Empirical Research Methods for Business: Assignment Solution Analysis

This assignment solution for an Empirical Research Methods course analyzes various business data using statistical techniques. It begins with descriptive statistics and graphical representations (histograms, pie charts, and bar charts) to examine weekly rent, distance to the train station, street appeal, and materials used. The solution then explores the relationship between weekly rent and distance to the train/bus station using scatter plots and regression analysis, assessing the significance of linear associations. Further, the assignment evaluates the reliability of an employee satisfaction subset using Cronbach's Alpha and suggests variable eliminations to enhance reliability. Finally, a one-way ANOVA test is conducted to determine if average rent differs across different degrees of street appeal, concluding with a rejection of the null hypothesis. The solution incorporates relevant statistical outputs from SPSS, interpretation of results, and appropriate references.

Empirical Research Methods for Business
STUDENT ID:
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Question 1
(a) The requisite descriptive statistics associated with weekly rent as obtained from SPSS are
summarised below.
The requisite graphical representation for weekly rent has been achieved through the use of
histogram shown below.
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Comment
Based on the above histogram, it is apparent that the shape of the distribution is not
symmetric since the tail on the right of the mean is longer then the tail on the left of the mean.
The presence of positive skew is also supported from the descriptive statistics. The given
variables cannot be assumed to be normally distributed as skew present is sizable and also the
kurtosis value is significantly different from 3 (Hillier, 2016).
(b) The requisite descriptive statistics associated with distance to train station as obtained
from SPSS are summarised below.
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The requisite graphical representation for distance from train station has been achieved
through the use of histogram shown below.
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Comment
Based on the above histogram, it is apparent that the shape of the distribution is almost
symmetric. The presence of negligible positive skew also provides support to the above
observation. Even though the skew is almost zero, but the graph does not fit in the bell
shaped curve and also has multiple peaks. Further, the kurtosis value fails to meet the
requisite value of 3 required for normal distribution and hence the given variable cannot be
assumed as normally distributed (Medhi, 2016).
(c) The requisite frequency distribution of “Street Appeal” along with corresponding
graphical representation in terms of pie chart is shown below.
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From the above frequency distribution and pie chart, it is evident that in terms of street
appeal, about 72% of the observations tend to not exceed average and only a limited
proportion of observations fall under categories which indicate varying degrees of good in
street appeal.
(d) The requisite frequency distribution of materials along with corresponding graphical
representation in terms of bar chart is shown below.
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Based on the above graphical representation, it is evident that Timber is the preferred
material amongst the key available options while Veneer has the lowest preference amongst
the sample properties. However, there is a fair representation of all the three materials which
implies that preference varies across customers.
Question 2
The relevant scatter plot with distance to the train station being the independent variable and
weekly rent being the dependent variable is shown below.
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From the above scatter plot, it is evident that the linear association between the two variables
is non-existent. This is apparent from the fact there is large deviation of the scatter points
from the line of best fit which clearly indicates that the model is not a good fit. Further,
evidence in this regards is provided by R2 value of 0.014 which would imply that distance of
property from train station would provide explanation to only 1.4% changes in weekly rent
(Flick, 2015).
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It is evident from the above regression output that the slope coefficient is not statistically
significant as the p value exceeds 0.05 and hence the linear relationship between the variables
is not significant.
One of the key assumptions associated with linear regression is that the residuals should be
normally distribution which is established based on the above plot as a broad linear trend is
observed (Eriksson and Kovalainen,2015).
The relevant scatter plot with distance to the bus station being the independent variable and
weekly rent being the dependent variable is shown below.
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From the above scatter plot, it is evident that the linear association between the two variables
is non-existent. This is apparent from the fact there is large deviation of the scatter points
from the line of best fit which clearly indicates that the model is not a good fit. Further,
evidence in this regards is provided by R2 value of 0.0006 which imply that the predictive
ability of weekly rent based on the distance of property from bus station is zero (Hair et. al.,
2015).
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It is evident from the above regression output that the slope coefficient is not statistically
significant as the p value exceeds 0.05 and hence the linear relationship between the variables
is not significant.
One of the key assumptions associated with linear regression is that the residuals should be
normally distribution which is established based on the above plot as a broad linear trend is
observed (Eriksson and Kovalainen,2015).
Question 3
The relevant output obtained from SPSS with regards to reliability of the employee
satisfaction subset is indicated below.
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