HR DATA ANALYSIS: WHO ARE LEAVING THE COMPANY AND WHY
30 December 2017
# Reading and Measuring the Data
mydata <- read.csv(paste("HR_comma_sep.csv", sep = ""))
View(mydata)
dim(mydata)## [1] 14999 10# Summary of the variables
library(psych)## Warning: package 'psych' was built under R version 3.4.3describe(mydata)## vars n mean sd median trimmed mad min
## satisfaction_level 1 14999 0.61 0.25 0.64 0.63 0.28 0.09
## last_evaluation 2 14999 0.72 0.17 0.72 0.72 0.22 0.36
## number_project 3 14999 3.80 1.23 4.00 3.74 1.48 2.00
## average_montly_hours 4 14999 201.05 49.94 200.00 200.64 65.23 96.00
## time_spend_company 5 14999 3.50 1.46 3.00 3.28 1.48 2.00
## Work_accident 6 14999 0.14 0.35 0.00 0.06 0.00 0.00
## left 7 14999 0.24 0.43 0.00 0.17 0.00 0.00
## promotion_last_5years 8 14999 0.02 0.14 0.00 0.00 0.00 0.00
## sales* 9 14999 6.94 2.75 8.00 7.23 2.97 1.00
## salary* 10 14999 2.35 0.63 2.00 2.41 1.48 1.00
## max range skew kurtosis se
## satisfaction_level 1 0.91 -0.48 -0.67 0.00
## last_evaluation 1 0.64 -0.03 -1.24 0.00
## number_project 7 5.00 0.34 -0.50 0.01
## average_montly_hours 310 214.00 0.05 -1.14 0.41
## time_spend_company 10 8.00 1.85 4.77 0.01
## Work_accident 1 1.00 2.02 2.08 0.00
## left 1 1.00 1.23 -0.49 0.00
## promotion_last_5years 1 1.00 6.64 42.03 0.00
## sales* 10 9.00 -0.79 -0.62 0.02
## salary* 3 2.00 -0.42 -0.67 0.01summary(mydata)## satisfaction_level last_evaluation number_project average_montly_hours
## Min. :0.0900 Min. :0.3600 Min. :2.000 Min. : 96.0
## 1st Qu.:0.4400 1st Qu.:0.5600 1st Qu.:3.000 1st Qu.:156.0
## Median :0.6400 Median :0.7200 Median :4.000 Median :200.0
## Mean :0.6128 Mean :0.7161 Mean :3.803 Mean :201.1
## 3rd Qu.:0.8200 3rd Qu.:0.8700 3rd Qu.:5.000 3rd Qu.:245.0
## Max. :1.0000 Max. :1.0000 Max. :7.000 Max. :310.0
##
## time_spend_company Work_accident left
## Min. : 2.000 Min. :0.0000 Min. :0.0000
## 1st Qu.: 3.000 1st Qu.:0.0000 1st Qu.:0.0000
## Median : 3.000 Median :0.0000 Median :0.0000
## Mean : 3.498 Mean :0.1446 Mean :0.2381
## 3rd Qu.: 4.000 3rd Qu.:0.0000 3rd Qu.:0.0000
## Max. :10.000 Max. :1.0000 Max. :1.0000
##
## promotion_last_5years sales salary
## Min. :0.00000 sales :4140 high :1237
## 1st Qu.:0.00000 technical :2720 low :7316
## Median :0.00000 support :2229 medium:6446
## Mean :0.02127 IT :1227
## 3rd Qu.:0.00000 product_mng: 902
## Max. :1.00000 marketing : 858
## (Other) :2923# One way contingency table
table(mydata$satisfaction_level)##
## 0.09 0.1 0.11 0.12 0.13 0.14 0.15 0.16 0.17 0.18 0.19 0.2 0.21 0.22 0.23
## 195 358 335 30 54 73 76 79 72 63 74 69 67 60 54
## 0.24 0.25 0.26 0.27 0.28 0.29 0.3 0.31 0.32 0.33 0.34 0.35 0.36 0.37 0.38
## 80 34 30 30 31 38 39 59 50 36 48 37 139 241 189
## 0.39 0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5 0.51 0.52 0.53
## 175 209 171 155 224 211 203 95 42 149 209 229 187 196 179
## 0.54 0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65 0.66 0.67 0.68
## 185 179 187 210 