The above dataset can be divided into two populations each having a different size and mean. After the above dataset was divided (Dataset 1: within suburbs Dataset 2: outside suburbs) on the basis of the distance, the immediate findings were as follows:The above are ALL descriptive statistics pertaining to the data. These are the results of simple functions applied to the dataset without any form of inferences being applied. As a matter of example, the calculations tell us that the average price of a house within the suburbs of the city is $232. It does not tell us whether this is high, low or average in comparison to any other city or change in location. It is a standalone number with little inferential value.“The average property value is higher if located closer to the city than the average property value if located in the suburb of the city. This allows us to prove this statement with facts such as higher gas mileage making city living more attractive due to a shorter commute.The above are the inferential statistics (z-scores) that have been calculated as a result of the descriptive statistics calculated earlier. The comparison of these statistics yield a result – a formidable conclusion that can be made at the end of the research.Our next step will be the comparison of the z-calculated statistic with the z-tabulated statistic. It should be remembered that this is a right-tailed test which means that the hypothesis will be rejected if the z-calculated statistic is greater than the z-tabulated statistic.The two population test carried out on the Real Estate dataset at the 95%. Descriptive and Inferential Statistics.
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