Bbs300 The Real Estate Market Assessment Answers

Answer:

The real estate market conditions were analyzed by the researcher in the data analysis segment. The dependence of selling price of properties on the distance of the house to the nearest train station and the nearest bus stop was investigated. The report has been presented as follows.

Variable Exploration

1. Price

The selling price of the real estate properties (M = $ 886580. SD =$ 324950) was noted to be approximately normally distributed (SKEW = 0.43). The median of selling price was identified to be at $ 852 and was noted to be less than the mean value.  With 95% confidence, the average selling price for real estate properties in Australia was estimated to be within [$ 824200,

The variable (Price) was plotted in a histogram and has been presented in Figure 1. The normal line has also fitted the histogram. The variable was noted to follow Gaussian nature with almost near zero skewness. The confirmatory tests was conducted using Shapiro-Wilk (W = 0.98, p = 0.12) and Kolmogorov-Smirnoff (S = 0.06, p = 0.20) tests. The null hypothesis failed to get rejected at 5% level of significance, and it was concluded that the price of the real estate properties was normally distributed (Corder, & Foreman, 2014).

1. Lot Size

Lot size of the real estate properties (M = 1175.23 Sq.M, SD = 372.90 Sq.M) was noted to be have a positive skewness (SKEW = 0.84). The median of lot sizes was identified to be at 980 square meters and was noted to be highly less than the mean value.  With 95% confidence, the average lot size for real estate properties in Australia was estimated to be within [1107.26, 1242.16] square meters.

The lot size was plotted in a box-plot and has been presented in Figure 2. The variable was noted to be significantly positively skewed. The confirmatory tests was conducted using Shapiro-Wilk (W = 0.86, p < 0.05) and Kolmogorov-Smirnoff (S = 0.23, p < 0.05) tests. The null hypothesis got rejected at 5% level of significance, and it was concluded that lot sizes of the real estate properties were not normally distributed.

1. Material

The most used material for the real estate properties was noted to be Veneer (Median = 2) and was also prominent to have a positive low skewness (SKEW = 0.10). The variable was categorical in nature, and the median was considered as the proper descriptive statistic. With 95% confidence, the estimated interval was found to be [1.81, 2.09] which contained 2 (Veneer) as the estimated material for real estate construction.  

The material was plotted in a histogram and has been presented in Figure 3. The distribution of this categorical variable was noted to be not normal. The confirmatory tests was conducted using Shapiro-Wilk (W = 0.86, p < 0.05) and Kolmogorov-Smirnoff (S = 0.25, p < 0.05) tests. The null hypothesis got rejected at 5% level of significance, and it was concluded that material of the real estate properties was not normally distributed.

1. Condition

The most observed condition for the real estate properties was noted to be good (Median = 3) and was also prominent to have a low negative skewness (SKEW = - 0.07). The variable was categorical in nature, and the median was considered as the proper descriptive statistic. With 95% confidence, the estimated interval for the average condition was found to be [2.43, 2.77] which contained 3 (Good condition) as the estimated condition for real estate structures.

The condition was plotted in a histogram and has been presented in Figure 4. The distribution of this categorical variable was noted to be not normal. The confirmatory tests was conducted using Shapiro-Wilk (W = 0.88, p < 0.05) and Kolmogorov-Smirnoff (S = 0.20, p < 0.05) tests. The null hypothesis got rejected at 5% level of significance, and it was concluded that condition of the real estate properties was not normally distributed.

Answer 2

The price of the real estate properties was observed earlier to be normal in nature, whereas the distance from the train (To train) (W = 0.96, p < 0.05) and bus (To bus) stations (W = 0.93, p < 0.05) were found to be not normally distributed.

Price of the properties was found to have a low negative and insignificant (r = -0.024, p = 0.796) correlation with a distance of the properties from the nearest railway station. Price of the properties was found to have a low negative and insignificant (r = 0.003, p = 0.974) correlation with the distance of the properties from the nearest bus station.

To assess the reliance of price on distance from bus and railway station, a backward regression model was constructed. The assumptions were also cross-checked for the validity of the model. From Table 12 it was evident that none of the models were able to explain the dependent variable, the price of the real estate properties significantly. The coefficient of determinations was almost zero for the three models (Landers, 2015).

From Table 13 it was noted that all the three regression models were insignificant in nature. Hence, none of the independent variables were the valid predictor of the models.

From the linear model it was found that the coefficients of the predictors were statistically insignificant, and therefore linear relation between the variables was not feasible. The co-linearity statistics were measured by VIF, where the VIF values were found to be equal to one. Hence, the multi-co-linearity was not present in the regression models (Park, 2015).

For the first regression model, the residual plot in Figure 5 was analyzed. It was found that the horizontal spread of the plot was between -2 and 2, whereas the vertical spread was between -3 and 3. Hence, variances of the error terms were spread across the independent variables in a similarity. Homoscedasticity assumption of the regression model was satisfied (Bolin, 2014).

Answer 3

The satisfaction levels of the employees were verified for the reliability of the thirteen statements of the dataset. Cronbach's alpha was evaluated for the purpose, and the item deleted matrix was also analyzed (Yockey, 2017). The value of Cronbach's alpha was evaluated as 0.92 with 13 items, which indicated that the responses were highly and significantly reliable for the thirteen questions. The inter-item correlation matrix was constructed and provided in Table 17. From the particular matrix question number 8 was identified separately for possessing low correlations. The correlations of Q8 with other items were considerably low for reliability purpose. Probably, Q8 was denoting or representing some other aspects or views other than the rest of the items. Q11 was had low correlations with other items, especially with Q3 and Q6. Other than Q8, rest of the items was noted have considerable correlations with other items.

 the item deleted reliability matrix significance of the items was clear. From Table 18 it was noted that Q2, Q4, Q5, Q7, Q12, and Q13 were discovered as the most significant impact factors in the reliability of the dataset. Deletion any one of these items would have reduced the reliability of the dataset. Q1, Q3, Q6, Q9, Q10, and Q11 were those factors, where removing them one at a time did not affect the overall reliability of the dataset. But, the item having a different orientation than other items was observed to be Q8. Deletion of Q8 obtained Chronbach’s alpha = 0.93. Hence, the observation of Q8 as a differently oriented factor other than the rest of the items was justified from the inter-item correlation matrix (Bonett, & Wright, 2015).

References

Bolin, J. H. (2014). Hayes, Andrew F.(2013). Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression?Based Approach. New York, NY: The Guilford Press. Journal of Educational Measurement, 51(3), 335-337.

Bonett, D. G., & Wright, T. A. (2015). Cronbach's alpha reliability: Interval estimation, hypothesis testing, and sample size planning. Journal of Organizational Behavior, 36(1), 3-15.

Corder, G. W., & Foreman, D. I. (2014). Nonparametric statistics: A step-by-step approach. John Wiley & Sons.

Landers, R. (2015). Computing Intraclass Correlations (ICC) as Estimates of Interrater Reliability in SPSS, The Winnower 2: e143518. 81744, 2015, DOI: 10.15200/winn. 143518.81744 Landers This article is distributed under the terms of the Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and redistribution in any medium,

provided that the original author and source are credited. Recently, a colleague of mine asked for some advice on how to compute interrater reliability for a coding task, and I discovered that there aren’t many resources online written in an easy-to-understand.

Park, H. M. (2015). Linear regression models for panel data using SAS, Stata, LIMDEP, and SPSS.

Yockey, R. D. (2017). SPSS demystified. Taylor & Francis.