182 219 193 208 188 209 187 199 228 177 162
## 0.69 0.7 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8 0.81 0.82 0.83
## 209 205 171 230 246 257 226 234 252 241 217 222 220 241 234
## 0.84 0.85 0.86 0.87 0.88 0.89 0.9 0.91 0.92 0.93 0.94 0.95 0.96 0.97 0.98
## 247 207 200 225 187 237 220 224 198 169 167 181 203 176 183
## 0.99 1
## 172 111table(mydata$last_evaluation)##
## 0.36 0.37 0.38 0.39 0.4 0.41 0.42 0.43 0.44 0.45 0.46 0.47 0.48 0.49 0.5
## 22 55 50 52 57 59 56 50 44 115 211 173 292 332 353
## 0.51 0.52 0.53 0.54 0.55 0.56 0.57 0.58 0.59 0.6 0.61 0.62 0.63 0.64 0.65
## 345 309 324 350 358 322 333 225 255 221 234 233 236 235 201
## 0.66 0.67 0.68 0.69 0.7 0.71 0.72 0.73 0.74 0.75 0.76 0.77 0.78 0.79 0.8
## 222 245 222 193 213 196 211 223 260 238 216 263 214 241 251
## 0.81 0.82 0.83 0.84 0.85 0.86 0.87 0.88 0.89 0.9 0.91 0.92 0.93 0.94 0.95
## 255 237 269 294 316 273 326 235 296 313 287 269 269 263 258
## 0.96 0.97 0.98 0.99 1
## 249 276 263 258 283table(mydata$average_montly_hours)##
## 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113
## 6 14 23 11 19 16 17 17 28 17 19 10 18 18 12 26 10 29
## 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131
## 15 14 10 18 12 10 10 24 11 20 13 19 25 72 65 63 59 69
## 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149
## 100 87 114 153 104 122 88 120 129 115 112 127 102 134 110 118 123 148
## 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167
## 108 147 112 122 121 125 153 126 124 121 136 87 96 73 78 78 73 94
## 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185
## 92 86 76 83 70 96 78 76 81 81 85 73 88 78 75 84 80 93
## 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203
## 76 68 73 85 75 80 96 67 71 67 79 70 86 79 58 86 80 72
## 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221
## 68 73 83 71 72 72 72 79 72 71 78 68 76 87 79 85 64 81
## 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239
## 84 93 112 95 93 77 76 93 59 77 97 102 74 76 83 90 108 96
## 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257
## 93 85 98 112 98 124 102 108 86 93 100 98 86 101 113 115 87 126
## 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275
## 110 98 124 102 86 110 111 91 105 88 93 102 93 104 86 88 94 82
## 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293
## 30 21 35 32 29 34 36 25 24 33 50 30 6 19 15 17 15 13
## 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310
## 16 12 21 7 13 6 11 24 8 6 17 18 18 14 20 16 18table(mydata$number_project)##
## 2 3 4 5 6 7
## 2388 4055 4365 2761 1174 256table(mydata$time_spend_company)##
## 2 3 4 5 6 7 8 10
## 3244 6443 2557 1473 718 188 162 214table(mydata$left)##
## 0 1
## 11428 3571table(mydata$promotion_last_5years)##
## 0 1
## 14680 319Code 0 in table for left represents the person has not left the job while code 1 denotes he has left the job. Code 0 in table for promotion represents no promotion for the employee while code 1 denotes promotion for the employee.
