(Supermarket Invoices)
Brief Description:
A supermarket invoice dataset describes the sold products, including prices, discounts, and other shipment information. The dataset deals with understanding how the number of products/units sold is dependent on other variables. This can significantly contribute to predicting the quantity of sold products. This can also help top management understand how the number of sold product varies with each independent variable, and determine accordingly the applicable discount, price, etc.
https://seuedu-my.sharepoint.com/:f:/g/personal/k_kumar_seu_edu_sa/EhL20mtXcD9EnVOB-TrtvT8BFUm-xNnh5yPUezZOo4kGmg?e=Zr1KgT
You are requested to analyze dataset using R programming language.
Write an R program to perform the following tasks:
Task 1:
Use R. Discuss and explain the type and structure of the data. Derive descriptive statistics regarding this dataset, including measures of central tendency for two variables.
> #load data
> df<-read.csv(“invoice.csv”)
> #describe data structure
> cat(“Number of rows: “,nrow(df))
Number of rows: 1059
> cat(“Number of columns: “,ncol(df))
Number of columns: 27
> head(df,1)
CustomerID CompanyName Address City Region PostalCode Country Salesperson
1 EASTC Eastern Connection 35 King George London WX3 6FW UK Nancy Davolio
OrderID OrderDate RequiredDate ShippedDate Shipper ProductID
1 10400 1/1/2019 1/29/2019 1/16/2019 Federal Shipping 29
ProductName UnitPrice Quantity Discount ExtendedPrice Freight
1 Thüringer Rostbratwurst $99.00 21 0% $2,079.00 $83.93
ShipName ShipAddress ShipCity ShipRegion ShipPostalCode ShipCountry
1 Eastern Connection 35 King George London WX3 6FW UK
CategoryName
1 Meat/Poultry
There are 1059 cases in the dataframe, and 27 variables.
Each case is an order. For each order, the dataset provides information on customer (id, name, address, city, region, postal code, country), the sales person, along with information on order: id, date, date to be delivered, dated of shipping, shipper, the id of the ordered product, and its name, unit price, ordered quantity, discount, extended price, freight, type of product (Category Name). In addition, we know about the ship name, shippingcity/region/country/address/postal code.
Most information is categorical, except for continuous variables: Unit Price, Quantity, Freight, Discount, Extended Price and the three variables including dates.
The numerical information and the one on dates are also coded as string, so transformations are required before producing statistics.
#transform some variables
df$UnitPrice<-as.numeric(gsub(“\$”, “”, df$UnitPrice))
df$Discount<-as.numeric(gsub(“\%”, “”, df$Discount))
df$ExtendedPrice<-as.numeric(gsub(“\,”, “”, gsub(“\$”, “”, df$ExtendedPrice)))
df$Freight<-as.numeric(gsub(“\,”, “”, (gsub(“\$”, “”, df$Freight))))
#descriptives
library(table1)
table1(~., data=subset(df, select = -c(Address, PostalCode) ))
Given that most of the categorical variables have too many categories, frequency distriobutions are not so meaningful, so the result of the above code is part of the Appendix 2.
> #transform date columns
> df$OrderDate<-as.Date(df$OrderDate, “%m/%d/%Y”)
> df$RequiredDate<-as.Date(df$RequiredDate, “%m/%d/%Y”)
> df$ShippedDate<-as.Date(df$ShippedDate, “%m/%d/%Y”)
>
> #more descriptives
> summary(df)
CustomerID CompanyName Address
Length:1059 Length:1059 Length:1059
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
City Region PostalCode
Length:1059 Length:1059 Length:1059
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
Country Salesperson OrderID
Length:1059 Length:1059 Min. :10400
Class :character Class :character 1st Qu.:10502
Mode :character Mode :character Median :10601
Mean :10601
3rd Qu.:10703
Max. :10807
OrderDate RequiredDate ShippedDate
Min. :2019-01-01 Min. :2011-01-01 Min. :2011-01-01
1st Qu.:2019-04-10 1st Qu.:2019-04-02 1st Qu.:2019-04-01
Median :2019-07-16 Median :2019-07-04 Median :2019-07-04
Mean :2019-07-10 Mean :2018-10-16 Mean :2019-03-07
3rd Qu.:2019-10-14 3rd Qu.:2019-10-10 3rd Qu.:2019-10-09
Max. :2019-12-31 Max. :2019-12-31 Max. :2019-12-31
Shipper ProductID ProductName
Length:1059 Min. : 1.00 Length:1059
Class :character 1st Qu.:23.00 Class :character
Mode :character Median :42.00 Mode :character
Mean :41.56
3rd Qu.:60.00
Max. :77.00
UnitPrice Quantity Discount
Min. : 2.00 Min. : 1.00 Min. : 0.00
1st Qu.: 12.50 1st Qu.: 10.00 1st Qu.: 0.00
Median : 18.60 Median : 20.00 Median : 0.00
Mean : 26.08 Mean : 24.07 Mean : 5.93
3rd Qu.: 32.80 3rd Qu.: 30.00 3rd Qu.:10.00
Max. :263.50 Max. :130.00 Max. :25.00
ExtendedPrice Freight ShipName
Min. : 4.8 Min. : 0.14 Length:1059
1st Qu.: 155.0 1st Qu.: 17.92 Class :character
Median : 340.0 Median : 58.33 Mode :character
Mean : 582.7 Mean : 100.84
3rd Qu.: 696.2 3rd Qu.: 127.34
Max. :10540.0 Max. :1007.64
ShipAddress ShipCity ShipRegion
Length:1059 Length:1059 Length:1059
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
ShipPostalCode ShipCountry CategoryName
Length:1059 Length:1059 Length:1059
Class :character Class :character Class :character
Mode :character Mode :character Mode :character
We can have a closer look to two continuous variables:
> #central tendency: two variables
> library(psych)
> describe(subset(df, select=c(UnitPrice, Quantity)))
vars n mean sd median trimmed mad min max
UnitPrice 1 1059 26.08 28.58 18.6 21.43 13.27 2 263.5
Quantity 2 1059 24.07 19.04 20.0 21.34 14.83 1 130.0
range skew kurtosis se
UnitPrice 261.5 5.13 35.39 0.88
Quantity 129.0 1.87 5.25 0.59
It seems that some departures from normality are observed: both have higher kurtosis and Quatity is also positively skewed. Histograms and boxplots are good for visualization:
#histograms
attach(df)
par(mfrow=c(1,2))
hist(UnitPrice, col=rgb(0,0,1,0.2), main=””)
hist(Quantity, col=rgb(1,0,0,0.2), main=””)
The histograms show indeed the positive skew, leptokurtic shape, and also a few outliers, that are better illustrated in boxplots:
#boxplots
par(mfrow=c(1,2))
boxplot(UnitPrice, col=rgb(0,0,1,0.2), main=”Unit Price”)
boxplot(Quantity, col=rgb(1,0,0,0.2), main=”Quantity”)
Task 2:
Extract a sample for the following statistical tasks to help draw conclusions about the population. Perform an independent and dependent t-test on the sample. Discuss in detail the statistical results, and its meaning based on the dataset you have chosen.
We extract a random sample of 500 cases.
#random sample of 500 cases
df2 <- df[sample(1:nrow(df), 500, replace=FALSE),]
For the t-test, we need to have a dichotomous variable. We create it:
#prepapre a variable for unitPrice over or lower the median
med<-median(df$UnitPrice)
library(expss)
df2$expensive<-recode(df2$UnitPrice, 0 %thru% med~0, TRUE ~1)
#independent-samples t-test
attach(df2)
t.test(Quantity~expensive)
Welch Two Sample t-test
data: Quantity by expensive
t = -0.38253, df = 497.87, p-value = 0.7022
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-4.058096 2.735420
sample estimates:
mean in group 0 mean in group 1
23.72881 24.39015
The t-test is insignificant (p=.70>>.05), therefore we cannot reject the null hypothesis and conclude that there is no difference in ordered quantity for products above or bellow the median price.
The boxplot allows visualizing the results.
#boxplot
par(mfrow=c(1,1))
boxplot(Quantity~expensive, col=rgb(1,0,0,0.2))
Given the presence of the outliers (the points with higher values, we repeat the t-test after removing them). The results indicate no significant difference (p=.52>>.05) between quantities ordered for expensive and unexpensive items:
> t.test(Quantity~expensive, data=subset(df2, Quantity<60))
Welch Two Sample t-test
data: Quantity by expensive
t = 0.64455, df = 449.89, p-value = 0.5195
alternative hypothesis: true difference in means between group 0 and group 1 is not equal to 0
95 percent confidence interval:
-1.575764 3.113824
sample estimates:
mean in group 0 mean in group 1
20.95575 20.18672
There is no appropriate pair of variables to have a paired sample t-test, therefore we create one. Let suppose that the delivered quantity is different as compared to the ordered one due to random factors. This is like having a pre-treatment measurement (the ordered quantity) and a post-treatment measurement (the Delivered Quantity). To intoduce the random factors, the Delivered Quantity was computed as product between the ordered quantity and a random uniform distribution with values between .8 and 1.2.
#paired samples t-test
#there is no appropriate data, so we create a variable
#indicating which quantity was actually delivered
df2$DeliveredQuantity<-df2$Quantity*runif(500,min=0.8, max=1.2)
attach(df2)
t.test(Quantity, DeliveredQuantity, paired = TRUE, alternative = “two.sided”)
Paired t-test
data: Quantity and DeliveredQuantity
t = -1.0834, df = 499, p-value = 0.2792
alternative hypothesis: true difference in means is not equal to 0
95 percent confidence interval:
-0.4429027 0.1280614
sample estimates:
mean of the differences
-0.157420
plot(DeliveredQuantity~Quantity, col=”blue”)
The t-test proves to be insignificant (t(499)=-1.08, p=.28>0.05). We conclude that there is no significant difference between the ordered quantity and the delivered one. The scatterplot illustrates the strong association between the two variables.
Task 3:
Perform a suitable multiple/logistic regression analysis on the sample dataset. Discuss the statistical results, and its meaning based on the dataset you have chosen.
The proposed model regresses ordered quantity on the unit price (expecting that higher prices lead to lower ordered volumes), the discount, the month when it was ordered (with an expectation to see a linear dependency, in the sense that the quantity increases in time), and the country for shipment (expecting higher quantities from better developed countries).
We need to create the “month” variable, and to specifiy Country as factor, for visualizations that follow.
> df2$Country<-as.factor(df2$Country)
> #extract month from date
> library(lubridate)
> df2$month<-month(df2$OrderDate)
> #regression model
> m1<-lm(Quantity ~ UnitPrice+Discount+month+Country, data=df2)
The next graphs visually check the regression assumptions.
