Product Revenue

The analysis is to find which product have more revenues and which product have more sales in which country.

Load the data

head(product, 10)

##        Continent   Product Revenue
## 1  South America Product C   719.1
## 2           Asia Product C   833.6
## 3  South America Product A   468.5
## 4      Australia Product A   873.5
## 5  South America Product A   543.5
## 6         Europe Product A   941.5
## 7      Australia Product B  1277.0
## 8         Africa Product B   534.5
## 9           Asia Product C   789.0
## 10 South America Product B   412.9

str(product)

## 'data.frame':    1000 obs. of  3 variables:
##  $ Continent: Factor w/ 6 levels "Africa","Asia",..: 6 2 6 3 6 4 3 1 2 6 ...
##  $ Product  : Factor w/ 3 levels "Product A","Product B",..: 3 3 1 1 1 1 2 2 3 2 ...
##  $ Revenue  : num  719 834 468 874 544 ...

summary(product)

##          Continent        Product       Revenue    
##  Africa       :167   Product A:401   Min.   : 154  
##  Asia         :186   Product B:396   1st Qu.: 525  
##  Australia    :148   Product C:203   Median : 716  
##  Europe       :173                   Mean   : 742  
##  North America:167                   3rd Qu.: 937  
##  South America:159                   Max.   :1489

Check the boxplot

boxplot(Revenue ~ Continent + Product + Continent*Product, data = product)

We ran ANOVA

product_aov <- aov(Revenue ~ Continent + Product + Continent*Product, data = product)
summary(product_aov)

##                    Df   Sum Sq Mean Sq F value Pr(>F)    
## Continent           5 42654998 8531000  323.35 <2e-16 ***
## Product             2    55901   27950    1.06   0.35    
## Continent:Product   9    85220    9469    0.36   0.95    
## Residuals         983 25934805   26383                   
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Since the interaction between Continent and Product is not significant. We’ll stop the analysis here.