# Two Way Contingency Tables
table(mydata$satisfaction_level, mydata$left)##
## 0 1
## 0.09 0 195
## 0.1 0 358
## 0.11 0 335
## 0.12 26 4
## 0.13 51 3
## 0.14 63 10
## 0.15 73 3
## 0.16 78 1
## 0.17 67 5
## 0.18 63 0
## 0.19 68 6
## 0.2 65 4
## 0.21 66 1
## 0.22 59 1
## 0.23 52 2
## 0.24 77 3
## 0.25 31 3
## 0.26 29 1
## 0.27 24 6
## 0.28 28 3
## 0.29 37 1
## 0.3 37 2
## 0.31 42 17
## 0.32 42 8
## 0.33 33 3
## 0.34 44 4
## 0.35 34 3
## 0.36 43 96
## 0.37 47 194
## 0.38 35 154
## 0.39 35 140
## 0.4 40 169
## 0.41 39 132
## 0.42 47 108
## 0.43 46 178
## 0.44 58 153
## 0.45 46 157
## 0.46 27 68
## 0.47 38 4
## 0.48 139 10
## 0.49 207 2
## 0.5 226 3
## 0.51 182 5
## 0.52 196 0
## 0.53 171 8
## 0.54 179 6
## 0.55 175 4
## 0.56 182 5
## 0.57 202 8
## 0.58 179 3
## 0.59 212 7
## 0.6 189 4
## 0.61 202 6
## 0.62 186 2
## 0.63 205 4
## 0.64 185 2
## 0.65 198 1
## 0.66 217 11
## 0.67 176 1
## 0.68 161 1
## 0.69 209 0
## 0.7 195 10
## 0.71 167 4
## 0.72 200 30
## 0.73 204 42
## 0.74 206 51
## 0.75 188 38
## 0.76 189 45
## 0.77 201 51
## 0.78 191 50
## 0.79 172 45
## 0.8 194 28
## 0.81 169 51
## 0.82 183 58
## 0.83 187 47
## 0.84 185 62
## 0.85 171 36
## 0.86 159 41
## 0.87 167 58
## 0.88 162 25
## 0.89 181 56
## 0.9 168 52
## 0.91 181 43
## 0.92 178 20
## 0.93 169 0
## 0.94 167 0
## 0.95 181 0
## 0.96 203 0
## 0.97 176 0
## 0.98 183 0
## 0.99 172 0
## 1 111 0As Job satisfaction level increases number of people leaving their jobs decreases.
table(mydata$last_evaluation, mydata$left)##
## 0 1
## 0.36 22 0
## 0.37 55 0
## 0.38 50 0
## 0.39 52 0
## 0.4 57 0
## 0.41 59 0
## 0.42 56 0
## 0.43 50 0
## 0.44 44 0
## 0.45 50 65
## 0.46 49 162
## 0.47 60 113
## 0.48 156 136
## 0.49 219 113
## 0.5 216 137
## 0.51 216 129
## 0.52 195 114
## 0.53 203 121
## 0.54 202 148
## 0.55 248 110
## 0.56 199 123
## 0.57 210 123
## 0.58 217 8
## 0.59 249 6
## 0.6 214 7
## 0.61 232 2
## 0.62 216 17
## 0.63 233 3
## 0.64 235 0
## 0.65 194 7
## 0.66 220 2
## 0.67 239 6
## 0.68 215 7
## 0.69 193 0
## 0.7 202 11
## 0.71 188 8
## 0.72 208 3
## 0.73 220 3
## 0.74 251 9
## 0.75 229 9
## 0.76 214 2
## 0.77 218 45
## 0.78 178 36
## 0.79 197 44
## 0.8 200 51
## 0.81 206 49
## 0.82 192 45
## 0.83 181 88
## 0.84 191 103
## 0.85 219 97
## 0.86 195 78
## 0.87 197 129
## 0.88 174 61
## 0.89 211 85
## 0.9 231 82
## 0.91 216 71
## 0.92 171 98
## 0.93 174 95
## 0.94 182 81
## 0.95 177 81
## 0.96 182 67
## 0.97 192 84
## 0.98 199 64
## 0.99 192 66
## 1 116 167Workers with higher evaluation scores have left their job which shows working/valuable employees are leaving their jobs.