> par(mfrow=c(2,2))
> plot(m1)
There are no homoscedasticity issues, except for some outliers that are influential.
We test whether they are influential:
> library(car)
> car::outlierTest(m1, cutoff=Inf, n.max=10)
rstudent unadjusted p-value Bonferroni p
817 5.725574 1.8298e-08 9.1306e-06
299 5.574081 4.1782e-08 2.0849e-05
959 5.236080 2.4682e-07 1.2317e-04
987 4.632545 4.6707e-06 2.3307e-03
501 4.268219 2.3798e-05 1.1875e-02
301 3.277947 1.1223e-03 5.6003e-01
960 3.145575 1.7616e-03 8.7904e-01
441 2.994517 2.8923e-03 NA
387 2.589814 9.8977e-03 NA
5 2.543372 1.1295e-02 NA
It turns out that some of them are. We remove all influential points by using the cook’s D as criterion (points with Cook’s D 4 times greater than mean are removed):
df2$cooksd <- cooks.distance(m1)
attach(df2)
df2$influential 4*mean(cooksd, na.rm=T))
We can see now the results:
m2<-lm(Quantity ~ UnitPrice+Discount+month+Country, data=subset(df2, !df2$influential))
par(mfrow=c(2,2))
plot(m2)
> library(stargazer)
> stargazer(m2, type=”text”, no.space=T, single.row=T )
===============================================
Dependent variable:
—————————
Quantity
———————————————–
UnitPrice -0.007 (0.019)
Discount 0.071 (0.075)
month -0.294 (0.179)
CountryAustria 31.357*** (6.505)
CountryBelgium 21.643*** (7.005)
CountryBrazil 11.550* (6.147)
CountryCanada 17.686*** (6.676)
CountryDenmark 9.266 (7.054)
CountryFinland 4.840 (6.935)
CountryFrance 10.252* (6.113)
CountryGermany 17.721*** (6.011)
CountryIreland 23.959*** (8.277)
CountryItaly 5.575 (7.064)
CountryMexico 8.814 (6.539)
CountryPoland 6.658 (8.759)
CountryPortugal 10.418 (6.880)
CountrySpain 1.921 (7.415)
CountrySweden 6.716 (6.534)
CountrySwitzerland 17.792** (7.639)
CountryUK 4.563 (6.233)
CountryUSA 17.929*** (6.052)
CountryVenezuela 14.873** (6.523)
Constant 8.777 (5.861)
———————————————–
Observations 469
R2 0.222
Adjusted R2 0.183
Residual Std. Error 12.980 (df = 446)
F Statistic 5.777*** (df = 22; 446)
===============================================
Note: *p<0.1; **p<0.05; ***p<0.01
The results indicate no significant relation with the price and discount. The impact of the month is contrary to expectations: the ordered quantity significantly decreased as time passed by. There are also significant differences across countries: several of them (Austria, Belgium, Brazil, Canada, Denmark, France, Germany, Sweden, Switzerland, the US, Venezuela) order higher quantities as compared to the reference category, which is Argentina. All the others do not significantly differ from Argentina. This is not as expected (a relation to level of development), but it shows a different significant relation that deserves being explored.
One also needs to check linearity of relations:
avPlots(m2)
In the added variable plots (the partial plots), one clearly observes that two orders affect the relation to UnitPrice (the two outliers in the lower-right corner of the first figure). Discount and quantity seem to have indeed no relation, while there is a small decrease in ordered quantities when we are approaching the end of the year.
A better visualization of effects is also possible:
> library(effects)
> plot(allEffects(m2))
One also needs a clear identification of the countries’ effects:
library(ggplot2)
library(jtools)
q<-effect_plot(m2, pred=Country,
plot.points = TRUE, jitter = c(0.1,0))
q + theme(axis.text.x = element_text(angle = 90, hjust = 1))
Task 4:
Perform an analysis of variance (ANOVA), the ANOVA F-Test, and analysis of covariance (ANCOVA). Discuss the statistical results, and its meaning based on the dataset you have chosen.
#ANOVA
par(mfrow=c(1,1))
boxplot(Quantity~Country, data=df2, horizontal=F, las=2)
df2$c<-as.factor(df2$Country)
m2<-aov(Quantity~Country, data=df2)
summary(m2)
Df Sum Sq Mean Sq F value Pr(>F)
Country 20 21765 1088.3 3.132 7.24e-06 ***
Residuals 479 166453 347.5
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The impact of country is significant, and one can see similar effects as the ones observed in regression analysis.
For post-hoc tests we need to see whether variances are homogeneous:
# homogeneity of variance
plot(m2, 1)
> library(car)
> leveneTest(Quantity~Country, data=df2)
Levene’s Test for Homogeneity of Variance (center = median)
Df F value Pr(>F)
group 20 1.6365 0.04071 *
479
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The test is significant, therefore we reject the null hypothesis of equality of variances. We will use the Tamhane test.
> # post-hoc
> library(PMCMRplus)
> tamhaneT2Test(m2)
Pairwise comparisons using Tamhane’s T2-test for unequal variances
data: Quantity by Country
Argentina Austria Belgium Brazil Canada Denmark
Austria 0.00029 – – – – –
Belgium 0.66788 1.00000 – – – –
Brazil 0.08936 0.17465 1.00000 – – –
Canada 0.23221 1.00000 1.00000 1.00000 – –
Denmark 0.93305 1.00000 1.00000 1.00000 1.00000 –
Finland 0.99715 0.08152 1.00000 1.00000 0.99970 1.00000
France 0.06259 0.24731 1.00000 1.00000 1.00000 1.00000
Germany 3.6e-05 1.00000 1.00000 0.41914 1.00000 1.00000
Ireland 0.07498 1.00000 1.00000 0.96990 1.00000 1.00000
Italy 1.00000 0.29641 1.00000 1.00000 0.99996 1.00000
Mexico 0.98328 0.14773 1.00000 1.00000 0.99999 1.00000
Norway 1.00000 0.00016 0.52278 0.35130 0.12290 0.82722
Poland – – – – – –
Portugal 0.57234 0.99890 1.00000 1.00000 1.00000 1.00000
Spain 1.00000 0.79458 1.00000 1.00000 1.00000 1.00000
Sweden 0.36115 0.88015 1.00000 1.00000 1.00000 1.00000
Switzerland 0.69892 0.98767 1.00000 1.00000 1.00000 1.00000
UK 0.97454 0.01856 1.00000 1.00000 0.98605 1.00000
USA 0.00392 0.87885 1.00000 1.00000 1.00000 1.00000
Venezuela 0.00636 1.00000 1.00000 0.98831 1.00000 1.00000
Finland France Germany Ireland Italy Mexico
Austria – – – – – –
Belgium – – – – – –
Brazil – – – – – –
Canada – – – – – –
Denmark – – – – – –
Finland – – – – – –
France 1.00000 – – – – –
Germany 0.32534 0.67247 – – – –
Ireland 0.79644 0.98663 1.00000 – – –
Italy 1.00000 1.00000 0.94518 0.88697 – –
Mexico 1.00000 1.00000 0.62288 0.89253 1.00000 –
Norway 0.84309 0.22269 0.00029 0.04785 1.00000 0.77200
Poland – – – – – –
Portugal 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
Spain 1.00000 1.00000 0.99992 0.97855 1.00000 1.00000
Sweden 1.00000 1.00000 1.00000 0.99998 1.00000 1.00000
Switzerland 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
UK 1.00000 1.00000 0.01131 0.58927 1.00000 1.00000
USA 1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
Venezuela 0.84039 0.99835 1.00000 1.00000 0.98923 0.95923
Norway Poland Portugal Spain Sweden Switzerland
Austria – – – – – –
Belgium – – – – – –
Brazil – – – – – –
Canada – – – – – –
Denmark – – – – – –
Finland – – – – – –
France – – – – – –
Germany – – – – – –
Ireland – – – – – –
Italy – – – – – –
Mexico – – – – – –
Norway – – – – – –
Poland – – – – – –
Portugal 0.40597 – – – – –
Spain 1.00000 – 1.00000 – – –
Sweden 0.15421 – 1.00000 1.00000 – –
Switzerland 0.41511 – 1.00000 1.00000 1.00000 –
UK 0.85973 – 1.00000 1.00000 1.00000 1.00000
USA 0.02235 – 1.00000 1.00000 1.00000 1.00000
Venezuela 0.00406 – 1.00000 0.99995 1.00000 1.00000
UK USA
Austria – –
Belgium – –
Brazil – –
Canada – –
Denmark – –
Finland – –
France – –
Germany – –
Ireland – –
Italy – –
Mexico – –
Norway – –
Poland – –
Portugal – –
Spain – –
Sweden – –
Switzerland – –
UK – –
USA 0.75299 –
Venezuela 0.36126 1.00000
P value adjustment method: T2 (Sidak)
alternative hypothesis: two.sided
The significant differences are indeed the ones noticed in the regression analysis.
> #ANCOVA
> m3<-aov(Quantity~Country+UnitPrice, data=df2)
> summary(m3)
Df Sum Sq Mean Sq F value Pr(>F)
Country 20 21765 1088.3 3.126 7.51e-06 ***
UnitPrice 1 62 61.5 0.177 0.674
Residuals 478 166391 348.1
—
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
The ANCOVA model predicts also the Quantity, with Country and unitPrice. Results reveal significant effects from country, and no effect from UnitPrice
Note:
Use visualization whenever possible in your tasks.
Done.
