Primary Variables for salary variation
GMAT Score, Gender, Age, WorkExperience
MBA Starting Salaries
Poornachandra Rao
January 5, 2018
The RMD file contains the Data Analysis of “MBA Starting Salaries” case study.
Possible questions before joining the Particular B-School
Whether to enroll in the MBA program at this particular school? About starting salaries, whether gender and/or age made a difference? Whether students liked this particular program?? Whether her GMAT score made a difference in marks?
More chances of Enrollment
Better Salary, More Satisfaction Rating
Reading the Data in to R
MBA_Data <- read.csv(paste("MBA Starting Salaries Data.csv",sep=""))
dim(MBA_Data)## [1] 274 13View(MBA_Data)Creating summary statistics (e.g. mean, standard deviation, median, mode) for the important variables in the dataset.
library(psych)
describe(MBA_Data)## vars n mean sd median trimmed mad min max
## age 1 274 27.36 3.71 27 26.76 2.97 22 48
## sex 2 274 1.25 0.43 1 1.19 0.00 1 2
## gmat_tot 3 274 619.45 57.54 620 618.86 59.30 450 790
## gmat_qpc 4 274 80.64 14.87 83 82.31 14.83 28 99
## gmat_vpc 5 274 78.32 16.86 81 80.33 14.83 16 99
## gmat_tpc 6 274 84.20 14.02 87 86.12 11.86 0 99
## s_avg 7 274 3.03 0.38 3 3.03 0.44 2 4
## f_avg 8 274 3.06 0.53 3 3.09 0.37 0 4
## quarter 9 274 2.48 1.11 2 2.47 1.48 1 4
## work_yrs 10 274 3.87 3.23 3 3.29 1.48 0 22
## frstlang 11 274 1.12 0.32 1 1.02 0.00 1 2
## salary 12 274 39025.69 50951.56 999 33607.86 1481.12 0 220000
## satis 13 274 172.18 371.61 6 91.50 1.48 1 998
## range skew kurtosis se
## age 26 2.16 6.45 0.22
## sex 1 1.16 -0.66 0.03
## gmat_tot 340 -0.01 0.06 3.48
## gmat_qpc 71 -0.92 0.30 0.90
## gmat_vpc 83 -1.04 0.74 1.02
## gmat_tpc 99 -2.28 9.02 0.85
## s_avg 2 -0.06 -0.38 0.02
## f_avg 4 -2.08 10.85 0.03
## quarter 3 0.02 -1.35 0.07
## work_yrs 22 2.78 9.80 0.20
## frstlang 1 2.37 3.65 0.02
## salary 220000 0.70 -1.05 3078.10
## satis 997 1.77 1.13 22.45Structure of Data
str(MBA_Data)## 'data.frame': 274 obs. of 13 variables:
## $ age : int 23 24 24 24 24 24 25 25 25 25 ...
## $ sex : int 2 1 1 1 2 1 1 2 1 1 ...
## $ gmat_tot: int 620 610 670 570 710 640 610 650 630 680 ...
## $ gmat_qpc: int 77 90 99 56 93 82 89 88 79 99 ...
## $ gmat_vpc: int 87 71 78 81 98 89 74 89 91 81 ...
## $ gmat_tpc: int 87 87 95 75 98 91 87 92 89 96 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.6 3.9 3.4 3.3 3.3 3.45 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.75 3.5 3.75 3.25 3.67 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 2 2 2 ...
## $ frstlang: int 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 999 0 0 0 999 998 ...
## $ satis : int 7 6 6 7 5 6 5 6 4 998 ...Converting the data type of some columns
Sex
MBA_Data$sex[MBA_Data$sex==1] <- "Male"
MBA_Data$sex[MBA_Data$sex==2] <- "Female"
MBA_Data$sex= factor(MBA_Data$sex)First Language
MBA_Data$frstlang[MBA_Data$frstlang==1] <- "English"
MBA_Data$frstlang[MBA_Data$frstlang==2] <- "Other"
MBA_Data$frstlang= factor(MBA_Data$frstlang)str(MBA_Data)## 'data.frame': 274 obs. of 13 variables:
## $ age : int 23 24 24 24 24 24 25 25 25 25 ...
## $ sex : Factor w/ 2 levels "Female","Male": 1 2 2 2 1 2 2 1 2 2 ...
## $ gmat_tot: int 620 610 670 570 710 640 610 650 630 680 ...
## $ gmat_qpc: int 77 90 99 56 93 82 89 88 79 99 ...
## $ gmat_vpc: int 87 71 78 81 98 89 74 89 91 81 ...
## $ gmat_tpc: int 87 87 95 75 98 91 87 92 89 96 ...
## $ s_avg : num 3.4 3.5 3.3 3.3 3.6 3.9 3.4 3.3 3.3 3.45 ...
## $ f_avg : num 3 4 3.25 2.67 3.75 3.75 3.5 3.75 3.25 3.67 ...
## $ quarter : int 1 1 1 1 1 1 1 1 1 1 ...
## $ work_yrs: int 2 2 2 1 2 2 2 2 2 2 ...
## $ frstlang: Factor w/ 2 levels "English","Other": 1 1 1 1 1 1 1 1 2 1 ...
## $ salary : int 0 0 0 0 999 0 0 0 999 998 ...
## $ satis : int 7 6 6 7 5 6 5 6 4 998 ...998 = did not answer the survey
999 = answered the survey but did not disclose salary data
0: Not Yet Placed
Salary Value = Who are Placed
DataFrame-1
NotAnsweredSurvey <- MBA_Data[which(MBA_Data$salary=='998'),]
NotAnsweredSurvey## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 10 25 Male 680 99 81 96 3.45 3.67 1
## 11 26 Male 740 99 98 99 3.56 4.00 1
## 12 26 Female 610 75 87 86 3.40 3.75 1
## 13 26 Male 710 95 95 98 3.50 3.50 1
## 14 26 Male 720 97 97 99 3.40 4.00 1
## 15 26 Female 660 84 93 94 3.30 3.25 1
## 16 26 Female 640 67 98 92 4.00 4.00 1
## 17 27 Female 660 71 99 95 3.50 4.00 1
## 18 27 Male 600 77 78 84 3.30 3.50 1
## 19 27 Male 630 79 89 89 3.50 4.00 1
## 20 27 Male 600 91 58 83 3.40 3.25 1
## 79 25 Female 670 99 85 96 3.00 3.25 2
## 80 25 Male 690 94 95 98 3.00 2.75 2
## 81 25 Female 630 83 89 90 3.00 2.75 2
## 82 25 Male 670 99 74 96 3.18 3.25 2
## 83 25 Male 680 91 95 97 3.00 3.00 2
## 84 25 Male 690 96 89 97 3.00 3.00 2
## 85 25 Male 670 97 81 95 3.10 3.25 2
## 86 25 Male 580 79 71 78 3.10 2.33 2
## 94 27 Male 630 87 84 89 3.20 3.00 2
## 95 27 Male 560 60 71 72 3.20 3.00 2
## 96 28 Female 450 49 22 44 3.10 3.75 2
## 148 25 Male 600 89 62 83 2.70 3.25 3
## 149 25 Male 630 79 91 89 2.70 2.75 3
## 153 25 Male 630 93 71 89 2.90 3.25 3
## 154 25 Male 560 79 58 72 2.73 3.17 3
## 155 26 Male 670 97 81 96 2.70 2.50 3
## 156 26 Male 660 88 93 94 2.90 2.75 3
## 157 26 Male 630 83 87 90 2.70 3.00 3
## 171 27 Male 600 68 87 83 2.90 3.25 3
## 172 27 Male 650 79 95 93 2.70 3.25 3
## 173 27 Male 560 52 81 72 2.70 2.75 3
## 174 27 Male 610 48 98 86 2.70 3.00 3
## 175 27 Male 600 77 81 84 2.70 3.00 3
## 176 28 Male 460 66 16 37 2.70 2.50 3
## 177 28 Male 650 99 63 93 2.90 3.00 3
## 178 28 Female 610 64 93 86 2.80 3.25 3
## 210 24 Male 610 82 81 86 2.50 2.75 4
## 211 24 Male 640 93 78 91 2.40 2.50 4
## 215 25 Male 640 79 93 91 2.67 0.00 4
## 216 25 Male 590 68 81 81 2.60 2.75 4
## 224 26 Male 590 89 58 81 2.50 2.25 4
## 225 26 Female 670 98 81 95 2.60 2.50 4
## 247 30 Male 630 