Rephrase The Whole Research
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Empirical Research techniques for Business
Q1a) Price
There are a total of 120 samples, of which, 100% of the observations are regarded by SPSS.
Based on the descriptive statistics, the mean price of a house in AUD (In $‘000s) is 886.575, with a 29.6344 standard error from the mean of the population. The confidence interval of 95% of the mean is between the upper bound of 945.3116 and the lower bound of 827.8384.
The trimmed mean value is 876.7778, which is very close the mean value of 886.5750. This shows that there is a low influence of the extreme values. The median price of a house is 852.000, which means that the distribution score is skewed slightly towards the lower end of the distribution, with a value of 0.426 as shown in the figure on the left. The minimum value of a house in the population is 192.00 and the maximum value of a house in the population is 1761.00. The value of the standard deviation is 324.94666, which is rather high in this context and means that the prices of the houses are spread out over a wide range of values.
The box plot indicates that the distribution is skewed towers the left of the distribution graph since the box is closer towards the lower end of the graph. In this case, there is also an individual large outlier. Which is indicated at the top of the line.
As for the Normality test, the significance of Kolmogorov-Smirnov is indicated at 0.200 and the significance of Shapiro-Wilk is at 0.116. Since both of these values are above 0.05, the data can be regarded as being normally distributed.
The histogram also shows that it is asymmetric and is skewed more towards the left and is not normally distributed.
Finally, the observed value of each house plotted against the expected value of the normal distribution does not show a reasonably straight light, which is evident that it is not a normal distribution.
1b) Lot Size
As for the Lot Size for the housese. There are a total of 120 samples, and 100% of the observations are regarded by SPSS.
The descriptive statistics for the Lot size suggests that the mean size of a house in the data set is 1175.2250, With a 34.041 standard error from the mean of the population. The confidence interval of 95% of the mean is between the upper bound of 1242.6296 and the lower bound of 1107.8204.
The trimmed mean value is 1161.3796, which is very close the mean value of 1174.2250. This indicates that there is a low level of influence of the extreme values. The median size of a house is 980.00, which means that the distribution score is skewed towards the lower end of the distribution, with a value of 0.838 as shown in the figure on the left. The minimum size of a house in the dataset is 632.00 and the maximum size of a house in the population is 1950.00. The value of the standard deviation is 372.90043, which is rather high in this context and means that the sizes of the houses are varies over a wide range of sizes.
The box plot indicates that the distribution is skewed towers the left of the distribution graph since the box is closer towards the lower end of the graph. In this case, there are no outliers.
As for the Normality test, the significance of Kolmogorov-Smirnov is indicated at 0.000 and the significance of Shapiro-Wilk is at 0.000. Since both of these values are below 0.05, the data can be regarded as not normally distributed
The histogram also shows that it is asymmetric and is skewed more towards the left as left side of the graph indicates extremities at the left. Therefore, it is not normally distributed.
The QQ plot also shows a large number datasets falling out of line when plotted against the expected value of the normal distribution. Since the line is not straight, it goes to show that it is not a normal distribution.
Q1c) Material
Material
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Timber
45
37.5
37.5
37.5
Veneer
36
30.0
30.0
67.5
Brick
39
32.5
32.5
100.0
Total
120
100.0
100.0
Since material is considered nominal scale, the data from the dataset cannot be measured. However, what we can gather from the descriptive stats is that out of the entire population of 120 houses, 45 houses use Timber, which is 37.5 percent of the population. 36 houses use Veneer, which is 30% of the population. As for the rest of the 39 house, Brick is the material used. Which is 32.5%.
Condition
Frequency
Percent
Valid Percent
Cumulative Percent
Valid
Very Poor
15
12.5
12.5
12.5
Poor
40
33.3
33.3
45.8
Good
42
35.0
35.0
80.8
Excellent
23
19.2
19.2
100.0
Total
120
100.0
100.0
Q1d) Condition
The condition of the houses is considered ordinal and the data from the data set cannot be measured. But what we can gather from the descriptive stats is that 15 houses are in poor condition, which makes up 12.5% of the population of 120 houses. 40 houses are in poor condition, which makes up 33.3% of the population. The majority of the houses are in good condition, with a total of 42 of them, making up 35% of the population. 23 houses are in excellent condition, which is 19.2% of the total population.
Q2)
Results from the normality test for distance to train, shows that the value of the significance of Kolmogorov-Smirnov and the value of the significance Shapiro-Wilk are 0.01 and 0.002 respectively. This indicates that it is not normally distributed since both values are lesser than 0.05.
The Histogram for distance to Train also appears to be asymmetric since a large portion of the values goes to high extremities. Therefore, it goes to show that it is not normally distributed.
Correlations
Price
To Train
Price
Pearson Correlation
1
.003
Sig. (2-tailed)
.974
N
120
120
To Train
Pearson Correlation
.003
1
Sig. (2-tailed)
.974
N
120
120
The results from the Pearson Correlation also indicates that there is very weak relationship between the distance to the train station and the price of the houses.
Therefore, the distance to the train station barely affects the price of the houses.
Using the scatter plot to further test the hypothesis, it is evident that there is a weak linear relationship between the prices of the house and the distance of the house to the train station.
As for the Kolmogorov-Smirnov and Shapiro-Wilk normality test for distance to bus station, it is evident that it is not normally distributed since both results show that their significance are zero.
The Histogram for distance to bus station also appears to be asymmetric since a there are large extremities at both ends of the graph. Therefore, it is not normally distributed.
Correlations
Price
ToBus
Price
Pearson Correlation
1
-.024
Sig. (2-tailed)
.796
N
120
120
ToBus
Pearson Correlation
-.024
1
Sig. (2-tailed)
.796
N
120
120
As for the Pearson Correlation results between the price of the house to the distance to the bus station, it shows a very weak negative relationship at only negative 0.024.
The Scatter Plot results of the prices of the houses and the distance to the bus stations shows that the data points are spread unevenly on the diagram with no patterns. Therefore, there is little correlation or a weak Linear relationship.
Q3)
The Cronbach’s Alpha from the reliability statistic shows a result of 0.923. Since this value is above 0.9, it is considered to be have an excellent level of internal consistency of scale. However, the reliability of the statistic can be further increased by eliminating one or more variables.
Since Q8 is the highest value at 0.928, deleting this variable will further increase the Cronbach’s Alpha value.
Deleting off Q8 results in the Cronbach’s Alpha increasing to 0.926 from 0.923. Although it is considered to be have an excellent level of internal consistency of scale, the value can be further increased by deleting off another variable.
Judging from the dataset, Q11 would be the highest value at 9.31. Deleting off this variable will further increase the Cronbach’s Alpha value.
After deleting both variables, Q8 and Q11, the Cronbach’s Alpha value has now increase to 0.931, from 0.926.
The data set suggests that the next variable to delete off would be Q9. However, since the Cronbach’s Alpha is already above 0.9, it is considered to be have an excellent level of internal consistency of scale and deleting off a variable to further increase the Cronbach’s Alpha value is not necessary.