Appendix 1. the full R-script
setwd(“d:/R/”)
##########################
# Task 1: #
#################################################################
# User R. Discuss and explain the type and structure of the data.
# Derive descriptive statistics regarding this dataset,
# including measures of central tendency for two variables.
#################################################################
#load data
df<-read.csv(“invoice.csv”)
#describe data structure
cat(“Number of rows: “,nrow(df))
cat(“Number of columns: “,ncol(df))
head(df,1)
#transform some variables
df$UnitPrice<-as.numeric(gsub(“\$”, “”, df$UnitPrice))
df$Discount<-as.numeric(gsub(“\%”, “”, df$Discount))
df$ExtendedPrice<-as.numeric(gsub(“\,”, “”, gsub(“\$”, “”, df$ExtendedPrice)))
df$Freight<-as.numeric(gsub(“\,”, “”, (gsub(“\$”, “”, df$Freight))))
#descriptives
library(table1)
table1(~., data=subset(df, select = -c(Address, PostalCode) ))
#transform date columns
df$OrderDate<-as.Date(df$OrderDate, “%m/%d/%Y”)
df$RequiredDate<-as.Date(df$RequiredDate, “%m/%d/%Y”)
df$ShippedDate<-as.Date(df$ShippedDate, “%m/%d/%Y”)
#more descriptives
summary(df)
#central tendency: two variables
library(psych)
describe(subset(df, select=c(UnitPrice, Quantity)))
attach(df)
#histograms
par(mfrow=c(1,2))
hist(UnitPrice, col=rgb(0,0,1,0.2), main=””)
hist(Quantity, col=rgb(1,0,0,0.2), main=””)
#boxplots
par(mfrow=c(1,2))
boxplot(UnitPrice, col=rgb(0,0,1,0.2), main=”Unit Price”)
boxplot(Quantity, col=rgb(1,0,0,0.2), main=”Quantity”)
##########################
# Task 2: #
#################################################################
# Extract a sample for the following statistical tasks to help draw conclusions
# about the population. Perform an independent and dependent t-test on the sample.
# Discuss in detail the statistical results, and its meaning based on the dataset
# you have chosen.
#################################################################
#random sample of 500 cases
df2 <- df[sample(1:nrow(df), 500, replace=FALSE),]
#prepapre a variable for unitPrice over or lower the median
med<-median(df$UnitPrice)
library(expss)
df2$expensive<-recode(df2$UnitPrice, 0 %thru% med~0, TRUE ~1)
#independent-samples t-test
attach(df2)
par(mfrow=c(1,1))
boxplot(Quantity~expensive, col=rgb(1,0,0,0.2))
t.test(Quantity~expensive)
t.test(Quantity~expensive, data=subset(df2, Quantity<60)) #removing outliers
#paired samples t-test
#there is no appropriate data, so we create a variable
#indicating which quantity was actually delivered
df2$DeliveredQuantity<-df2$Quantity*runif(500,min=0.8, max=1.2)
attach(df2)
t.test(Quantity, DeliveredQuantity, paired = TRUE, alternative = “two.sided”)
plot(DeliveredQuantity~Quantity, col=”blue”)
##########################
# Task 3: #
#################################################################
# Perform a suitable multiple/logistic regression analysis on the sample dataset.
# Discuss the statistical results, and its meaning based on the dataset you have chosen.
#################################################################
df2$Country<-as.factor(df2$Country)
#extract month from date
library(lubridate)
df2$month<-month(df2$OrderDate)
#regression model
m1<-lm(Quantity ~ UnitPrice+Discount+month+Country, data=df2)
par(mfrow=c(2,2))
plot(m1)
#outliers
library(car)
car::outlierTest(m1, cutoff=Inf, n.max=10)
df2$cooksd <- cooks.distance(m1)
attach(df2)
df2$influential 4*mean(cooksd, na.rm=T))
#rerun the model
m2<-lm(Quantity ~ UnitPrice+Discount+month+Country, data=subset(df2, !df2$influential))
par(mfrow=c(2,2))
plot(m2)
library(stargazer)
stargazer(m2, type=”text”, no.space=T, single.row=T )
avPlots(m2)
library(effects)
plot(allEffects(m2))
library(ggplot2)
library(jtools)
q<-effect_plot(m2, pred=Country,
plot.points = TRUE, jitter = c(0.1,0))
q + theme(axis.text.x = element_text(angle = 90, hjust = 1))
##########################
# Task 4: #
#################################################################
# Perform an analysis of variance (ANOVA), the ANOVA F-Test,
# and analysis of covariance (ANCOVA). Discuss the statistical results, and its meaning
# based on the dataset you have chosen.
#################################################################
#ANOVA
par(mfrow=c(1,1))
boxplot(Quantity~Country, data=df2, horizontal=F, las=2)
df2$c<-as.factor(df2$Country)
m2<-aov(Quantity~Country, data=df2)
summary(m2)
# homogeneity of variance
plot(m2, 1)
car::leveneTest(Quantity~Country, data=df2)
# post-hoc
library(PMCMRplus)
tamhaneT2Test(m2)
#ANCOVA
m3<-aov(Quantity~Country+UnitPrice, data=df2)
summary(m3)
Appendix 2. Descriptives
Overall
(N=1059)
CustomerID
ALFKI
6 (0.6%)
ANATR
4 (0.4%)
ANTON
14 (1.3%)
AROUT
18 (1.7%)
BERGS
27 (2.5%)
BLAUS
7 (0.7%)
BLONP
15 (1.4%)
BOLID
2 (0.2%)
BONAP
21 (2.0%)
BOTTM
13 (1.2%)
BSBEV
14 (1.3%)
CACTU
4 (0.4%)
CHOPS
9 (0.8%)
COMMI
3 (0.3%)
CONSH
5 (0.5%)
DRACD
1 (0.1%)
DUMON
4 (0.4%)
EASTC
7 (0.7%)
ERNSH
44 (4.2%)
FAMIA
13 (1.2%)
FOLIG
16 (1.5%)
FOLKO
18 (1.7%)
FRANK
24 (2.3%)
FRANR
3 (0.3%)
FRANS
6 (0.6%)
FURIB
14 (1.3%)
GALED
3 (0.3%)
GODOS
6 (0.6%)
GOURL
16 (1.5%)
GREAL
15 (1.4%)
GROSR
2 (0.2%)
HANAR
11 (1.0%)
HILAA
25 (2.4%)
HUNGC
5 (0.5%)
HUNGO
26 (2.5%)
ISLAT
12 (1.1%)
KOENE
18 (1.7%)
LAMAI
17 (1.6%)
LAUGB
5 (0.5%)
LAZYK
2 (0.2%)
LEHMS
23 (2.2%)
LETSS
7 (0.7%)
LILAS
9 (0.8%)
LINOD
15 (1.4%)
LONEP
6 (0.6%)
MAGAA
12 (1.1%)
MAISD
8 (0.8%)
MEREP
25 (2.4%)
MORGK
7 (0.7%)
NORTS
5 (0.5%)
OCEAN
3 (0.3%)
OLDWO
9 (0.8%)
OTTIK
18 (1.7%)
PERIC
7 (0.7%)
PICCO
16 (1.5%)
PRINI
4 (0.4%)
QUEDE
13 (1.2%)
QUEEN
24 (2.3%)
QUICK
44 (4.2%)
RANCH
5 (0.5%)
RATTC
17 (1.6%)
REGGC
10 (0.9%)
RICAR
11 (1.0%)
RICSU
16 (1.5%)
SANTG
3 (0.3%)
SAVEA
64 (6.0%)
SEVES
14 (1.3%)
SIMOB
12 (1.1%)
SPECD
1 (0.1%)
SPLIR
6 (0.6%)
SUPRD
11 (1.0%)
THEBI
4 (0.4%)
THECR
5 (0.5%)
TOMSP
10 (0.9%)
TORTU
9 (0.8%)
TRADH
4 (0.4%)
TRAIH
7 (0.7%)
VAFFE
17 (1.6%)
VICTE
12 (1.1%)
VINET
4 (0.4%)
WANDK
12 (1.1%)
WARTH
28 (2.6%)
WELLI
11 (1.0%)
WHITC
23 (2.2%)
WILMK
7 (0.7%)
WOLZA
6 (0.6%)
CompanyName
Alfreds Futterkiste
6 (0.6%)
Ana Trujillo Emparedados y helados
4 (0.4%)
Antonio Moreno Taquería
14 (1.3%)
Around the Horn
18 (1.7%)
B’s Beverages
14 (1.3%)
Berglunds snabbköp
27 (2.5%)
Blauer See Delikatessen
7 (0.7%)
Blondel pčre et fils
15 (1.4%)
Bólido Comidas preparadas
2 (0.2%)
Bon app’
21 (2.0%)
Bottom-Dollar Markets
13 (1.2%)
Cactus Comidas para llevar
4 (0.4%)
Chop-suey Chinese
9 (0.8%)
Comércio Mineiro
3 (0.3%)
Consolidated Holdings
5 (0.5%)
Die Wandernde Kuh
12 (1.1%)
Drachenblut Delikatessen
1 (0.1%)
Du monde entier
4 (0.4%)
Eastern Connection
7 (0.7%)
Ernst Handel
44 (4.2%)
Familia Arquibaldo
13 (1.2%)
Folies gourmandes
16 (1.5%)
Folk och fä HB
18 (1.7%)
France restauration
3 (0.3%)
Franchi S.p.A.
6 (0.6%)
Frankenversand
24 (2.3%)
Furia Bacalhau e Frutos do Mar
14 (1.3%)
Galería del gastrónomo
3 (0.3%)
Godos Cocina Típica
6 (0.6%)
Gourmet Lanchonetes
16 (1.5%)
Great Lakes Food Market
15 (1.4%)
GROSELLA-Restaurante
2 (0.2%)
Hanari Carnes
11 (1.0%)
HILARIÓN-Abastos
25 (2.4%)
Hungry Coyote Import Store
5 (0.5%)
Hungry Owl All-Night Grocers
26 (2.5%)
Island Trading
12 (1.1%)
Königlich Essen
18 (1.7%)
La maison d’Asie
17 (1.6%)
Laughing Bacchus Wine Cellars
5 (0.5%)
Lazy K Kountry Store
2 (0.2%)
Lehmanns Marktstand
23 (2.2%)
Let’s Stop N Shop
7 (0.7%)
LILA-Supermercado
9 (0.8%)
LINO-Delicateses
15 (1.4%)
Lonesome Pine Restaurant
6 (0.6%)
Magazzini Alimentari Riuniti
12 (1.1%)
Maison Dewey
8 (0.8%)
Mčre Paillarde
25 (2.4%)
Morgenstern Gesundkost
7 (0.7%)
North/South
5 (0.5%)
Océano Atlántico Ltda.
3 (0.3%)
Old World Delicatessen
9 (0.8%)
Ottilies Käseladen
18 (1.7%)
Pericles Comidas clásicas
7 (0.7%)
Piccolo und mehr
16 (1.5%)
Princesa Isabel Vinhos
4 (0.4%)
Que Delícia
13 (1.2%)
Queen Cozinha
24 (2.3%)
QUICK-Stop
44 (4.2%)
Rancho grande
5 (0.5%)
Rattlesnake Canyon Grocery
17 (1.6%)
Reggiani Caseifici
10 (0.9%)
Ricardo Adocicados
11 (1.0%)
Richter Supermarkt
16 (1.5%)
Santé Gourmet
3 (0.3%)
Save-a-lot Markets
64 (6.0%)
Seven Seas Imports
14 (1.3%)
Simons bistro
12 (1.1%)
Spécialités du monde
1 (0.1%)
Split Rail Beer & Ale
6 (0.6%)
Supręmes délices
11 (1.0%)
The Big Cheese
4 (0.4%)
The Cracker Box
5 (0.5%)
Toms Spezialitäten
10 (0.9%)
Tortuga Restaurante
9 (0.8%)
Tradiçăo Hipermercados
4 (0.4%)
Trail’s Head Gourmet Provisioners
7 (0.7%)
Vaffeljernet
17 (1.6%)
Victuailles en stock
12 (1.1%)
Vins et alcools Chevalier
4 (0.4%)
Wartian Herkku
28 (2.6%)
Wellington Importadora
11 (1.0%)
White Clover Markets
23 (2.2%)
Wilman Kala
7 (0.7%)
Wolski Zajazd
6 (0.6%)
City
Aachen
1 (0.1%)
Albuquerque
17 (1.6%)
Anchorage
9 (0.8%)
Barcelona
3 (0.3%)
Barquisimeto
9 (0.8%)
Bergamo
12 (1.1%)
Berlin
6 (0.6%)
Bern
9 (0.8%)
Boise
64 (6.0%)
Bräcke
18 (1.7%)
Brandenburg
18 (1.7%)
Bruxelles
8 (0.8%)
Buenos Aires
12 (1.1%)
Butte
5 (0.5%)
Campinas
16 (1.5%)
Caracas
2 (0.2%)
Charleroi
11 (1.0%)
Cork
26 (2.5%)
Cowes
12 (1.1%)
Cunewalde
44 (4.2%)
Elgin
5 (0.5%)
Eugene
15 (1.4%)
Frankfurt a.M.
