library(readr)
## Warning: replacing previous import 'lifecycle::last_warnings' by
## 'rlang::last_warnings' when loading 'hms'
## Warning: replacing previous import 'lifecycle::last_warnings' by
## 'rlang::last_warnings' when loading 'pillar'
data <- read_csv("D:/USB DRive/Project DSP Laptop/KSF.csv")
##
## -- Column specification --------------------------------------------------------
## cols(
## `Key Success Factor` = col_character(),
## `TM One` = col_double(),
## Maxis = col_double(),
## Celcom = col_double(),
## AWS = col_double(),
## Google = col_double(),
## Microsoft = col_double(),
## AwanTec = col_double(),
## Time = col_double(),
## Enfrasys = col_double()
## )
inno <- c( 4,4,4,2,2,3,2,2,1 )
advert <- c(2,3,4,2,2,2,1,1,1)
brand_name <- c(3,3,4,3,3,2,1,1,1)
product <- c(2,1,4,3,3,1,1,1,1)
connect <- c(2,3,4,2,1,2,1,1,1)
rlimpois <- function(n, lambda, lowlimit, toplimit){
sample(x=lowlimit:toplimit, size=n,
prob=dpois(lowlimit:toplimit, lambda), replace=TRUE)
}
set.seed(1234)
inno1 <- rlimpois (100,3,1,4)
inno1
## [1] 3 4 4 4 1 4 3 3 4 2 4 2 3 1 3 1 3 3 3 3 2 2 3 3 3 1 2 1 1 3 2 3 2 2 3 4 3
## [38] 3 1 1 2 4 2 4 2 2 4 2 3 4 3 2 4 2 3 2 2 4 3 1 1 3 2 3 3 4 2 2 3 2 3 1 3 4
## [75] 3 2 2 3 2 4 1 2 3 2 3 1 2 2 3 1 3 1 3 3 3 2 2 3 2 4
set.seed(889)
advert1 <- rlimpois (100,3,1,4)
advert1
## [1] 3 1 2 4 1 4 3 4 1 3 1 2 1 3 3 1 3 2 4 2 1 4 2 2 3 1 4 2 2 4 4 2 4 4 3 2 1
## [38] 4 4 2 1 2 2 3 1 3 4 2 3 4 1 3 2 1 3 4 3 4 2 3 3 2 2 3 4 4 4 2 3 1 3 4 2 4
## [75] 3 3 4 3 1 4 3 4 2 2 2 2 1 4 1 1 3 2 3 2 1 3 1 3 2 1
set.seed(664)
brand1 <- rlimpois (100,3,1,4)
brand1
## [1] 4 2 3 4 2 2 1 4 4 3 4 3 2 2 1 3 3 2 4 2 4 4 4 3 1 1 2 4 2 1 4 3 2 4 2 1 2
## [38] 4 4 2 4 3 3 3 3 4 2 3 2 4 3 4 3 3 4 1 3 3 3 4 1 4 1 1 1 4 3 1 2 1 4 2 4 2
## [75] 2 2 2 3 1 2 1 1 2 3 4 1 3 4 1 1 4 3 4 3 4 1 1 3 3 3
set.seed(775)
product1 <- rlimpois (100,3,1,4)
product1
## [1] 2 2 4 3 3 2 4 3 3 2 1 1 3 4 3 1 3 2 2 3 3 3 1 1 2 3 2 1 2 3 1 4 4 4 3 2 3
## [38] 1 2 2 4 3 1 3 2 1 4 3 3 4 4 3 3 1 4 3 4 3 2 3 1 3 4 4 1 2 3 3 4 1 3 3 2 3
## [75] 2 2 2 3 2 1 1 1 1 2 3 3 2 2 3 3 1 1 4 2 3 3 4 4 3 3
set.seed(445)
connect1 <- rlimpois (100,3,1,4)
connect1
## [1] 2 1 3 3 2 2 3 2 3 2 3 3 2 2 3 2 2 2 3 3 3 4 2 4 4 1 2 2 3 1 4 4 2 2 3 2 3
## [38] 2 1 4 1 1 2 3 2 3 2 3 3 4 2 3 3 1 3 3 3 1 1 4 4 2 3 4 1 2 1 4 3 1 4 2 3 1
## [75] 3 2 3 4 4 4 3 2 1 2 3 2 3 2 1 3 4 3 2 1 1 2 4 2 2 2
set.seed(139)
perform <- rnorm(100, mean = 1.5, sd = 1.0)
perform
## [1] 1.562974634 1.449306611 0.015958490 0.106977739 1.674036908
## [6] 2.619345253 0.481378393 1.990018159 1.666834728 1.501674807
## [11] 2.616479662 1.513863102 -1.010425161 1.410219061 1.239917216
## [16] 3.348547350 3.215448574 2.551628585 2.005688051 -0.413743942
## [21] -0.098899929 2.415166729 1.230079838 0.752894467 0.387533782
## [26] 2.298282678 3.515924636 0.369994983 1.095983211 0.547650129
## [31] 2.714364782 1.535289299 -0.304877527 1.924302686 1.672798378
## [36] 1.744663351 1.541646726 1.043658676 1.633919309 1.897634077
## [41] 2.661331740 1.766275205 0.373081700 3.005034020 1.574653009
## [46] -0.006153962 -0.040883103 0.640731090 1.646945547 -0.905255555
## [51] 0.492904441 2.044625479 0.665545155 2.714636887 0.077440257
## [56] 0.070208801 1.317224311 0.934192502 1.602460713 1.928426774
## [61] 0.776798555 0.968327257 0.913413225 0.933770514 1.722033192
## [66] 0.122381956 1.418906040 0.988572281 1.301389710 -0.474391281
## [71] 1.402394549 1.365783136 2.049052591 2.304222557 4.080246113
## [76] 0.434261553 0.823211545 1.481528824 0.937571487 2.612146320
## [81] 1.223946546 2.562873196 1.139381915 3.298898361 1.041060221
## [86] 0.796722148 1.593426905 2.221923228 0.120813166 1.484350720
## [91] 0.857624102 0.405026999 1.087632019 3.110096646 2.037523302
## [96] 1.461000444 0.282572425 -0.782105515 1.056146233 2.672739044
glm1 <- glm( perform ~ inno1 + advert1 + brand1 + product1 + connect1, data = data, family = "gaussian")
summary(glm1)
##
## Call:
## glm(formula = perform ~ inno1 + advert1 + brand1 + product1 +
## connect1, family = "gaussian", data = data)
##
## Deviance Residuals:
## Min 1Q Median 3Q Max
## -2.18789 -0.63989 0.00362 0.55121 2.74303
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.98434 0.51930 3.821 0.000238 ***
## inno1 -0.02556 0.11119 -0.230 0.818698
## advert1 0.02827 0.09433 0.300 0.765076
## brand1 0.09715 0.09264 1.049 0.297021
## product1 -0.23184 0.10224 -2.268 0.025649 *
## connect1 -0.12863 0.10516 -1.223 0.224332
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## (Dispersion parameter for gaussian family taken to be 1.014542)
##
## Null deviance: 104.129 on 99 degrees of freedom
## Residual deviance: 95.367 on 94 degrees of freedom
## AIC: 293.04
##
## Number of Fisher Scoring iterations: 2