Description

Task 1: Exploratory Data Analysis (25 points)
Using ‘FACILITY_TOY’ dataset available in SAS Viya, answer the following questions. Export/copy all
charts you create into your answer script, describe the charts (e.g. what your chart is visualising in
each axis) and interpret the charts, i.e. what your chart highlights. This interpretation is ideally
accessible to even a non-technical reader.

Q1. On a geographic map, show the countries where toy facilities are located. Size of the bubble
should be the total unit capacity (4 points).
Q2. What is the total unit capacity in the United States (1 point) and in Australia (1 point).
Q3. Temporarily remove United States from the map you prepared in Q1. Show the updated map
(without US) (3 points). Which country has the second-largest unit capacity after United States (1
point).
Q4. Many countries have more than one toy facility. Further, most facilities have more than one unit
manufacturing toys. As one would expect, majority of these units do not operate at full capacity.
Assuming the actual usage of the units is provided by ‘Unit Actual’ variable and the total unit capacity
is provided by ‘Unit Capacity’ variable, calculate the ‘Capacity Utilisation Ratio’ and store values in a
new variable. Show how you created this calculated item by taking a screenshot of the appropriate
SAS Viya window.
Generate a histogram of the new variable and copy/export it into your answer script. Interpret the
histogram (4 Points).
Q5. Prepare a bar chart to show the average ‘Capacity Utilisation Ratio’ by facility for each country.
Use a filter to show only Spain, Australia, and Japan in this bar chart. Copy/export the chart into your
answer script. Interpret your chart (4 points)
Q6. There are many factors that could explain the variation observed in the Unit Capacity Utilization
Ratio. Identify two such factors and demonstrate how these two factors explain the variation in Unit
Capacity Utilization Ratio with the help of two charts and associated interpretation. (3.5 points per
chart).

MBAS901 – Final Assignment April 9, 2022
Task 2. Predictive Data Analytics (25 points)
Using ‘FLCRASH’ data, answer the following questions.
Q1. Note the variable ‘Total Crash Injuries’ provide a number of injuries associated with every
accident. In SAS viya, prepare a histogram showing the distribution of Total Crash Injuries. What can
you say about the distribution of crash injuries? (2 points)
Q2. Create a new custom category variable based on ‘Total Crash Injuries’ variable. This new custom
category variable should contain two categories only. One category is injuries equal to zero, while the
other category is for crashes with one or more injuries. (3 points).
Visualise the frequency of the two new categories you just created on a bar chart. How many crashes
report zero injuries? (3 points).
Q3. In Q2, you created a new categorical variable with only two values (binary). Your task now is to
develop two models that can predict the value this target variable takes, given other explanatory
variables. In other words, you attempt to predict if a crash is going to result in injuries (or not) given
other important variables.
What are the two models (or techniques) you can use to predict this target variable? (2 points).
Create one model to predict the target variable you created in Q2. Assess this model’s accuracy.
What are the most important variables in predicting this target variable? (6 points).
Create the second model to predict the target variable. Assess this model’s accuracy. What are the
most important variables identified by the model to predict the target variable.(6 points).
Compare the performance of the two model. Report and discuss the results of your comparison.
Which model is the champion? (3 points).