23 (2.2%)
Genčve
16 (1.5%)
Graz
44 (4.2%)
Helsinki
7 (0.7%)
I. de Margarita
15 (1.4%)
Kirkland
7 (0.7%)
Köln
18 (1.7%)
Křbenhavn
12 (1.1%)
Lander
6 (0.6%)
Leipzig
7 (0.7%)
Lille
16 (1.5%)
Lisboa
18 (1.7%)
London
63 (5.9%)
Ĺrhus
17 (1.6%)
Luleĺ
27 (2.5%)
Lyon
12 (1.1%)
Madrid
2 (0.2%)
Mannheim
7 (0.7%)
Marseille
21 (2.0%)
México D.F.
34 (3.2%)
Montréal
25 (2.4%)
München
24 (2.3%)
Münster
10 (0.9%)
Nantes
7 (0.7%)
Oulu
28 (2.6%)
Paris
1 (0.1%)
Portland
10 (0.9%)
Reggio Emilia
10 (0.9%)
Reims
4 (0.4%)
Resende
11 (1.0%)
Rio de Janeiro
35 (3.3%)
Salzburg
16 (1.5%)
San Cristóbal
25 (2.4%)
San Francisco
7 (0.7%)
Săo Paulo
44 (4.2%)
Seattle
23 (2.2%)
Sevilla
6 (0.6%)
Stavern
3 (0.3%)
Strasbourg
15 (1.4%)
Stuttgart
12 (1.1%)
Torino
6 (0.6%)
Toulouse
17 (1.6%)
Tsawassen
13 (1.2%)
Vancouver
5 (0.5%)
Walla Walla
2 (0.2%)
Warszawa
6 (0.6%)
Region
651 (61.5%)
AK
9 (0.8%)
BC
18 (1.7%)
CA
7 (0.7%)
Co. Cork
26 (2.5%)
DF
2 (0.2%)
ID
64 (6.0%)
Isle of Wight
12 (1.1%)
Lara
9 (0.8%)
MT
5 (0.5%)
NM
17 (1.6%)
Nueva Esparta
15 (1.4%)
OR
30 (2.8%)
Québec
25 (2.4%)
RJ
35 (3.3%)
SP
71 (6.7%)
Táchira
25 (2.4%)
WA
32 (3.0%)
WY
6 (0.6%)
Country
Argentina
12 (1.1%)
Austria
60 (5.7%)
Belgium
19 (1.8%)
Brazil
106 (10.0%)
Canada
43 (4.1%)
Denmark
29 (2.7%)
Finland
35 (3.3%)
France
93 (8.8%)
Germany
170 (16.1%)
Ireland
26 (2.5%)
Italy
28 (2.6%)
Mexico
34 (3.2%)
Norway
3 (0.3%)
Poland
6 (0.6%)
Portugal
18 (1.7%)
Spain
11 (1.0%)
Sweden
45 (4.2%)
Switzerland
25 (2.4%)
UK
75 (7.1%)
USA
170 (16.1%)
Venezuela
51 (4.8%)
Salesperson
Andrew Fuller
102 (9.6%)
Anne Dodsworth
45 (4.2%)
Janet Leverling
184 (17.4%)
Laura Callahan
124 (11.7%)
Margaret Peacock
218 (20.6%)
Michael Suyama
86 (8.1%)
Nancy Davolio
156 (14.7%)
Robert King
91 (8.6%)
Steven Buchanan
53 (5.0%)
OrderID
Mean (SD)
10600 (117)
Median [Min, Max]
10600 [10400, 10800]
OrderDate
1/1/2019
7 (0.7%)
1/10/2019
5 (0.5%)
1/13/2019
1 (0.1%)
1/14/2019
5 (0.5%)
1/15/2019
2 (0.2%)
1/16/2019
7 (0.7%)
1/17/2019
4 (0.4%)
1/2/2019
2 (0.2%)
1/20/2019
2 (0.2%)
1/21/2019
8 (0.8%)
1/22/2019
1 (0.1%)
1/23/2019
5 (0.5%)
1/24/2019
2 (0.2%)
1/27/2019
3 (0.3%)
1/28/2019
1 (0.1%)
1/29/2019
2 (0.2%)
1/3/2019
5 (0.5%)
1/30/2019
7 (0.7%)
1/31/2019
2 (0.2%)
1/6/2019
1 (0.1%)
1/7/2019
8 (0.8%)
1/8/2019
3 (0.3%)
1/9/2019
2 (0.2%)
10/1/2019
4 (0.4%)
10/10/2019
4 (0.4%)
10/13/2019
5 (0.5%)
10/14/2019
6 (0.6%)
10/15/2019
2 (0.2%)
10/16/2019
6 (0.6%)
10/17/2019
5 (0.5%)
10/2/2019
2 (0.2%)
10/20/2019
2 (0.2%)
10/21/2019
5 (0.5%)
10/22/2019
9 (0.8%)
10/23/2019
2 (0.2%)
10/24/2019
6 (0.6%)
10/27/2019
7 (0.7%)
10/28/2019
2 (0.2%)
10/29/2019
5 (0.5%)
10/3/2019
6 (0.6%)
10/30/2019
3 (0.3%)
10/31/2019
3 (0.3%)
10/6/2019
7 (0.7%)
10/7/2019
3 (0.3%)
10/8/2019
6 (0.6%)
10/9/2019
6 (0.6%)
11/10/2019
2 (0.2%)
11/11/2019
4 (0.4%)
11/12/2019
3 (0.3%)
11/13/2019
4 (0.4%)
11/14/2019
4 (0.4%)
11/17/2019
2 (0.2%)
11/18/2019
4 (0.4%)
11/19/2019
8 (0.8%)
11/20/2019
6 (0.6%)
11/21/2019
3 (0.3%)
11/24/2019
6 (0.6%)
11/25/2019
3 (0.3%)
11/26/2019
4 (0.4%)
11/27/2019
8 (0.8%)
11/28/2019
4 (0.4%)
11/3/2019
5 (0.5%)
11/4/2019
7 (0.7%)
11/5/2019
3 (0.3%)
11/6/2019
3 (0.3%)
11/7/2019
6 (0.6%)
12/1/2019
3 (0.3%)
12/10/2019
3 (0.3%)
12/11/2019
5 (0.5%)
12/12/2019
2 (0.2%)
12/15/2019
5 (0.5%)
12/16/2019
5 (0.5%)
12/17/2019
4 (0.4%)
12/18/2019
7 (0.7%)
12/19/2019
5 (0.5%)
12/2/2019
6 (0.6%)
12/22/2019
8 (0.8%)
12/23/2019
5 (0.5%)
12/24/2019
6 (0.6%)
12/25/2019
5 (0.5%)
12/26/2019
8 (0.8%)
12/29/2019
6 (0.6%)
12/3/2019
5 (0.5%)
12/30/2019
8 (0.8%)
12/31/2019
4 (0.4%)
12/4/2019
1 (0.1%)
12/5/2019
4 (0.4%)
12/8/2019
8 (0.8%)
12/9/2019
1 (0.1%)
2/10/2019
5 (0.5%)
2/11/2019
3 (0.3%)
2/12/2019
6 (0.6%)
2/13/2019
2 (0.2%)
2/14/2019
7 (0.7%)
2/17/2019
2 (0.2%)
2/18/2019
3 (0.3%)
2/19/2019
6 (0.6%)
2/20/2019
2 (0.2%)
2/21/2019
5 (0.5%)
2/24/2019
4 (0.4%)
2/25/2019
3 (0.3%)
2/26/2019
5 (0.5%)
2/27/2019
3 (0.3%)
2/28/2019
5 (0.5%)
2/3/2019
3 (0.3%)
2/4/2019
3 (0.3%)
2/5/2019
5 (0.5%)
2/6/2019
3 (0.3%)
2/7/2019
4 (0.4%)
3/10/2019
3 (0.3%)
3/11/2019
5 (0.5%)
3/12/2019
2 (0.2%)
3/13/2019
6 (0.6%)
3/14/2019
3 (0.3%)
3/17/2019
5 (0.5%)
3/18/2019
1 (0.1%)
3/19/2019
4 (0.4%)
3/20/2019
4 (0.4%)
3/21/2019
1 (0.1%)
3/24/2019
5 (0.5%)
3/25/2019
4 (0.4%)
3/26/2019
6 (0.6%)
3/27/2019
2 (0.2%)
3/28/2019
2 (0.2%)
3/3/2019
2 (0.2%)
3/31/2019
5 (0.5%)
3/4/2019
6 (0.6%)
3/5/2019
5 (0.5%)
3/6/2019
4 (0.4%)
3/7/2019
2 (0.2%)
4/1/2019
2 (0.2%)
4/10/2019
3 (0.3%)
4/11/2019
6 (0.6%)
4/14/2019
1 (0.1%)
4/15/2019
4 (0.4%)
4/16/2019
2 (0.2%)
4/17/2019
1 (0.1%)
4/18/2019
5 (0.5%)
4/2/2019
4 (0.4%)
4/21/2019
4 (0.4%)
4/22/2019
8 (0.8%)
4/23/2019
5 (0.5%)
4/24/2019
6 (0.6%)
4/25/2019
3 (0.3%)
4/28/2019
3 (0.3%)
4/29/2019
5 (0.5%)
4/3/2019
3 (0.3%)
4/30/2019
4 (0.4%)
4/4/2019
4 (0.4%)
4/7/2019
3 (0.3%)
4/8/2019
2 (0.2%)
4/9/2019
3 (0.3%)
5/1/2019
8 (0.8%)
5/12/2019
6 (0.6%)
5/13/2019
4 (0.4%)
5/14/2019
9 (0.8%)
5/15/2019
2 (0.2%)
5/16/2019
4 (0.4%)
5/19/2019
8 (0.8%)
5/2/2019
2 (0.2%)
5/20/2019
2 (0.2%)
5/21/2019
4 (0.4%)
5/22/2019
1 (0.1%)
5/23/2019
5 (0.5%)
5/26/2019
2 (0.2%)
5/27/2019
3 (0.3%)
5/28/2019
7 (0.7%)
5/29/2019
2 (0.2%)
5/30/2019
9 (0.8%)
5/5/2019
5 (0.5%)
5/6/2019
3 (0.3%)
5/7/2019
3 (0.3%)
5/8/2019
5 (0.5%)
5/9/2019
2 (0.2%)
6/10/2019
5 (0.5%)
6/11/2019
2 (0.2%)
6/12/2019
6 (0.6%)
6/13/2019
