# Business Decision Making - Essay Example

Summary
2.1 Property type Ave. price - Mean Ave. price - Mode Ave. price - Median 2 bedroom flat ?235,970 ?179,950, ?209,950, ?215,000, ?229,950, ?345,000 ?215,000 3 bedroom flat ?260,475 ?249,950, ?250,000, ?262,000, ?279,950 ?256,000 2 bedroom terrace house ?364,999.67 ?…

## Extract of sample "Business Decision Making"

Download file to see previous pages The price for the terrace house with 3 bedrooms and 2 bathrooms is ?395,000. The average price for 2-bedroom terrace house with 1 bathroom is ?364,999.67 with median ?370,000 which is not far from the mean. In this case, the ultimate basis for the decision as to how much will be the budget for the terrace house with 4 bedrooms and 4 toilets will be the mean and median. Notice how much increase was incurred from the mean of 2-bedroom terrace house with 1 bathroom to 3-bedroom terrace house with 2 bathrooms. The difference in the mean is ?30,000 and the difference in the median is ?25,000. In this case, there is a remarkable basis to approximate how much will be the probable price for a 4-bedroom terrace house with 4 toilets. Adding these remarkable differences to ?395,000, then the average price-mean for a 4 bedroom terrace will be 420,000 and the average median-price will be approximately ?425,000. These are all approximate values, but the trend for the actual employed values is taken into account. 2.3 There are many ways on how to analyse data using measures of dispersion. The measure of dispersion will inform us whether a distribution is normal or abnormal (Rachev et al., 2005; Rubin, 2012). For this reason, a statistical analyst can generate this idea by solving the value of skewness and kurtosis using the following formula (Basu, 2009; Celsi et al., 2011). Sk = [3(x – Md)/SD, where x is the mean, Md is the median and SD is the standard deviation. Ku = Q/(P90 – P10) where Q = (Q3-Q1)/2, and P90 and P10 are corresponding percentile ranks. Below are the computed values for dispersion using the given data. Property type Skewness Kurtosis 2 bedroom flat 1.19 2.89 3 bedroom flat 0.69 1.85 2 bedroom terrace house -0.59 1.50 3 bedroom terrace house 0 0 3 bedroom semi-detached house 0.60 1.80 The rule states that if the skewness is equal to zero and kurtosis equals 0.265, then the distribution is normal or the dispersion is most likely following a normal distribution. The data are equally distributed from its central location like mean or median. In the above given data, it seems that most data are highly dispersed to the right, and with high vertical dispersion because each kurtosis tends to be leptokurtic in nature because of the positive value. On the other hand, a correlation coefficient cannot only test the relationship between the data sets, but the degree of their variation, and at some point this will have meaningful implication on their actual dispersion. The table below shows the correlation values generated from the data sets concerning the price, and the number of bedrooms and bathrooms. Correlation Number of bedrooms Number of bathrooms Price 0.42 0.13 The generated values as shown in the above table only shows that there is a significant point to justify the information generated on the data’s skewness and kurtosis, because the poor correlation signifies that the given values are varied and most likely not to follow a certain pattern or trend. 2.4 Based on the information from 2.3, skewness and kurtosis are obtained by employing the values for quartiles and percentiles. These means that quartiles and percentiles have strong role to play to help us identify the actual level of dispersion of the data, because they all have the necessary information to tell us something about the distribution of the data sets and the actual variation. In other words, they have the credibility to inform us of ...Download file to see next pagesRead More
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