table(mydata$average_montly_hours, mydata$left)##
## 0 1
## 96 6 0
## 97 14 0
## 98 23 0
## 99 11 0
## 100 19 0
## 101 16 0
## 102 17 0
## 103 17 0
## 104 28 0
## 105 17 0
## 106 19 0
## 107 10 0
## 108 18 0
## 109 18 0
## 110 12 0
## 111 26 0
## 112 10 0
## 113 29 0
## 114 15 0
## 115 14 0
## 116 10 0
## 117 18 0
## 118 12 0
## 119 10 0
## 120 10 0
## 121 24 0
## 122 11 0
## 123 20 0
## 124 13 0
## 125 19 0
## 126 15 10
## 127 15 57
## 128 18 47
## 129 16 47
## 130 17 42
## 131 23 46
## 132 52 48
## 133 65 22
## 134 76 38
## 135 78 75
## 136 56 48
## 137 78 44
## 138 52 36
## 139 86 34
## 140 82 47
## 141 72 43
## 142 76 36
## 143 74 53
## 144 67 35
## 145 80 54
## 146 68 42
## 147 68 50
## 148 72 51
## 149 94 54
## 150 81 27
## 151 89 58
## 152 74 38
## 153 78 44
## 154 75 46
## 155 71 54
## 156 91 62
## 157 82 44
## 158 74 50
## 159 75 46
## 160 90 46
## 161 68 19
## 162 94 2
## 163 72 1
## 164 70 8
## 165 77 1
## 166 72 1
## 167 94 0
## 168 90 2
## 169 85 1
## 170 76 0
## 171 83 0
## 172 67 3
## 173 96 0
## 174 78 0
## 175 74 2
## 176 81 0
## 177 78 3
## 178 84 1
## 179 71 2
## 180 84 4
## 181 74 4
## 182 72 3
## 183 81 3
## 184 78 2
## 185 93 0
## 186 76 0
## 187 68 0
## 188 72 1
## 189 83 2
## 190 75 0
## 191 80 0
## 192 90 6
## 193 67 0
## 194 71 0
## 195 64 3
## 196 78 1
## 197 70 0
## 198 82 4
## 199 76 3
## 200 58 0
## 201 86 0
## 202 78 2
## 203 71 1
## 204 67 1
## 205 71 2
## 206 77 6
## 207 71 0
## 208 72 0
## 209 72 0
## 210 72 0
## 211 76 3
## 212 70 2
## 213 69 2
## 214 73 5
## 215 68 0
## 216 71 5
## 217 75 12
## 218 65 14
## 219 63 22
## 220 59 5
## 221 64 17
## 222 76 8
## 223 75 18
## 224 102 10
## 225 82 13
## 226 74 19
## 227 64 13
## 228 56 20
## 229 74 19
## 230 56 3
## 231 69 8
## 232 69 28
## 233 88 14
## 234 61 13
## 235 61 15
## 236 63 20
## 237 75 15
## 238 90 18
## 239 72 24
## 240 76 17
## 241 75 10
## 242 75 23
## 243 83 29
## 244 63 35
## 245 77 47
## 246 76 26
## 247 75 33
## 248 63 23
## 249 65 28
## 250 75 25
## 251 73 25
## 252 69 17
## 253 70 31
## 254 66 47
## 255 69 46
## 256 63 24
## 257 91 35
## 258 80 30
## 259 69 29
## 260 89 35
## 261 79 23
## 262 56 30
## 263 76 34
## 264 76 35
## 265 71 20
## 266 81 24
## 267 57 31
## 268 71 22
## 269 72 30
## 270 70 23
## 271 79 25
## 272 70 16
## 273 71 17
## 274 71 23
## 275 44 38
## 276 12 18
## 277 8 13
## 278 13 22
## 279 15 17
## 280 20 9
## 281 16 18
## 282 11 25
## 283 12 13
## 284 12 12
## 285 17 16
## 286 28 22
## 287 13 17
## 288 0 6
## 289 0 19
## 290 0 15
## 291 0 17
## 292 0 15
## 293 0 13
## 294 0 16
## 295 0 12
## 296 0 21
## 297 0 7
## 298 0 13
## 299 0 6
## 300 0 11
## 301 0 24
## 302 0 8
## 303 0 6
## 304 0 17
## 305 0 18
## 306 0 18
## 307 0 14
## 308 0 20
## 309 0 16
## 310 0 18Employees who have spent larger amount of hours in the company have left it , which shows hard working employees of the company are leaving their jobs.