1 (0.1%)
6/16/2019
2 (0.2%)
6/17/2019
4 (0.4%)
6/18/2019
4 (0.4%)
6/19/2019
7 (0.7%)
6/2/2019
5 (0.5%)
6/20/2019
4 (0.4%)
6/23/2019
6 (0.6%)
6/24/2019
2 (0.2%)
6/25/2019
2 (0.2%)
6/26/2019
4 (0.4%)
6/27/2019
2 (0.2%)
6/3/2019
3 (0.3%)
6/30/2019
4 (0.4%)
6/4/2019
5 (0.5%)
6/5/2019
2 (0.2%)
6/6/2019
4 (0.4%)
6/9/2019
2 (0.2%)
7/1/2019
1 (0.1%)
7/10/2019
3 (0.3%)
7/11/2019
6 (0.6%)
7/14/2019
2 (0.2%)
7/15/2019
1 (0.1%)
7/16/2019
4 (0.4%)
7/17/2019
1 (0.1%)
7/18/2019
4 (0.4%)
7/2/2019
4 (0.4%)
7/21/2019
4 (0.4%)
7/22/2019
8 (0.8%)
7/23/2019
1 (0.1%)
7/24/2019
3 (0.3%)
7/25/2019
4 (0.4%)
7/28/2019
5 (0.5%)
7/29/2019
5 (0.5%)
7/3/2019
2 (0.2%)
7/30/2019
1 (0.1%)
7/31/2019
5 (0.5%)
7/4/2019
1 (0.1%)
7/7/2019
5 (0.5%)
7/8/2019
2 (0.2%)
7/9/2019
5 (0.5%)
8/1/2019
3 (0.3%)
8/11/2019
5 (0.5%)
8/12/2019
3 (0.3%)
8/13/2019
2 (0.2%)
8/14/2019
3 (0.3%)
8/15/2019
8 (0.8%)
8/18/2019
3 (0.3%)
8/19/2019
5 (0.5%)
8/20/2019
4 (0.4%)
8/21/2019
2 (0.2%)
8/22/2019
4 (0.4%)
8/25/2019
6 (0.6%)
8/26/2019
2 (0.2%)
8/27/2019
6 (0.6%)
8/28/2019
4 (0.4%)
8/29/2019
3 (0.3%)
8/4/2019
2 (0.2%)
8/5/2019
6 (0.6%)
8/6/2019
2 (0.2%)
8/7/2019
8 (0.8%)
8/8/2019
3 (0.3%)
9/1/2019
4 (0.4%)
9/10/2019
6 (0.6%)
9/11/2019
3 (0.3%)
9/12/2019
4 (0.4%)
9/15/2019
4 (0.4%)
9/16/2019
5 (0.5%)
9/17/2019
5 (0.5%)
9/18/2019
4 (0.4%)
9/19/2019
3 (0.3%)
9/2/2019
5 (0.5%)
9/22/2019
5 (0.5%)
9/23/2019
5 (0.5%)
9/24/2019
3 (0.3%)
9/25/2019
6 (0.6%)
9/26/2019
4 (0.4%)
9/29/2019
3 (0.3%)
9/3/2019
1 (0.1%)
9/30/2019
5 (0.5%)
9/4/2019
9 (0.8%)
9/5/2019
7 (0.7%)
9/8/2019
1 (0.1%)
9/9/2019
3 (0.3%)
RequiredDate
1/1/2011
1 (0.1%)
1/12/2011
4 (0.4%)
1/13/2011
3 (0.3%)
1/14/2011
4 (0.4%)
1/15/2011
7 (0.7%)
1/16/2011
3 (0.3%)
1/19/2011
8 (0.8%)
1/2/2011
6 (0.6%)
1/20/2011
5 (0.5%)
1/21/2011
6 (0.6%)
1/22/2011
5 (0.5%)
1/23/2011
5 (0.5%)
1/26/2011
6 (0.6%)
1/27/2011
8 (0.8%)
1/28/2011
4 (0.4%)
1/29/2019
7 (0.7%)
1/31/2019
5 (0.5%)
1/5/2011
8 (0.8%)
1/6/2011
1 (0.1%)
1/7/2011
3 (0.3%)
1/8/2011
3 (0.3%)
1/9/2011
2 (0.2%)
10/1/2019
3 (0.3%)
10/10/2019
4 (0.4%)
10/13/2019
7 (0.7%)
10/14/2019
5 (0.5%)
10/15/2019
6 (0.6%)
10/16/2019
4 (0.4%)
10/17/2019
3 (0.3%)
10/2/2019
9 (0.8%)
10/20/2019
9 (0.8%)
10/21/2019
5 (0.5%)
10/22/2019
3 (0.3%)
10/23/2019
6 (0.6%)
10/24/2019
4 (0.4%)
10/27/2019
3 (0.3%)
10/28/2019
5 (0.5%)
10/29/2019
1 (0.1%)
10/3/2019
7 (0.7%)
10/30/2019
5 (0.5%)
10/31/2019
1 (0.1%)
10/6/2019
1 (0.1%)
10/7/2019
3 (0.3%)
10/8/2019
7 (0.7%)
10/9/2019
5 (0.5%)
11/11/2019
8 (0.8%)
11/12/2019
2 (0.2%)
11/13/2019
3 (0.3%)
11/14/2019
8 (0.8%)
11/17/2019
4 (0.4%)
11/18/2019
5 (0.5%)
11/19/2019
11 (1.0%)
11/21/2019
6 (0.6%)
11/24/2019
9 (0.8%)
11/26/2019
1 (0.1%)
11/27/2019
1 (0.1%)
11/28/2019
6 (0.6%)
11/3/2019
3 (0.3%)
11/5/2019
4 (0.4%)
11/6/2019
8 (0.8%)
11/7/2019
4 (0.4%)
12/1/2019
3 (0.3%)
12/10/2019
7 (0.7%)
12/11/2019
6 (0.6%)
12/12/2019
3 (0.3%)
12/15/2019
2 (0.2%)
12/16/2019
7 (0.7%)
12/17/2019
8 (0.8%)
12/18/2019
6 (0.6%)
12/19/2019
3 (0.3%)
12/2/2019
7 (0.7%)
12/22/2019
6 (0.6%)
12/23/2019
3 (0.3%)
12/24/2019
4 (0.4%)
12/25/2019
10 (0.9%)
12/26/2019
4 (0.4%)
12/29/2019
4 (0.4%)
12/3/2019
3 (0.3%)
12/30/2019
8 (0.8%)
12/31/2019
5 (0.5%)
12/4/2019
3 (0.3%)
12/5/2019
6 (0.6%)
12/8/2019
2 (0.2%)
12/9/2019
4 (0.4%)
2/10/2019
1 (0.1%)
2/11/2019
5 (0.5%)
2/12/2019
2 (0.2%)
2/13/2019
16 (1.5%)
2/14/2019
6 (0.6%)
2/17/2019
2 (0.2%)
2/18/2019
9 (0.8%)
2/19/2019
1 (0.1%)
2/20/2019
3 (0.3%)
2/21/2019
2 (0.2%)
2/24/2019
3 (0.3%)
2/25/2019
1 (0.1%)
2/3/2019
1 (0.1%)
2/4/2019
3 (0.3%)
2/5/2019
3 (0.3%)
2/6/2011
3 (0.3%)
2/6/2019
4 (0.4%)
2/7/2019
5 (0.5%)
3/10/2019
4 (0.4%)
3/11/2019
3 (0.3%)
3/12/2019
8 (0.8%)
3/13/2019
2 (0.2%)
3/14/2019
7 (0.7%)
3/17/2019
2 (0.2%)
3/18/2019
6 (0.6%)
3/19/2019
2 (0.2%)
3/20/2019
2 (0.2%)
3/21/2019
5 (0.5%)
3/24/2019
1 (0.1%)
3/25/2019
1 (0.1%)
3/26/2019
5 (0.5%)
3/27/2019
5 (0.5%)
3/28/2019
5 (0.5%)
3/3/2019
3 (0.3%)
3/31/2019
2 (0.2%)
3/4/2019
4 (0.4%)
3/5/2019
9 (0.8%)
3/6/2019
3 (0.3%)
3/7/2019
4 (0.4%)
4/1/2019
7 (0.7%)
4/10/2019
4 (0.4%)
4/11/2019
3 (0.3%)
4/14/2019
5 (0.5%)
4/16/2019
4 (0.4%)
4/17/2019
4 (0.4%)
4/18/2019
1 (0.1%)
4/2/2019
5 (0.5%)
4/21/2019
5 (0.5%)
4/23/2019
6 (0.6%)
4/24/2019
2 (0.2%)
4/25/2019
2 (0.2%)
4/28/2019
5 (0.5%)
4/29/2019
2 (0.2%)
4/3/2019
4 (0.4%)
4/30/2019
4 (0.4%)
4/4/2019
2 (0.2%)
4/7/2019
7 (0.7%)
4/8/2019
11 (1.0%)
4/9/2019
2 (0.2%)
5/1/2019
3 (0.3%)
5/12/2019
1 (0.1%)
5/13/2019
4 (0.4%)
5/14/2019
2 (0.2%)
5/15/2019
1 (0.1%)
5/16/2019
5 (0.5%)
5/19/2019
4 (0.4%)
5/2/2019
4 (0.4%)
5/20/2019
8 (0.8%)
5/22/2019
6 (0.6%)
5/26/2019
3 (0.3%)
5/27/2019
5 (0.5%)
5/28/2019
9 (0.8%)
5/29/2019
8 (0.8%)
5/30/2019
2 (0.2%)
5/5/2019
3 (0.3%)
5/6/2019
2 (0.2%)
5/7/2019
8 (0.8%)
5/8/2019
3 (0.3%)
5/9/2019
9 (0.8%)
6/10/2019
7 (0.7%)
6/11/2019
4 (0.4%)
6/12/2019
2 (0.2%)
6/13/2019
4 (0.4%)
6/16/2019
8 (0.8%)
6/17/2019
4 (0.4%)
6/18/2019
4 (0.4%)
6/19/2019
1 (0.1%)
6/2/2019
5 (0.5%)
6/20/2019
5 (0.5%)
6/23/2019
2 (0.2%)
6/25/2019
4 (0.4%)
6/26/2019
2 (0.2%)
6/27/2019
9 (0.8%)
6/3/2019
3 (0.3%)
6/30/2019
5 (0.5%)
6/4/2019
3 (0.3%)
6/5/2019
5 (0.5%)
6/6/2019
2 (0.2%)
6/9/2019
6 (0.6%)
7/10/2019
6 (0.6%)
7/11/2019
1 (0.1%)
7/14/2019
2 (0.2%)
7/15/2019
3 (0.3%)
7/16/2019
4 (0.4%)
7/17/2019
7 (0.7%)
7/2/2019
5 (0.5%)
7/21/2019
3 (0.3%)
7/22/2019
