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 project, you will explore both analytics using the data set provided.

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.

First, calculate the mean, standard deviation, maximum, and minimum for the Age column using R.

In R, we must read the file first and extract the column and find the values that are asked for.

```
#Read File
mydata=read.csv(file="data/creditrisk.csv")
#Name the extracted variable
age=mydata$Age
```

```
#Calculate the average, standard deviation, maximum and minimum age below.
meanage=mean(age)
meanage
```

`## [1] 34.39765`

```
maxage=max(age)
maxage
```

`## [1] 73`

```
spreadage=sd(age)
spreadage
```

`## [1] 11.04513`

```
MinAge=min(age)
MinAge
```

`## [1] 18`

An outlier is value that “lies outside” most of the other values in a set of data. Next, use the formula from class to find the upper and lower limits for age to decide on outliers.

```
#Use the common formula to calculate the upper and lower thresholds
UpperOutlier = meanage + 3*spreadage
UpperOutlier
```

`## [1] 67.53302`

```
LowerOtlier = meanage- 3*spreadage
LowerOtlier
```

`## [1] 1.262269`

Are there any outliers? How can you check the data to find out if there are potential outliers? Use the chunk below to make a desicion about possible outliers.

```
# Insert here your work to find if the data contains potential outliers.
#After calculating the maximum value, I found that it is higher than the 3rd standard deviation, which proves there is an outlier.
```

Another similar method to find the upper and lower thresholds discussed in introductory statistics courses involves finding the interquartile range. Use the chunk below to first calculate the interquartile range..

```
#interquantile range
quantile(age)
```

```
## 0% 25% 50% 75% 100%
## 18 26 32 41 73
```

```
lowerq = quantile(age)[2]
upperq=quantile(age) [4]
iqr = upperq - lowerq
iqr
```

```
## 75%
## 15
```

The threshold is the boundaries that determine if a value is an outlier. If the value falls above the upper threshold or below the lower threshold, it can be identified as a potential outlier.

Below is the upper threshold:

```
upperthreshold= (iqr * 1.5) + upperq
upperthreshold
```

```
## 75%
## 63.5
```

Below is the lower threshold:

```
lowerthreshold = lowerq - (iqr * 1.5)
lowerthreshold
```

```
## 25%
## 3.5
```

Are there any outliers? How many? 15

It can also be useful to visualize the data using a box and whisker plot. Use the boxplot() command to visualize your data.

`boxplot(age)`

Can you identify the outliers from the boxplot? If so how many outliers? 5

Begin by reading in the data from the ‘marketing.csv’ file, and viewing it to make sure it is read in correctly.

```
#read the marketing file and view it to make sure it is read correctly
newdata = read.csv(file="data/marketing.csv")
head(newdata)
```

```
## 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 2.1
```

Now calculate the Range, Min, Max, Mean, STDEV, and Variance for the variable ‘sales’.

Sales

```
Sales <- newdata$sales
#Max Sales
maxsales <- max(Sales)
maxsales
```

`## [1] 20450`

```
#Min Sales
minsales <- min(Sales)
#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] 6848961`

An easy way to calculate the statistics of all of these variables is with the summary() function. Run the summary command to visualize the statistics for all variables in the dataset.

```
# Summary statistics for all variables.
summary(newdata)
```

```
## 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
```

You can also use the summary() command to find the statistics for the sales variable.

```
# Summary statistics for the sales variable
summary(Sales)
```

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

There are some statistics not calculated with the summary() function. Specify which. statistics not calculated are range and standard deviation ———-

Given a sales value of $25000, calculate the corresponding z-value or z-score.

```
# Calculate the z-value and display it
x <- 25000
zvalue <- (x- meansales)/spreadsales
zvalue
```

`## [1] 3.164935`

Based on the z-value, how would you rate a `$25000`

sales value: poor, average, good, or very good performance? Explain your logic. z-value is an outlier because its bigger than 3 and anything less than -3 are greater is considered an outlier/ I would rate 25000 sales value as good because the value is a little bit above 3.