i would like the R code to perform the same answer for table $5.$ i would like to replicate Table $5.$ bellow attached is the data and table $5.$ i would like the steps and how to perform the table using R program please. thank you

Table $5.$ Individual country impact on coefficient estimates

Notes: Table entries are DFBetas calculated as $\frac{(hat())-\text{h}\text{a}\text{t}({)}_{-i}}{h}at({)}_{\text{h}\text{a}\text{t}()}$ which is the difference between the estimated

regression coefficient when each country $i$ is included and individually excluded from the estimation scaled

by the estimated SE of the coefficient $($Belsley et al$.,1980).$

the SD$.$ We have followed the approach of Belsley,

et al$.(1980,$ p$.28)$ who suggest that observations for

which the DFBeta exceeds $\frac{2}{\sqrt[\phantom{2}]{n}}$ warrant further

examination, so we will focus on those cases where

the calculated DFBetas exceed $\frac{2}{\sqrt[\phantom{2}]{26}}=0\mathrm{.}3922.{?}^8$ The

DFBeta influence statistics for each country and

coefficient are displayed in Table $5.$

Four of the countries $-$ Ecuador, Korea, Pakistan

and Switzerland $-$ have DFBetas that exceed $0\mathrm{.}3922$

on one or more coefficients. The inclusion of Ecuador

increases the coefficient on the collectivism index by

about $0\mathrm{.}637$ SEs and decreases the coefficient on

internet usage by about $0\mathrm{.}406$ SEs. The inclusion of

Korea in the analysis decreases the coefficient

on internet usage by about $0\mathrm{.}532$ SEs, while

Pakistan increases the coefficient on the collectivism

index by about $0\mathrm{.}397.$ Finally, the inclusion of

Switzerland increases the coefficient on income by

$0\mathrm{.}898$SEs and decreases the coefficient on internet

usage by $0\mathrm{.}61$ SEs. Ecuador and Pakistan both tend

to increase the coefficient on collectivism, while

Ecuador, Korea and Switzerland all serve to decrease

the coefficient on internet usage, though the actual

change in the magnitude of the estimated coefficients

is extremely small. The influence statistics serve to

highlight the countries that have a particularly strong

influence on the estimates; future research may seek

to study piracy at a more disaggregated level within

each country to discover factors useful in preventing

intellectual property theft.

v$.$ Conclusions

We empirically examined the rate of motion$-$picture

piracy across a sample of $26$ diverse countries in this

preliminary analysis. The level of piracy was

explained empirically by the level of income, the

cost of enforcing property rights, the level of

collectivism present in a country's social institutions

and the level of internet usage. The results of a cross$-$

${?}^8$ It is also common practice to use $1$ as the threshold for further investigation $($Bollen and Jackman, $1990).$ We have opted to

use a more stringent criterion in our analysis by applying the $\frac{2}{\sqrt[\phantom{2}]{n}}$ threshold suggested by Belsley et al$.(1980).\backslash$table$[[$A$,$B$,$C$,$D$,$E$],[$PIR$,$COL,INC,COST,INT$],[45,54,6841,8\mathrm{.}5,112],[30,62,4641,2\mathrm{.}4,82],[40,77,5441,14\mathrm{.}7,237],[75,87,2275,5\mathrm{.}9,46],[95,92,1795,10\mathrm{.}5,41],[15,65,14162,8\mathrm{.}2,154],[20,75,25455,6\mathrm{.}9,430],[60,52,493,222\mathrm{.}3,15],[92,86,1060,386\mathrm{.}2,37],[40,46,16675,34\mathrm{.}1,301],[15,24,21395,3\mathrm{.}9,352],[20,82,14936,4\mathrm{.}5,551],[20,74,4810,2,319],[70,70,3720,10,98],[95,86,520,45\mathrm{.}8,10],[75,84,2379,29\mathrm{.}7,93],[85,68,1208,103\mathrm{.}7,44],[40,62,7562,0,61],[40,80,27532,1\mathrm{.}9,504],[40,35,4201,61\mathrm{.}6,68],[25,49,18050,10\mathrm{.}7,156],[12,29,33664,7\mathrm{.}6,573],[20,32,46553,3\mathrm{.}9,351],[60,80,3000,1\mathrm{.}4,77],[45,63,2946,5\mathrm{.}4,72]]$