4 (0.4%)
7/23/2019
2 (0.2%)
7/24/2019
4 (0.4%)
7/25/2019
2 (0.2%)
7/28/2019
4 (0.4%)
7/29/2019
3 (0.3%)
7/3/2019
2 (0.2%)
7/30/2019
4 (0.4%)
7/31/2019
2 (0.2%)
7/4/2019
8 (0.8%)
7/7/2019
5 (0.5%)
7/8/2019
3 (0.3%)
7/9/2019
5 (0.5%)
8/1/2019
1 (0.1%)
8/11/2019
2 (0.2%)
8/13/2019
2 (0.2%)
8/14/2019
1 (0.1%)
8/15/2019
4 (0.4%)
8/18/2019
4 (0.4%)
8/19/2019
8 (0.8%)
8/20/2019
1 (0.1%)
8/21/2019
3 (0.3%)
8/22/2019
4 (0.4%)
8/25/2019
5 (0.5%)
8/26/2019
6 (0.6%)
8/27/2019
3 (0.3%)
8/28/2019
5 (0.5%)
8/4/2019
5 (0.5%)
8/5/2019
2 (0.2%)
8/6/2019
5 (0.5%)
8/7/2019
3 (0.3%)
8/8/2019
6 (0.6%)
9/1/2019
2 (0.2%)
9/10/2019
4 (0.4%)
9/11/2019
3 (0.3%)
9/12/2019
11 (1.0%)
9/15/2019
3 (0.3%)
9/16/2019
5 (0.5%)
9/17/2019
4 (0.4%)
9/18/2019
2 (0.2%)
9/19/2019
4 (0.4%)
9/2/2019
6 (0.6%)
9/22/2019
8 (0.8%)
9/23/2019
2 (0.2%)
9/24/2019
3 (0.3%)
9/25/2019
2 (0.2%)
9/26/2019
3 (0.3%)
9/29/2019
4 (0.4%)
9/3/2019
2 (0.2%)
9/30/2019
5 (0.5%)
9/4/2019
8 (0.8%)
9/5/2019
3 (0.3%)
9/8/2019
3 (0.3%)
9/9/2019
3 (0.3%)
ShippedDate
1/1/2011
2 (0.2%)
1/10/2019
6 (0.6%)
1/13/2019
5 (0.5%)
1/14/2011
6 (0.6%)
1/14/2019
5 (0.5%)
1/15/2019
3 (0.3%)
1/16/2019
6 (0.6%)
1/17/2019
2 (0.2%)
1/19/2011
2 (0.2%)
1/2/2011
7 (0.7%)
1/20/2011
2 (0.2%)
1/21/2011
1 (0.1%)
1/21/2019
3 (0.3%)
1/22/2019
1 (0.1%)
1/24/2019
6 (0.6%)
1/27/2019
14 (1.3%)
1/28/2019
4 (0.4%)
1/30/2011
1 (0.1%)
1/30/2019
5 (0.5%)
1/31/2019
1 (0.1%)
1/5/2011
12 (1.1%)
1/6/2011
3 (0.3%)
1/7/2011
3 (0.3%)
1/8/2011
2 (0.2%)
1/8/2019
3 (0.3%)
1/9/2011
2 (0.2%)
1/9/2019
2 (0.2%)
10/1/2019
4 (0.4%)
10/10/2019
4 (0.4%)
10/13/2019
2 (0.2%)
10/14/2019
9 (0.8%)
10/15/2019
4 (0.4%)
10/16/2019
8 (0.8%)
10/17/2019
5 (0.5%)
10/20/2019
3 (0.3%)
10/21/2019
5 (0.5%)
10/22/2019
5 (0.5%)
10/23/2019
5 (0.5%)
10/24/2019
4 (0.4%)
10/27/2019
8 (0.8%)
10/29/2019
12 (1.1%)
10/3/2019
8 (0.8%)
10/30/2019
3 (0.3%)
10/31/2019
3 (0.3%)
10/7/2019
4 (0.4%)
10/8/2019
2 (0.2%)
10/9/2019
3 (0.3%)
11/10/2019
3 (0.3%)
11/11/2019
4 (0.4%)
11/12/2019
3 (0.3%)
11/14/2019
8 (0.8%)
11/17/2019
2 (0.2%)
11/18/2019
9 (0.8%)
11/20/2019
3 (0.3%)
11/21/2019
9 (0.8%)
11/24/2019
4 (0.4%)
11/25/2019
5 (0.5%)
11/26/2019
4 (0.4%)
11/27/2019
7 (0.7%)
11/28/2019
9 (0.8%)
11/4/2019
4 (0.4%)
11/5/2019
12 (1.1%)
11/7/2019
4 (0.4%)
12/10/2019
3 (0.3%)
12/12/2019
7 (0.7%)
12/15/2019
9 (0.8%)
12/16/2019
3 (0.3%)
12/17/2019
1 (0.1%)
12/18/2019
4 (0.4%)
12/19/2019
10 (0.9%)
12/2/2019
4 (0.4%)
12/22/2019
4 (0.4%)
12/23/2019
3 (0.3%)
12/24/2019
3 (0.3%)
12/25/2019
2 (0.2%)
12/26/2019
6 (0.6%)
12/3/2019
4 (0.4%)
12/31/2019
9 (0.8%)
12/4/2019
3 (0.3%)
12/5/2019
5 (0.5%)
12/8/2019
7 (0.7%)
12/9/2019
8 (0.8%)
2/10/2019
4 (0.4%)
2/11/2019
4 (0.4%)
2/12/2019
1 (0.1%)
2/13/2019
2 (0.2%)
2/14/2019
7 (0.7%)
2/18/2019
3 (0.3%)
2/19/2019
4 (0.4%)
2/20/2019
2 (0.2%)
2/21/2019
4 (0.4%)
2/24/2019
4 (0.4%)
2/25/2019
3 (0.3%)
2/26/2019
4 (0.4%)
2/27/2019
3 (0.3%)
2/28/2019
9 (0.8%)
2/3/2019
4 (0.4%)
2/4/2019
1 (0.1%)
2/6/2019
2 (0.2%)
2/7/2019
10 (0.9%)
3/11/2019
4 (0.4%)
3/12/2019
6 (0.6%)
3/13/2019
2 (0.2%)
3/14/2019
16 (1.5%)
3/18/2019
4 (0.4%)
3/19/2019
2 (0.2%)
3/21/2019
10 (0.9%)
3/24/2019
4 (0.4%)
3/25/2019
5 (0.5%)
3/26/2019
1 (0.1%)
3/28/2019
3 (0.3%)
3/3/2019
8 (0.8%)
3/31/2019
4 (0.4%)
3/4/2019
6 (0.6%)
3/5/2019
3 (0.3%)
3/6/2019
2 (0.2%)
3/7/2019
3 (0.3%)
4/1/2019
3 (0.3%)
4/10/2019
4 (0.4%)
4/11/2019
8 (0.8%)
4/16/2019
5 (0.5%)
4/17/2019
2 (0.2%)
4/18/2019
4 (0.4%)
4/2/2019
5 (0.5%)
4/21/2019
4 (0.4%)
4/22/2019
2 (0.2%)
4/24/2019
4 (0.4%)
4/25/2019
2 (0.2%)
4/28/2019
5 (0.5%)
4/29/2019
7 (0.7%)
4/3/2019
3 (0.3%)
4/4/2019
3 (0.3%)
4/7/2019
4 (0.4%)
4/8/2019
2 (0.2%)
4/9/2019
3 (0.3%)
5/1/2019
8 (0.8%)
5/12/2019
6 (0.6%)
5/13/2019
2 (0.2%)
5/14/2019
3 (0.3%)
5/15/2019
3 (0.3%)
5/16/2019
7 (0.7%)
5/19/2019
6 (0.6%)
5/2/2019
5 (0.5%)
5/21/2019
4 (0.4%)
5/22/2019
3 (0.3%)
5/23/2019
13 (1.2%)
5/26/2019
2 (0.2%)
5/27/2019
3 (0.3%)
5/29/2019
4 (0.4%)
5/30/2019
9 (0.8%)
5/5/2019
3 (0.3%)
5/6/2019
4 (0.4%)
5/7/2019
6 (0.6%)
5/9/2019
6 (0.6%)
6/10/2019
5 (0.5%)
6/12/2019
2 (0.2%)
6/13/2019
7 (0.7%)
6/16/2019
3 (0.3%)
6/17/2019
3 (0.3%)
6/18/2019
5 (0.5%)
6/19/2019
2 (0.2%)
6/2/2019
4 (0.4%)
6/20/2019
3 (0.3%)
6/24/2019
2 (0.2%)
6/25/2019
4 (0.4%)
6/26/2019
1 (0.1%)
6/3/2019
5 (0.5%)
6/30/2019
14 (1.3%)
6/4/2019
5 (0.5%)
6/5/2019
6 (0.6%)
6/6/2019
13 (1.2%)
6/9/2019
4 (0.4%)
7/1/2019
3 (0.3%)
7/10/2019
3 (0.3%)
7/11/2019
2 (0.2%)
7/14/2019
8 (0.8%)
7/16/2019
7 (0.7%)
7/18/2019
5 (0.5%)
7/2/2019
1 (0.1%)
7/21/2019
3 (0.3%)
7/22/2019
3 (0.3%)
7/25/2019
7 (0.7%)
7/29/2019
6 (0.6%)
7/30/2019
3 (0.3%)
7/31/2019
3 (0.3%)
7/4/2019
8 (0.8%)
7/9/2019
5 (0.5%)
8/1/2019
14 (1.3%)
8/11/2019
6 (0.6%)
8/12/2019
8 (0.8%)
8/13/2019
3 (0.3%)
8/14/2019
5 (0.5%)
8/15/2019
1 (0.1%)
8/18/2019
4 (0.4%)
8/19/2019
7 (0.7%)
8/20/2019
6 (0.6%)
8/21/2019
9 (0.8%)
8/26/2019
7 (0.7%)
8/27/2019
1 (0.1%)
8/28/2019
2 (0.2%)
8/29/2019
2 (0.2%)
8/4/2019
1 (0.1%)
8/5/2019
4 (0.4%)
8/6/2019
2 (0.2%)
8/7/2019
2 (0.2%)
8/8/2019
5 (0.5%)
9/1/2019
6 (0.6%)
9/10/2019
6 (0.6%)
9/11/2019
6 (0.6%)
9/15/2019
8 (0.8%)
9/17/2019
3 (0.3%)
9/18/2019
6 (0.6%)
9/19/2019
10 (0.9%)
9/2/2019
5 (0.5%)
9/22/2019
3 (0.3%)
9/23/2019
6 (0.6%)
9/24/2019
3 (0.3%)
9/26/2019
7 (0.7%)
9/29/2019
3 (0.3%)
9/3/2019
9 (0.8%)
9/30/2019
8 (0.8%)
