Business Analytics Lab Worksheet 04 (bsad_lab04)
CME Group Foundation Business Analytics Lab
Joseph Cuellar
2/14/19
About
Qualitative Descriptive Analytics aims to gather an in-depth understanding of the underlying reasons and motivations for an event or observation. It is typically represented with visuals or charts.
Quantitative Descriptive Analytics focuses on investigating a phenomenon via statistical, mathematical, and computationaly techniques. It aims to quantify an event with metrics and numbers.
In this lab, we will explore both analytics using the data set provided.
Setup
Remember to always set your working directory to the source file location. Go to ‘Session’, scroll down to ‘Set Working Directory’, and click ‘To Source File Location’. Read carefully the below and follow the instructions to complete the tasks and answer any questions. Submit your work to RPubs as detailed in previous notes.
Note
For your assignment you may be using different data sets than what is included here. Read carefully the instructions on Sakai.
Task 1: Quantitative Analysis
Begin by reading in the data from the ‘marketing.csv’ file, and viewing it to make sure it is read in correctly.
mydata = read.csv(file="data/marketing.csv")
head(mydata)## case_number sales radio paper tv pos
## 1 1 11125 65 89 250 1.3
## 2 2 16121 73 55 260 1.6
## 3 3 16440 74 58 270 1.7
## 4 4 16876 75 82 270 1.3
## 5 5 13965 69 75 255 1.5
## 6 6 14999 70 71 255 2.1Now calculate the Range, Min, Max, Mean, STDEV, and Variance for each variable. Below is an example of how to compute the items for the variable ‘sales’.
Sales
sales = mydata$sales
#Max Sales
maxsales = max(sales)
maxsales## [1] 20450#Min Sales
minsales = min(sales)
minsales## [1] 11125#Range
rangesales = maxsales-minsales
rangesales## [1] 9325#Mean
meansales = mean(sales)
meansales## [1] 16717.2#Standard Deviation
spreadsales = sd(sales)
spreadsales## [1] 2617.052#Variance
variancesales = var(sales)
variancesales## [1] 6848961Repeat the above calculations for radio, paper, tv, and pos.
radio = mydata$radio
#Max Radio
maxradio = max(radio)
maxradio## [1] 89#Min Radio
minradio = min(radio)
minradio## [1] 65#Range
rangeradio = maxradio-minradio
rangeradio## [1] 24#Mean
meanradio = mean(radio)
meanradio## [1] 76.1#Standard Deviation
spreadradio = sd(radio)
spreadradio## [1] 7.354912#Variance
varianceradio = var(radio)
varianceradio## [1] 54.09474paper = mydata$paper
#Max Paper
maxpaper = max(paper)
maxpaper## [1] 89#Min Paper
minpaper = min(paper)
minpaper## [1] 35#Range
rangepaper = maxpaper-minpaper
rangepaper## [1] 54#Mean
meanpaper = mean(paper)
meanpaper## [1] 62.3#Standard Deviation
spreadpaper = sd(paper)
spreadpaper## [1] 15.35921#Variance
variancepaper = var(paper)
variancepaper## [1] 235.9053tv = mydata$tv
#Max TV
maxtv = max(tv)
maxtv## [1] 280#Min TV
mintv = min(tv)
mintv## [1] 250#Range
rangetv = maxtv-mintv
rangetv## [1] 30#Mean
meantv = mean(tv)
meantv## [1] 266.6#Standard Deviation
spreadtv = sd(tv)
spreadtv## [1] 11.3388#Variance
variancetv = var(tv)
variancetv## [1] 128.5684pos = mydata$pos
#Max POS
maxpos = max(pos)
maxpos## [1] 3#Min POS
minpos = min(pos)
minpos## [1] 0#Range
rangepos = maxpos-minpos
rangepos## [1] 3#Mean
meanpos = mean(pos)
meanpos## [1] 1.535#Standard Deviation
spreadpos = sd(pos)
spreadpos## [1] 0.7499298#Variance
variancepos = var(pos)
variancepos## [1] 0.5623947An easy way to calculate all of these statistics of all of these variables is with the summary() function. Below is an example.