table(mydata$number_project, mydata$left)##
## 0 1
## 2 821 1567
## 3 3983 72
## 4 3956 409
## 5 2149 612
## 6 519 655
## 7 0 256Employees handling larger number of projects have left the company as well in large numbers.
table(mydata$time_spend_company, mydata$left)##
## 0 1
## 2 3191 53
## 3 4857 1586
## 4 1667 890
## 5 640 833
## 6 509 209
## 7 188 0
## 8 162 0
## 10 214 0Those who have worked with the company for a longer time have stayed with the company.
table(mydata$promotion_last_5years, mydata$left)##
## 0 1
## 0 11128 3552
## 1 300 19Employees who haven’t been promoted have left the company in large numbers.
# Effect of satisfaction level, evaluation scores, number of projects and average monthly hours on left and not left categories
aggregate(cbind(satisfaction_level, last_evaluation, number_project, average_montly_hours) ~ left,
data = mydata, mean)## left satisfaction_level last_evaluation number_project
## 1 0 0.6668096 0.7154734 3.786664
## 2 1 0.4400980 0.7181126 3.855503
## average_montly_hours
## 1 199.0602
## 2 207.4192My project studies that these employees who have left in such large numbers, most of them , on average are those who have a low level of satisfaction, who work for more hours and didn’t get promotion for last five years that is most of them are valuable employees who are leaving. So, I would show which of these factors pushes them the most to leave the company.
# Boxplots
Satisfaction Level
par(mar = rep(2, 4))
boxplot(satisfaction_level~left,data=mydata, main="Satisfaction level of those left",
xlab="Satisfaction Level", ylab="Left")
we can observe that those who have left had lower satisfaction level.
Last Evaluation
boxplot(last_evaluation~left,data=mydata, main="Evaluating Scores of those left",
xlab="Last Evaluation Score", ylab="Left")
We can observe that those who have left have higher scores in last evaluation that is they are mostly good performing employees.
Number Of Projects
boxplot(number_project~left,data=mydata, main="Number of Projects of those left",
xlab="Number of Projects", ylab="Left")
Those who have left had been engaged in higher number of projects.
Average Monthly Salary
boxplot(average_montly_hours~left,data=mydata, main="Average Monthly Hours Spent by those left",
xlab="Monthly Hours Spent", ylab="Left")
Those who have left have spent more monthly hours for the company.
# Histograms
Creating a new dataframe of those employees who have left the company so that we can observe how each variable in the data affects
For satisfaction
left <- mydata[ which(mydata$left=='1') , ]
View(left)
hist(left$satisfaction_level,col="#3090C7", main = "Satisfaction level")
For Last Evaluation
hist(left$last_evaluation,col="#3090C7", main = "Last evaluation")
For Average Monthly Hours
hist(left$average_montly_hours,col="#3090C7", main = "Average montly hours")
# Plotting
We define who are the valuable employees : those who have scored lin last evaluation more than 0.6, who have worked with the company for more than 4 years and those who have completed more than 4 projects : but they have still left
library(dplyr)## Warning: package 'dplyr' was built under R version 3.4.3##
## Attaching package: 'dplyr'## The following objects are masked from 'package:stats':
##
## filter, lag## The following objects are masked from 'package:base':
##
## intersect, setdiff, setequal, uniongoodpeopleleft <- left %>% filter(last_evaluation >= 0.60 | time_spend_company >= 4 | number_project > 4)## Warning: package 'bindrcpp' was built under R version 3.4.3View(goodpeopleleft)goodpeopleleft2 <- mydata %>% filter(last_evaluation >= 0.60 | time_spend_company >= 4 | number_project > 4)
goodpeople <- goodpeopleleft2 %>% select(satisfaction_level, number_project: promotion_last_5years)
M <- cor(goodpeople)
library(corrplot)## Warning: package 'corrplot' was built under R version 3.4.3## corrplot 0.84 loadedcorrplot(M, method="number")
We can infer that, on average valuable employees that leave are not satisfied, work on many projects, spend many hours in the company each month and aren’t promoted.