9/5/2019
2 (0.2%)
9/8/2019
6 (0.6%)
9/9/2019
2 (0.2%)
Shipper
Federal Shipping
317 (29.9%)
Speedy Express
339 (32.0%)
United Package
403 (38.1%)
ProductID
Mean (SD)
41.6 (21.8)
Median [Min, Max]
42.0 [1.00, 77.0]
ProductName
Alice Mutton
18 (1.7%)
Aniseed Syrup
7 (0.7%)
Boston Crab Meat
25 (2.4%)
Camembert Pierrot
21 (2.0%)
Carnarvon Tigers
12 (1.1%)
Chai
16 (1.5%)
Chang
18 (1.7%)
Chartreuse verte
13 (1.2%)
Chef Anton’s Cajun Seasoning
10 (0.9%)
Chef Anton’s Gumbo Mix
2 (0.2%)
Chocolade
5 (0.5%)
Côte de Blaye
10 (0.9%)
Escargots de Bourgogne
7 (0.7%)
Filo Mix
15 (1.4%)
Flřtemysost
21 (2.0%)
Geitost
16 (1.5%)
Genen Shouyu
4 (0.4%)
Gnocchi di nonna Alice
33 (3.1%)
Gorgonzola Telino
26 (2.5%)
Grandma’s Boysenberry Spread
2 (0.2%)
Gravad lax
2 (0.2%)
Guaraná Fantástica
19 (1.8%)
Gudbrandsdalsost
18 (1.7%)
Gula Malacca
15 (1.4%)
Gumbär Gummibärchen
19 (1.8%)
Gustaf’s Knäckebröd
9 (0.8%)
Ikura
19 (1.8%)
Inlagd Sill
15 (1.4%)
Ipoh Coffee
11 (1.0%)
Jack’s New England Clam Chowder
21 (2.0%)
Konbu
11 (1.0%)
Lakkalikööri
19 (1.8%)
Laughing Lumberjack Lager
4 (0.4%)
Longlife Tofu
6 (0.6%)
Louisiana Fiery Hot Pepper Sauce
18 (1.7%)
Louisiana Hot Spiced Okra
6 (0.6%)
Manjimup Dried Apples
19 (1.8%)
Mascarpone Fabioli
5 (0.5%)
Maxilaku
9 (0.8%)
Mishi Kobe Niku
4 (0.4%)
Mozzarella di Giovanni
16 (1.5%)
Nord-Ost Matjeshering
16 (1.5%)
Northwoods Cranberry Sauce
5 (0.5%)
NuNuCa Nuß-Nougat-Creme
6 (0.6%)
Original Frankfurter grüne Soße
17 (1.6%)
Outback Lager
17 (1.6%)
Pavlova
22 (2.1%)
Pâté chinois
16 (1.5%)
Perth Pasties
15 (1.4%)
Queso Cabrales
21 (2.0%)
Queso Manchego La Pastora
7 (0.7%)
Raclette Courdavault
31 (2.9%)
Ravioli Angelo
8 (0.8%)
Rhönbräu Klosterbier
25 (2.4%)
Röd Kaviar
11 (1.0%)
Rössle Sauerkraut
17 (1.6%)
Rřgede sild
9 (0.8%)
Sasquatch Ale
8 (0.8%)
Schoggi Schokolade
4 (0.4%)
Scottish Longbreads
16 (1.5%)
Singaporean Hokkien Fried Mee
16 (1.5%)
Sir Rodney’s Marmalade
4 (0.4%)
Sir Rodney’s Scones
23 (2.2%)
Sirop d’érable
13 (1.2%)
Spegesild
14 (1.3%)
Steeleye Stout
16 (1.5%)
Tarte au sucre
22 (2.1%)
Teatime Chocolate Biscuits
21 (2.0%)
Thüringer Rostbratwurst
15 (1.4%)
Tofu
15 (1.4%)
Tourtičre
22 (2.1%)
Tunnbröd
10 (0.9%)
Uncle Bob’s Organic Dried Pears
10 (0.9%)
Valkoinen suklaa
5 (0.5%)
Vegie-spread
7 (0.7%)
Wimmers gute Semmelknödel
14 (1.3%)
Zaanse koeken
15 (1.4%)
UnitPrice
Mean (SD)
26.1 (28.6)
Median [Min, Max]
18.6 [2.00, 264]
Quantity
Mean (SD)
24.1 (19.0)
Median [Min, Max]
20.0 [1.00, 130]
Discount
Mean (SD)
5.93 (8.41)
Median [Min, Max]
0 [0, 25.0]
ExtendedPrice
Mean (SD)
583 (824)
Median [Min, Max]
340 [4.80, 10500]
Freight
Mean (SD)
101 (139)
Median [Min, Max]
58.3 [0.140, 1010]
ShipName
Alfreds Futterkiste
6 (0.6%)
Ana Trujillo Emparedados y helados
4 (0.4%)
Antonio Moreno Taquería
14 (1.3%)
Around the Horn
18 (1.7%)
B’s Beverages
14 (1.3%)
Berglunds snabbköp
27 (2.5%)
Blauer See Delikatessen
7 (0.7%)
Blondel pčre et fils
15 (1.4%)
Bólido Comidas preparadas
2 (0.2%)
Bon app’
21 (2.0%)
Bottom-Dollar Markets
13 (1.2%)
Cactus Comidas para llevar
4 (0.4%)
Chop-suey Chinese
9 (0.8%)
Comércio Mineiro
3 (0.3%)
Consolidated Holdings
5 (0.5%)
Die Wandernde Kuh
12 (1.1%)
Drachenblut Delikatessen
1 (0.1%)
Du monde entier
4 (0.4%)
Eastern Connection
7 (0.7%)
Ernst Handel
44 (4.2%)
Familia Arquibaldo
13 (1.2%)
Folies gourmandes
16 (1.5%)
Folk och fä HB
18 (1.7%)
France restauration
3 (0.3%)
Franchi S.p.A.
6 (0.6%)
Frankenversand
24 (2.3%)
Furia Bacalhau e Frutos do Mar
14 (1.3%)
Galería del gastronómo
3 (0.3%)
Godos Cocina Típica
6 (0.6%)
Gourmet Lanchonetes
16 (1.5%)
Great Lakes Food Market
15 (1.4%)
GROSELLA-Restaurante
2 (0.2%)
Hanari Carnes
11 (1.0%)
HILARIÓN-Abastos
25 (2.4%)
Hungry Coyote Import Store
5 (0.5%)
Hungry Owl All-Night Grocers
26 (2.5%)
Island Trading
12 (1.1%)
Königlich Essen
18 (1.7%)
La maison d’Asie
17 (1.6%)
Laughing Bacchus Wine Cellars
5 (0.5%)
Lazy K Kountry Store
2 (0.2%)
Lehmanns Marktstand
23 (2.2%)
Let’s Stop N Shop
7 (0.7%)
LILA-Supermercado
9 (0.8%)
LINO-Delicateses
15 (1.4%)
Lonesome Pine Restaurant
6 (0.6%)
Magazzini Alimentari Riuniti
12 (1.1%)
Maison Dewey
8 (0.8%)
Mčre Paillarde
25 (2.4%)
Morgenstern Gesundkost
7 (0.7%)
North/South
5 (0.5%)
Océano Atlántico Ltda.
3 (0.3%)
Old World Delicatessen
9 (0.8%)
Ottilies Käseladen
18 (1.7%)
Pericles Comidas clásicas
7 (0.7%)
Piccolo und mehr
16 (1.5%)
Princesa Isabel Vinhos
4 (0.4%)
Que Delícia
13 (1.2%)
Queen Cozinha
24 (2.3%)
QUICK-Stop
44 (4.2%)
Rancho grande
5 (0.5%)
Rattlesnake Canyon Grocery
17 (1.6%)
Reggiani Caseifici
10 (0.9%)
Ricardo Adocicados
11 (1.0%)
Richter Supermarkt
16 (1.5%)
Santé Gourmet
3 (0.3%)
Save-a-lot Markets
64 (6.0%)
Seven Seas Imports
14 (1.3%)
Simons bistro
12 (1.1%)
Spécialités du monde
1 (0.1%)
Split Rail Beer & Ale
6 (0.6%)
Supręmes délices
11 (1.0%)
The Big Cheese
4 (0.4%)
The Cracker Box
5 (0.5%)
Toms Spezialitäten
10 (0.9%)
Tortuga Restaurante
9 (0.8%)
Tradiçăo Hipermercados
4 (0.4%)
Trail’s Head Gourmet Provisioners
7 (0.7%)
Vaffeljernet
17 (1.6%)
Victuailles en stock
12 (1.1%)
Vins et alcools Chevalier
4 (0.4%)
Wartian Herkku
28 (2.6%)
Wellington Importadora
11 (1.0%)
White Clover Markets
23 (2.2%)
Wilman Kala
7 (0.7%)
Wolski Zajazd
6 (0.6%)
ShipAddress
1 rue Alsace-Lorraine
17 (1.6%)
1029 – 12th Ave. S.
