Comparison of Weekly Rent between Suburbs and Its Relationship with Bond Amount
Added on 2019-11-26
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Surname 1First name(s), Family name Student IDData statisticsSection 1: IntroductionResearch is important in understanding the direction of the association and the effect of some variables tothe dependent variables. This paper is designed to assess factors that affect the cost of accommodationaround Australia. Thus, four suburbs Sydney, Randwick, Parramatta, and Auburn in Sydney Metro willbe assessed. The key focus is pinned on dwelling type, number bedrooms, and suburb. The primaryobjective of the research is to determine whether these factors have a significant effect on the bondamount and the monthly rent. That is, appropriate hypothesis tests will be carried out to determinewhether this factor significantly influences the rent and bond amount.The researcher collected secondary data, as they were obtained from other person surveys. A sample of500 was used which was sufficiently large to make inference about the population from which they weredrawn from [ CITATION ohn14 \l 1033 ]. The sample was drawn randomly, implying that they werenormally distributed. This is accordance with [ CITATION Bes16 \l 1033 ] a large sample drawnrandomly imitated the characteristics of the original population. The research will focus on the residentialrental cost in the three suburbs Sydney, Parramatta, Auburn, and Randwick. The research will check onthe dwelling type of the house either a house or a flat, and also the bond amount. Both descriptive andinferential statistics will be computed. The average rental cost of all the dwelling places will be assessedwhether it significantly differs in the three suburbs. Lastly but not least an assessment of the nature, andstrength of association between bond amount and the Section 2: International Students’ Weekly RentDescriptive statistics were run in excel to illustrate the measure of central tendency and the measure ofdispersion. The distribution of the weekly rent was illustrated using histogram and a box chart as shownin Figure 1.2003004005006007008009001,0001,100051015202530HistogramWeeklyRentPercent9409609801000102010401060BoxPlotWeeklyRent Figure 1: Distribution of weekly rent
Surname 2The chart illustrates that the weekly rent has a long tail to the right or they are positively skewed. Also,the boxplot indicates that there are outliers on the upper side of the boxplot. The descriptive statistics forthis variable was run and the results are as follows. Table 1: Descriptive statisticsWeekly Rent Count500 Mean579.45 Sample standarddeviation166.69 Sample variance27,786.16 Minimum250 Maximum1050 Range800 1st quartile460.00 Median552.50 3rd quartile651.25 Interquartile range191.25 Mode550.00 low extremes0 low outliers0 high outliers20 high extremes0 The statistics indicate that on average $579.45 was paid as weekly rent, with a standard deviation of$166.69. The summary indicates that there are 20 outliers on the higher side of the data. However, thevalues indicate that there are no higher extremes values which might cause an alarm in the validity of thedata. The interquartile value shows that the difference between the upper 75% and the lower 25% is$191.25. Lastly, the summary shows that the minimum weekly rent was $250 and a maximum of $1,050.Section 3: Rental Bond Board Property Data – Dwelling TypeFirst, an assessment of the dwelling-place type was carried out. The bar chart of this variable was plottedand since the data was categorical in nature, the frequency distribution was computed. FlatHouse05010015020025030035040045050046238Count of DwellingType Total
Surname 3Figure 2: Count of Dwelling TypeThe plot indicates that most of the dwelling types are flats with 462, and only 38 houses. This implies thatonly 7.60% of the dwellings were houses, and 92.40% of the dwelling are flats. A test of the hypothesis was conducted to determine whether the proportion of House dwelling type isless than 10% at the 5% level of significance. The z-score normal distribution for test of proportion willbe used to test the following hypothesis. This is a one-tailed test of hypothesis.Table 2: Z Test of Hypothesis for the ProportionDataNull Hypothesis p =0.1Level of Significance0.05Number of Items of Interest38Sample Size500Intermediate CalculationsSample Proportion0.076Standard Error0.0134Z Test Statistic-1.7889Lower-Tail TestLower Critical Value-1.6449p-Value0.0368Reject the null hypothesisThe summary shows that there is sufficient evidence to support the claim that the proportion of housedwelling type are less than 10%. This means that the number of houses in the suburbs is less than 10% ofthe number of houses rented. With 95% confidence, we can state that there are less than 10% of thenumber of houses rented in the Sydney suburbs. An analysis of the
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