summary(mydata)## case_number sales radio paper
## Min. : 1.00 Min. :11125 Min. :65.00 Min. :35.00
## 1st Qu.: 5.75 1st Qu.:15175 1st Qu.:70.00 1st Qu.:53.75
## Median :10.50 Median :16658 Median :74.50 Median :62.50
## Mean :10.50 Mean :16717 Mean :76.10 Mean :62.30
## 3rd Qu.:15.25 3rd Qu.:18874 3rd Qu.:81.75 3rd Qu.:75.50
## Max. :20.00 Max. :20450 Max. :89.00 Max. :89.00
## tv pos
## Min. :250.0 Min. :0.000
## 1st Qu.:255.0 1st Qu.:1.200
## Median :270.0 Median :1.500
## Mean :266.6 Mean :1.535
## 3rd Qu.:276.2 3rd Qu.:1.800
## Max. :280.0 Max. :3.000#Repeat the above for the varialble sales. There are some statistics not calculated with the summary() function Specify which.Standard Deviation, Range, and Variance are not calculated by the summary() function.
Task 2: Qualitative Analysis
Now, we will produce a basic blot of the ‘sales’ variable . Here we utilize the plot function and within the plot function we call the variable we want to plot.
plot(sales)
We can customize the plot by adding labels to the x- and y- axis.
#xlab labels the x axis, ylab labels the y axis
plot(sales, type="b", xlab = "Case Number", ylab = "Sales in $1,000") 
There are further ways to customize plots, such as changing the colors of the lines, adding a heading, or even making them interactive.
Now, lets plot the sales graph, alongside radio, paper, and tv which you will code. Make sure to run the code in the same chunk so they are on the same layout.
#Layout allows us to see all 4 graphs on one screen
layout(matrix(1:4,2,2))
#Example of how to plot the sales variable
plot(sales, type="b", xlab = "Case Number", ylab = "Sales in $1,000")
#Plot of Radio. Label properly
plot(radio, type="b" , xlab = "Case Number" , ylab = "Radio Expenses in $1000")
#Plot of Paper. Label properly
plot(paper, type="b" , xlab = "Case Number" , ylab = "Paper Expenses in $1000")
#Plot of TV. Label properly
plot(tv, type='b', xlab = "Case Number" , ylab = "tv Expenses in $1000")
When looking at these plots it is hard to see a particular trend. One way to observe any possible trend in the sales data would be to re-order the data from low to high. The 20 months case studies are in no particular chronological time sequence. The 20 case numbers are independent sequentially generated numbers. Since each case is independent, we can reorder them.
#Re-order sales from low to high, and save re-ordered data in a new set. As sales data is re-reorded associated other column fields follow.
newdata = mydata[order(sales),]
head(newdata)## case_number sales radio paper tv pos
## 1 1 11125 65 89 250 1.3
## 19 19 12369 65 37 250 2.5
## 20 20 13882 68 80 252 1.4
## 5 5 13965 69 75 255 1.5
## 6 6 14999 70 71 255 2.1
## 11 11 15234 70 66 255 1.5# Redefine the new variables
newsales = newdata$sales
newradio = newdata$radio
newtv = newdata$tv
newpaper = newdata$paper#Repeat the 4 graphs layout with proper labeling using instead the four new variables for sales, radio, tv, and paper.
#Layout allows us to see all 4 graphs on one screen
layout(matrix(1:4,2,2))
#Example of how to plot the sales variable
plot(newsales, type="b", xlab = "Case Number", ylab = "Sales in $1,000")
#Plot of Radio. Label properly
plot(newradio, type="b" , xlab = "Case Number" , ylab = "Radio Expenses in $1000")
#Plot of Paper. Label properly
plot(newpaper, type="b" , xlab = "Case Number" , ylab = "Paper Expenses in $1000")
#Plot of TV. Label properly
plot(newtv, type='b', xlab = "Case Number" , ylab = "TV Expenses in $1000")
Shares your observations on what the new plots are revealing in terms of trending relationship.
Answer: We can see that as sales begin to increase as paper expenses and TV expenses increase. This is most likely because of marketing and advertising. It is safe to assume that their marketing efforts are paying off. The plots show us that there is a positive correlation between marketing and sales.
Task 3: Standarized Z-Value
Given a sales value of $25000, calculate the corresponding z-value or z-score using the mean and standard deviation calculations conducted in task 1. We know that z-score = (x - mean)/sd.
# Show calculations here
zscore = (25000- meansales) / spreadsales
zscore## [1] 3.164935Based on the z-value, how would you rate a $25000 sales value: poor, average, good, or very good performance? Explain your logic.
Answer: If the z-score is greater than 3 or less than -3, it is an outlier. Since the score we calculated is greater than 3, the sales value is very good. If you think about a normal distribution, being on the upper end or the right end of the bell curve is very good to be. The worker is an excellent salesman.