23 (2.2%)
12 Orchestra Terrace
2 (0.2%)
12, rue des Bouchers
21 (2.0%)
184, chaussée de Tournai
16 (1.5%)
187 Suffolk Ln.
64 (6.0%)
2, rue du Commerce
12 (1.1%)
23 Tsawassen Blvd.
13 (1.2%)
2319 Elm St.
5 (0.5%)
24, place Kléber
15 (1.4%)
25, rue Lauriston
1 (0.1%)
2732 Baker Blvd.
15 (1.4%)
2743 Bering St.
9 (0.8%)
2817 Milton Dr.
17 (1.6%)
35 King George
7 (0.7%)
43 rue St. Laurent
25 (2.4%)
54, rue Royale
3 (0.3%)
55 Grizzly Peak Rd.
5 (0.5%)
59 rue de l’Abbaye
4 (0.4%)
5Ş Ave. Los Palos Grandes
2 (0.2%)
67, rue des Cinquante Otages
4 (0.4%)
722 DaVinci Blvd.
7 (0.7%)
8 Johnstown Road
26 (2.5%)
87 Polk St. Suite 5
7 (0.7%)
89 Chiaroscuro Rd.
6 (0.6%)
89 Jefferson Way Suite 2
4 (0.4%)
90 Wadhurst Rd.
14 (1.3%)
Adenauerallee 900
12 (1.1%)
Alameda dos Canŕrios, 891
24 (2.3%)
Av. Brasil, 442
16 (1.5%)
Av. Copacabana, 267
11 (1.0%)
Av. del Libertador 900
5 (0.5%)
Av. dos Lusíadas, 23
3 (0.3%)
Av. Inęs de Castro, 414
4 (0.4%)
Avda. Azteca 123
9 (0.8%)
Avda. de la Constitución 2222
4 (0.4%)
Ave. 5 de Mayo Porlamar
15 (1.4%)
Berguvsvägen 8
27 (2.5%)
Berkeley Gardens 12 Brewery
5 (0.5%)
Berliner Platz 43
24 (2.3%)
Boulevard Tirou, 255
11 (1.0%)
Brook Farm Stratford St. Mary
18 (1.7%)
C/ Araquil, 67
2 (0.2%)
C/ Romero, 33
6 (0.6%)
Calle Dr. Jorge Cash 321
7 (0.7%)
Carrera 22 con Ave. Carlos Soublette #8-35
25 (2.4%)
Carrera 52 con Ave. Bolívar #65-98 Llano Largo
9 (0.8%)
Cerrito 333
4 (0.4%)
City Center Plaza 516 Main St.
5 (0.5%)
Erling Skakkes gate 78
3 (0.3%)
Estrada da saúde n. 58
4 (0.4%)
Fauntleroy Circus
14 (1.3%)
Forsterstr. 57
7 (0.7%)
Garden House Crowther Way
12 (1.1%)
Geislweg 14
16 (1.5%)
Hauptstr. 31
9 (0.8%)
Heerstr. 22
7 (0.7%)
Ing. Gustavo Moncada 8585 Piso 20-A
3 (0.3%)
Jardim das rosas n. 32
14 (1.3%)
Keskuskatu 45
7 (0.7%)
Kirchgasse 6
44 (4.2%)
Ĺkergatan 24
18 (1.7%)
Luisenstr. 48
10 (0.9%)
Magazinweg 7
23 (2.2%)
Mataderos 2312
14 (1.3%)
Maubelstr. 90
18 (1.7%)
Mehrheimerstr. 369
18 (1.7%)
Obere Str. 57
6 (0.6%)
P.O. Box 555
6 (0.6%)
Rambla de Cataluńa, 23
3 (0.3%)
Rua da Panificadora, 12
13 (1.2%)
Rua do Mercado, 12
11 (1.0%)
Rua do Paço, 67
11 (1.0%)
Rua Orós, 92
13 (1.2%)
Rue Joseph-Bens 532
8 (0.8%)
Smagslřget 45
17 (1.6%)
South House 300 Queensbridge
5 (0.5%)
Starenweg 5
16 (1.5%)
Strada Provinciale 124
10 (0.9%)
Taucherstraße 10
44 (4.2%)
Torikatu 38
28 (2.6%)
ul. Filtrowa 68
6 (0.6%)
Via Ludovico il Moro 22
12 (1.1%)
Via Monte Bianco 34
6 (0.6%)
Vinbćltet 34
12 (1.1%)
Walserweg 21
1 (0.1%)
ShipCity
Aachen
1 (0.1%)
Albuquerque
17 (1.6%)
Anchorage
9 (0.8%)
Barcelona
3 (0.3%)
Barquisimeto
9 (0.8%)
Bergamo
12 (1.1%)
Berlin
6 (0.6%)
Bern
9 (0.8%)
Boise
64 (6.0%)
Bräcke
18 (1.7%)
Brandenburg
18 (1.7%)
Bruxelles
8 (0.8%)
Buenos Aires
12 (1.1%)
Butte
5 (0.5%)
Campinas
16 (1.5%)
Caracas
2 (0.2%)
Charleroi
11 (1.0%)
Colchester
18 (1.7%)
Cork
26 (2.5%)
Cowes
12 (1.1%)
Cunewalde
44 (4.2%)
Elgin
5 (0.5%)
Eugene
15 (1.4%)
Frankfurt a.M.
23 (2.2%)
Genčve
16 (1.5%)
Graz
44 (4.2%)
Helsinki
7 (0.7%)
I. de Margarita
15 (1.4%)
Kirkland
7 (0.7%)
Köln
18 (1.7%)
Křbenhavn
12 (1.1%)
Lander
6 (0.6%)
Leipzig
7 (0.7%)
Lille
16 (1.5%)
Lisboa
18 (1.7%)
London
45 (4.2%)
Ĺrhus
17 (1.6%)
Luleĺ
27 (2.5%)
Lyon
12 (1.1%)
Madrid
2 (0.2%)
Mannheim
7 (0.7%)
Marseille
21 (2.0%)
México D.F.
34 (3.2%)
Montréal
25 (2.4%)
München
24 (2.3%)
Münster
10 (0.9%)
Nantes
7 (0.7%)
Oulu
28 (2.6%)
Paris
1 (0.1%)
Portland
10 (0.9%)
Reggio Emilia
10 (0.9%)
Reims
4 (0.4%)
Resende
11 (1.0%)
Rio de Janeiro
35 (3.3%)
Salzburg
16 (1.5%)
San Cristóbal
25 (2.4%)
San Francisco
7 (0.7%)
Săo Paulo
44 (4.2%)
Seattle
23 (2.2%)
Sevilla
6 (0.6%)
Stavern
3 (0.3%)
Strasbourg
15 (1.4%)
Stuttgart
12 (1.1%)
Torino
6 (0.6%)
Toulouse
17 (1.6%)
Tsawassen
13 (1.2%)
Vancouver
5 (0.5%)
Walla Walla
2 (0.2%)
Warszawa
6 (0.6%)
ShipRegion
633 (59.8%)
AK
9 (0.8%)
BC
18 (1.7%)
CA
7 (0.7%)
Co. Cork
26 (2.5%)
DF
2 (0.2%)
Essex
18 (1.7%)
ID
64 (6.0%)
Isle of Wight
12 (1.1%)
Lara
9 (0.8%)
MT
5 (0.5%)
NM
17 (1.6%)
Nueva Esparta
15 (1.4%)
OR
30 (2.8%)
Québec
25 (2.4%)
RJ
35 (3.3%)
SP
71 (6.7%)
Táchira
25 (2.4%)
WA
32 (3.0%)
WY
6 (0.6%)
ShipPostalCode
26 (2.5%)
01-012
6 (0.6%)
01307
44 (4.2%)
02389-673
13 (1.2%)
02389-890
11 (1.0%)
04179
7 (0.7%)
04876-786
16 (1.5%)
05021
4 (0.4%)
05023
14 (1.3%)
05033
16 (1.5%)
05432-043
3 (0.3%)
05442-030
13 (1.2%)
05454-876
11 (1.0%)
05487-020
24 (2.3%)
05634-030
4 (0.4%)
08737-363
11 (1.0%)
1010
12 (1.1%)
10100
6 (0.6%)
1081
2 (0.2%)
1204
16 (1.5%)
12209
6 (0.6%)
13008
21 (2.0%)
14776
18 (1.7%)
1675
14 (1.3%)
1734
12 (1.1%)
1756
4 (0.4%)
21240
7 (0.7%)
24100
12 (1.1%)
28023
2 (0.2%)
3012
9 (0.8%)
31000
17 (1.6%)
3508
9 (0.8%)
4110
3 (0.3%)
41101
6 (0.6%)
42100
10 (0.9%)
44000
7 (0.7%)
44087
10 (0.9%)
4980
15 (1.4%)
5020
16 (1.5%)
5022
25 (2.4%)
50739
18 (1.7%)
51100
4 (0.4%)
52066
1 (0.1%)
59000
16 (1.5%)
59801
5 (0.5%)
60528
23 (2.2%)
67000
15 (1.4%)
68306
7 (0.7%)
69004
12 (1.1%)
70563
12 (1.1%)
75016
1 (0.1%)
8010
44 (4.2%)
8022
3 (0.3%)
80805
24 (2.3%)
8200
17 (1.6%)
82520
6 (0.6%)
83720
64 (6.0%)
87110
17 (1.6%)
90110
28 (2.6%)
94117
7 (0.7%)
97201
4 (0.4%)
97219
6 (0.6%)
97403
15 (1.4%)
97827
5 (0.5%)
98034
7 (0.7%)
98124
23 (2.2%)
99362
2 (0.2%)
99508
9 (0.8%)
B-1180
8 (0.8%)
B-6000
11 (1.0%)
CO7 6JX
18 (1.7%)
EC2 5NT
14 (1.3%)
H1J 1C3
25 (2.4%)
OX15 4NB
14 (1.3%)
PO31 7PJ
12 (1.1%)
S-844 67
18 (1.7%)
S-958 22
27 (2.5%)
SW7 1RZ
5 (0.5%)
T2F 8M4
13 (1.2%)
V3F 2K1
5 (0.5%)
WX1 6LT
5 (0.5%)
WX3 6FW
7 (0.7%)
ShipCountry
Argentina
12 (1.1%)
Austria
60 (5.7%)
Belgium
19 (1.8%)
Brazil
106 (10.0%)
Canada
43 (4.1%)
Denmark
29 (2.7%)
Finland
35 (3.3%)
France
93 (8.8%)
Germany
170 (16.1%)
Ireland
26 (2.5%)
Italy
28 (2.6%)
Mexico
34 (3.2%)
Norway
3 (0.3%)
Poland
6 (0.6%)
Portugal
18 (1.7%)
Spain
11 (1.0%)
Sweden
45 (4.2%)
Switzerland
25 (2.4%)
UK
75 (7.1%)
USA
170 (16.1%)
Venezuela
51 (4.8%)
CategoryName
Beverages
176 (16.6%)
Condiments
106 (10.0%)
Confections
171 (16.1%)
Dairy Products
182 (17.2%)
Grains/Cereals
105 (9.9%)
Meat/Poultry
90 (8.5%)
Produce
67 (6.3%)
Seafood
162 (15.3%)
The post (Supermarket Invoices) Brief Description: A supermarket invoice dataset describes the sold products, appeared first on PapersSpot.