Business Analytics - Discussion and Quantitative Questions

Business Analytics
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Contents
Contents................................................................................................................................................2

SECTION A: Discussion Questions.........................................................................................................4
QUESTION 1...........................................................................................................................................4
Examples of imbalance data and approach to handling it.................................................................4
QUESTION 2...........................................................................................................................................4
Using R2 or adjusted R2 to evaluate a logistic regression...................................................................4
Question 3.............................................................................................................................................4
Practical Examples of Logistics Regression........................................................................................4
Question 4.............................................................................................................................................5
Developing a Logistics Model Using Categorical Variables................................................................5
SECTION B: QUANTITATIVE QUESTIONS...........................................................................................5
Question 5.............................................................................................................................................5
a. Steps of developing a K Nearest Neighbour Model...................................................................5
b. Whether the model will improve with 500 rather than 700......................................................6
c. A Predictive model.....................................................................................................................6
Question 6.............................................................................................................................................7
a. Insights and recommendations from Data Analysis...................................................................7
b. Predictive analysis.....................................................................................................................8
c. Data that should be added........................................................................................................9
Question 7.............................................................................................................................................9
a. Dealing with Missing Values......................................................................................................9
b. Calculating the Averages..........................................................................................................10
c. Calculating the range...............................................................................................................10
d. Visualisation tools....................................................................................................................11
QUESTION 8.........................................................................................................................................11
a. Predictive Model......................................................................................................................11
b. A multiple linear regression model..........................................................................................11
c. A multiple regression...............................................................................................................12
d. Risk calculation........................................................................................................................13
Question 9...........................................................................................................................................13
a. A spreadsheet..........................................................................................................................13
b. Percentage of his salary...........................................................................................................15
a. Number of advertisements......................................................................................................16
b. Rewrite the model...................................................................................................................16
References...........................................................................................................................................17

SECTION A: Discussion Questions
QUESTION 1
Examples of imbalance data and approach to handling it
An imbalance data is that which is not uniformly distributed (Enikeev, 2014).
Two examples of that may face imbalance in data classification techniques are
salary classification of employees in different sectors i.e. manufacturing and
education and customer preference customers of different ages. There are
several ways of handling imbalance data. Some of the common ways include
using the right metric for evaluation, under sampling or over sampling,
resampling and clustering the abundant class (Frankfort-Nachias, 2015). For instant,
to deal with imbalance in data in salary classifications, you may cluster the
employees into different sectors for easy analysis.
QUESTION 2
Using R2 or adjusted R2 to evaluate a logistic regression
Logistics regression is a method of predicting a dependent variable using two or
more independent variables (Lind, et al., 2008). R2 or adjusted R2 are suitable for
use in evaluating a logistic regression because the value of R2 or adjusted R2 will
indicate the proportion (percentage) of the data that is explained by the model
or the logistic model. This implies that the values of R2 or adjusted R2 will tell the
accuracy of the logistic model (Lind, et al., 2008)