:Bond AmountWeekly RentDwelling TypeNumber BedroomsPostcodeSuburb$2,700$675Flat32031RANDWICK$3,000$750Flat22031RANDWICK$1,540$385Flat22144AUBURN$2,360$590House32144AUBURN$2,600$650Flat12000SYDNEYSection 2We now use the primary data, to give a snapshot with numerical and visual help. weekly rent Mean160Standard Error24.2899156Median150Mode#N/AStandard Deviation54.31390246Sample Variance2950
Datasets Analysis Report Assignment_1
Kurtosis-1.952887101Skewness0.327662152Range130Minimum100Maximum230Sum800Count5It can be seen that the highest rent is $230, while the lowest is $120 only0 almost half of the maximum. The mean rent is $160 while median is $150. The data is limited, but a little skewed to the right. Section 3Next we consider dataset2, and look at the variable - Dwelling Type. The following points are clear:Most students prefer to live in flats- 474/500 or 94.8% in the sample.Parramatta is the most preferred location- 156/500 = 33.2% stay here The most dominated suburb is Sydney in terms of flats, with just 1/167 =0.5% staying in a house here. The other suburbs have a % of 17.8% , 5.7% and 2.9% staying in houses. The difference in flat and house residency is seen more starkly in a bar chart below. SUBURBFlatHouseGrand Total
Datasets Analysis Report Assignment_2
AUBURN601373PARRAMATTA1479156RANDWICK1013104SYDNEY1661167Grand Total47426500. We move to check the hypothesis that houses are preferred by less than 10% of students. = sample proportion of students in houses = 26/500 = 0.052Ho: p= 0.1H1: p < 0.1 ( left tail test) Z test value = (0.052 – 0.1)/ SE where SE = (0.052 *.948 /500)^.5 = 0.01Test value = - 0.042 /0.01 = -4.834. Using a 95% confidence level, the critical z value is -1.645. The p value of the test value is P (z < -4.834) = 0 as p value < 0.05, we conclude that we cannot accept the null hypothesis. There is statistical evidence that proportion of Houses is less than 0.1.This conclusion proves the data shown above in true in a statistical sense. It is not mere luck/sampling problem that share of flats is so high. The low share of houses is systematic, and may have deeper reasons which we are unable to see in this assignment. Section 4
Datasets Analysis Report Assignment_3

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