# Corrgram
library(corrgram) ## Warning: package 'corrgram' was built under R version 3.4.3corrgram(mydata, order=FALSE, lower.panel=panel.shade, upper.panel=panel.pie, diag.panel=panel.minmax, text.panel=panel.txt, main="Corrgram of HR intercorrelations")
# Scatterplot Matrix
library(car)## Warning: package 'car' was built under R version 3.4.3##
## Attaching package: 'car'## The following object is masked from 'package:dplyr':
##
## recode## The following object is masked from 'package:psych':
##
## logitscatterplotMatrix(left[,c("satisfaction_level","last_evaluation","number_project","average_montly_hours","time_spend_company")], spread=FALSE, smoother.args=list(lty=2), main="Scatter Plot Matrix")
# Chi Square Tests
For Satisfaction level : If employees with low satisfaction leave
leftforsatisfaction <- xtabs(~ left + satisfaction_level, data=mydata)
chisq.test(leftforsatisfaction)##
## Pearson's Chi-squared test
##
## data: leftforsatisfaction
## X-squared = 7937.7, df = 91, p-value < 2.2e-16Since p- value is so small we can conclude that the action of leaving the jobs depends on the satisfaction level.
For Evaluation scores : If Employees with higher evaluation scores leaving the jobs
leftbyperformers <- xtabs(~ left + last_evaluation, data=mydata)
chisq.test(leftbyperformers)##
## Pearson's Chi-squared test
##
## data: leftbyperformers
## X-squared = 2534.8, df = 64, p-value < 2.2e-16Since p- value is so small we can conclude that the employees leaving the industry are high performers.
For Number Of Projects: Employees who have handled higher number of projects are leaving the jobs
leftonprojects <- xtabs(~ left + number_project, data=mydata)
chisq.test(leftonprojects)##
## Pearson's Chi-squared test
##
## data: leftonprojects
## X-squared = 5373.6, df = 5, p-value < 2.2e-16Since p- value is so small we can conclude that the employees leaving the industry have higher number of projects.
For Average monthly hours: employees working for more hours have left
leftonhours <- xtabs(~ left + average_montly_hours, data=mydata)
chisq.test(leftonhours)## Warning in chisq.test(leftonhours): Chi-squared approximation may be
## incorrect##
## Pearson's Chi-squared test
##
## data: leftonhours
## X-squared = 3623.1, df = 214, p-value < 2.2e-16Since p- value is so small we can conclude that the employees leaving the industry are those who work for more hours
For Promotion: Employees who haven’t recieved promotion have left
lefton <- xtabs(~ left + promotion_last_5years , data=mydata)
chisq.test(lefton)##
## Pearson's Chi-squared test with Yates' continuity correction
##
## data: lefton
## X-squared = 56.262, df = 1, p-value = 6.344e-14Since p- value is so small we can conclude that the employees leaving the industry are those who haven’t recieved promotion.
# t test
t.test(leftbyperformers)##
## One Sample t-test
##
## data: leftbyperformers
## t = 15.66, df = 129, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 100.7997 129.9541
## sample estimates:
## mean of x
## 115.3769t.test(leftforsatisfaction)##
## One Sample t-test
##
## data: leftforsatisfaction
## t = 13.488, df = 183, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 69.59252 93.44009
## sample estimates:
## mean of x
## 81.5163t.test(lefton)##
## One Sample t-test
##
## data: lefton
## t = 1.4496, df = 3, p-value = 0.243
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -4482.527 11982.027
## sample estimates:
## mean of x
## 3749.75It implies with at 0.05 level of significance promotion and employees leaving or not leaving the company is independent as p- value is >0.05.
t.test(leftonhours)##
## One Sample t-test
##
## data: leftonhours
## t = 23.274, df = 429, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 31.93559 37.82721
## sample estimates:
## mean of x
## 34.8814t.test(leftonprojects)##
## One Sample t-test
##
## data: leftonprojects
## t = 3.0739, df = 11, p-value = 0.01059
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 354.9412 2144.8921
## sample estimates:
## mean of x
## 1249.917