Business Analytics: Assessment Questions

BUSINESS ANALYTICS: ASSESSMENT
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BUSINESS ANALYTICS: ASSESSMENT QUESTIONS
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Business Analytics: Assessment Questions
Question 1
Imbalance data is a common problem in classification; it is created when classes are not
of equal size/ volume (Hilbe 2009). One example, the daily attendance register for employees in
an office setting, you can get that out of 200 individuals 4 were absent and 196 were present on
18th of March 2018. Another example is there are four departments (A, B, C, and D) that
generate the same product for an organization. According to data statistics collected by the
organization the overall product yield can be attributed 50% to A, 20% to B, 12% to C, and 18%
to D. These two examples show how common the issue of imbalance data is in the world of
business. To address this problem there are two techniques we can employ such as random
under-sampling and re-sampling. Random under-sampling seeks to achieve equality in class
distribution by through the random negation of the majority class. While, re-sampling technique
calls for the increment of the minority classes or an alternative would be to decrease the majority
classes in order to balance the classes (Hilbe 2009).
Question 2
In a logistic regression it is impossible to get a single line that goes through all the point
thereby indicating the line of best fit. Since, it is impossible to get the value of 1 for R squared
there is no use of R-squared and adjusted R-squared in a regression analysis. As such, special
tests have been developed to tackle this problem; for example, McFadden's pseudo-R-squared
(Hilbe 2009).
Question 3
Logistic regression can be used by business management because they can assign values
of 0s and 1s to data to distinguish the classes. For example, management can use 1s to denote

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employs who attended a meeting and 0s to indicate those who were absent from the meeting.
Another example of where logistic regression can be applied in a business setting is when
information is being collected on how many employees received bonuses. The individuals that
received bonuses will be denoted by 1s and those who did not get bonuses will be represented by
0s. Therefore, logistic regression is used in organization to analysis qualitative data with fixed
responses/choices that can be assigned numerical value.
Question 4
Each of the levels of the first explanatory variable (X1) will be assigned numerical values
that are unique. For example, 0 for low, 1 for average, 2 for high, and 3 for very high. Likewise,
the same will be done for the second explanatory variable (X2); As such, the levels will be
assigned values like 0=Sydney, 1=Melbourne, and 2=Brisbane. It is easy to see that the same
number allocation system can be used on different variables with unrelated data; given X1 deals
with a ranking system and X2 deals with Australian cities. Since we have two independent
variables where will be three coefficients i.e. B0, B1, and B2. It is important to indicate that B1
and B2 are the coefficients for X1 and X2 respectively.
Question 5
Part (a)
KNN models are based on a non-parametric learning technique through which we attempt
to predict the value of give variable based on a training set. The first step involves the evaluation
of similarity through the use of distance functions like Euclidean. The formula we will employ is
d ( x , y )= √∑
i=1
n
( xi− yi )2
The second step deals with finding the K-nearest neighbours. For instance, you get the five most
closest to the desired value and then choose which spending level best suits the customer in

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question. We can also chart a graph to demonstrate the distance values to best see which ones are
closest to that of the new customer.
Part (b)
Yes, it will increase because we will be given a wide scope from where to choose the
new customers spending. Moreover, using the CONTIF function in Microsoft Excel it is clear
that 100% of all customers in the data spent more than $500; As such, it is very likely that this
new customer will spend at least $500.
Part (c)
The type of product being purchased information is omitted as such that first column will
be ignore in our calculation. After calculation the new female customer had a distance of 0 with a
pre-exist customer. Therefore, we will conclude the customer is most likely to spend $938
Question 6
Part (a)
The best thing would be to analysis the data and compare the variables to discern their
relationship. For instance, we can compare how much of a give type of repair was performed by
a particular repair person. According to the data and chart below it is clear that majority of the
repairs were done by Bob and John. Majority of the Mechanical repair jobs were performed by
John; As such, if there are constant mechanical issues being witnesses with machines the bulk of
the blame should be directed at John. It is therefore important for management to develop a plan
that will ensure that John is assessed and trained on his competence as a mechanical repair. On
the other hand, majority of the electrical repairs were performed by Bob; likewise, he should be
held as most accountable for repetitive electrical issues with the machines. It is important to note
that James has done very little compared to the other two employees. It is recommendable that he