BSAD343 Fall 2018 Lab Worksheet 04

Qualitative & Quantitative Analytics (bsad-lab04)

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. Always read carefully the instructions on Sakai. For clarity, tasks/questions to be completed/answered are highlighted in red color and numbered according to their particular placement in the task section. Quite often you will need to add your own code chunk.

Execute all code chunks, preview, publish, and submit link on Sakai.

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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)

Now let’s 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 = mydata$sales
#Max Sales
max = max(sales)
max

[1] 20450

#Min Sales
min = min(sales)
min

[1] 11125

#Range
max-min

[1] 9325

#Mean
mean(sales)

[1] 16717.2

#Standard Deviation
sd(sales)

[1] 2617.052

#Variance
var(sales)

[1] 6848961

##### 1A) Repeat the above statistics for the variable radio

radio = mydata$radio
#Max Sales
max = max(radio)
max

[1] 89

#Min radio
min = min(radio)
min

[1] 65

#Range
max-min

[1] 24

#Mean
mean(radio)

[1] 76.1

#Standard Deviation
sd(radio)

[1] 7.354912

#Variance
var(radio)

[1] 54.09474

An easy way to calculate many of these statistics is with the summary() function. Below is an example.

summary(sales)

   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
  11125   15175   16658   16717   18874   20450 

##### 1B) Repeat the above summary calculation for the variable paper. Some statistics are not calculated with the summary() function. Specify which

paper = mydata$paper
summary(paper)

SD is not found nor is var

Task 2: Qualitative Analysis

Now, we will produce a basic plot of the ‘sales’ variable . Here we call the plot function and within the plot function we refer the variable we want to plot.

plot(sales)

We can customize the plot by connecting the dots and 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.

tv = mydata$tv

#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", main= "Sales") 
# Add three other plots here
plot(radio, type="b", xlab = "Case Number", ylab = "Sales in $1,000", main= "Radio") 

plot(paper, type="b", xlab = "Case Number", ylab = "Sales in $1,000", main= "Paper") 
plot(tv, type="b", xlab = "Case Number", ylab = "Sales in $1,000", main= "TV") 

##### 2A) Insert in the above code chunk the three other plots for Radio, Paper, and TV. Label the axes properly.

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 used as tags. Since each case is independent, we can reorder them. Note that as each case is re-ordered corresponding column values are also re-organised to maintain the relationship integrity.

#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)

# Redefine the new variables 
newsales = newdata$sales
newradio = newdata$radio
newtv = newdata$tv
newpaper = newdata$paper

##### 2B) Repeat the previous 4 graphs layout exercise using instead the above defined four new variables for sales, radio, tv, and paper.

layout(matrix(1:4,2,2))
plot(newsales, type="b", xlab = "Case Number", ylab = "Sales in $1,000", main= "Sales") 
plot(newradio, type="b", xlab = "Case Number", ylab = "Sales in $1,000", main= "Radio") 

plot(newpaper, type="b", xlab = "Case Number", ylab = "Sales in $1,000", main= "Paper") 
plot(newtv, type="b", xlab = "Case Number", ylab = "Sales in $1,000", main= "TV") 

##### 2C) Explain what the new plots are revealing in terms of trending relationships. they are all positively correlated except for Paper which is slowy dying.
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Task 3: Standarized Z-Value

You are given a sales value of $25000. We want to calculate the corresponding z-value or z-score for sales using the mean and standard deviation calculations as shown in task 1. Remember that z-score = (x - mean)/sd.

(25000-mean(sales))/sd(sales)

[1] 3.164935

##### 3A) Calculate the z-value. Based on your result, would you rate a $25000 in sales as poor, average, good, or very good performance? Explain your logic. its very good performance since it is positive 3.1