Sample Data for Supermarket Profits. Supermarket Food Sals Nonfood Sales Number

Question

Sample Data for Supermarket Profits. Supermarket Food Sals Nonfood Sales Number (tens of thousands (tens of thousands (thousands of (thousands of of dollars) Store Size square feet) 35 Profit of dollars) dollars) 305 130 189 175 101 269 421 195 282 203 35 98 83 76 93 20 15 17 27 16 28 46 56 12 40 32 16 27 35 57 31 92 10 23 1. Describe the Skewness and Kurtosis of each variable. See an example of the table below Table 2: Summary statistics for the quarterly series Statistic Mean Std. Dev Skewness 0.2932 -0.4532 -0.1282 00673 0.3238 0.1870 Kurtosis 2.0376 1.8808 1.7895 1.7175 2.3089 1.4557 Jarque-Bera 6.7198 109764 8.1022" 8.8002"446 13.3611 In SP 4.2515 3.8416 4.3551 20.1439 3.5474 1.0830 0.5362 0.7043 0.2585 0.7365 1.6622 0.195 In IP In CPI InM ?? In ER

Explanation / Answer

R- code for computing skewness and kurtosis of each variable is given below:

First, we have to install "moments" package in R. Then load it into current session using following code:

> install.packages("moments")

> library("moments", lib.loc="~/R/win-library/3.2")

Food sales:

> fs<-c(305,130,189,175,101,269,421,195,282,203)

> skewness(fs)
[1] 0.6852605

> kurtosis(fs)
[1] 2.872009

Non Food sales:

> nfs<-c(35,98,83,76,93,77,44,57,31,92)

> skewness(nfs)
[1] -0.3810678

> kurtosis(nfs)
[1] 1.616494

Store Size:

> sz<-c(35,22,27,16,28,46,56,12,40,32)

> skewness(sz)
[1] 0.3229752

> kurtosis(sz)
[1] 2.322725

Profit:

> pft<-c(20,15,17,9,16,27,35,7,22,23)

> skewness(pft)
[1] 0.3465507

> kurtosis(pft)
[1] 2.61977

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