82 87 89 3.80 3.50 4
## 248 31 Male 580 83 67 79 3.00 3.25 4
## 249 31 Male 740 99 98 99 2.20 3.00 4
## work_yrs frstlang salary satis
## 10 2 English 998 998
## 11 2 English 998 998
## 12 2 English 998 998
## 13 3 English 998 998
## 14 2 English 998 998
## 15 4 English 998 998
## 16 2 English 998 998
## 17 4 English 998 998
## 18 3 Other 998 998
## 19 2 English 998 998
## 20 4 English 998 998
## 79 2 Other 998 998
## 80 2 English 998 998
## 81 3 English 998 998
## 82 2 Other 998 998
## 83 2 English 998 998
## 84 3 English 998 998
## 85 2 English 998 998
## 86 2 English 998 998
## 94 4 English 998 998
## 95 4 English 998 998
## 96 4 Other 998 998
## 148 4 English 998 998
## 149 2 English 998 998
## 153 3 Other 998 998
## 154 2 Other 998 998
## 155 4 English 998 998
## 156 3 Other 998 998
## 157 3 English 998 998
## 171 3 English 998 998
## 172 3 English 998 998
## 173 2 English 998 998
## 174 4 English 998 998
## 175 3 English 998 998
## 176 4 English 998 998
## 177 4 Other 998 998
## 178 4 English 998 998
## 210 2 English 998 998
## 211 1 English 998 998
## 215 1 English 998 998
## 216 3 English 998 998
## 224 3 English 998 998
## 225 3 English 998 998
## 247 7 English 998 998
## 248 6 English 998 998
## 249 8 English 998 998DataFrame-2
NotdisclosedSalary <- MBA_Data[which(MBA_Data$salary=='999'),]
NotdisclosedSalary## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 5 24 Female 710 93 98 98 3.60 3.75 1
## 9 25 Male 630 79 91 89 3.30 3.25 1
## 21 27 Female 570 65 82 77 3.30 3.25 1
## 26 30 Male 620 82 84 87 3.40 2.80 1
## 30 32 Male 570 71 71 0 3.50 3.50 1
## 78 25 Male 690 87 98 98 3.00 3.00 2
## 87 26 Male 680 92 93 97 3.00 3.00 2
## 91 27 Male 740 99 98 99 3.10 3.50 2
## 99 28 Male 660 95 85 96 3.10 3.25 2
## 101 29 Male 580 91 50 80 3.10 2.67 2
## 105 29 Male 590 68 84 81 3.10 3.00 2
## 108 31 Male 670 83 98 96 3.20 3.40 2
## 145 24 Male 650 89 84 93 2.70 3.25 3
## 152 25 Male 660 95 84 94 2.70 3.00 3
## 158 26 Male 640 87 84 91 2.70 3.20 3
## 161 26 Male 600 97 45 83 2.70 3.00 3
## 166 27 Male 730 95 99 99 2.90 3.33 3
## 170 27 Female 620 97 54 87 2.70 2.75 3
## 179 28 Male 500 46 54 52 2.90 2.75 3
## 181 29 Male 560 57 74 73 2.80 3.00 3
## 212 25 Male 600 53 95 84 2.50 3.00 4
## 214 25 Female 650 87 91 93 2.50 2.50 4
## 217 25 Male 590 97 41 81 2.50 2.75 4
## 221 26 Male 560 87 45 72 2.60 3.00 4
## 223 26 Male 570 82 58 75 2.50 2.75 4
## 226 27 Male 660 97 81 94 2.50 2.50 4
## 228 27 Male 790 99 99 99 2.40 2.50 4
## 231 27 Male 620 85 85 89 3.30 3.00 4
## 235 28 Male 620 93 71 87 2.40 2.75 4
## 239 29 Male 690 99 87 97 2.30 2.25 4
## 240 29 Male 630 87 84 89 2.90 2.80 4
## 245 30 Male 550 79 45 69 2.45 2.75 4
## 246 30 Female 600 99 46 86 2.80 3.00 4
## 251 31 Male 640 79 92 92 2.70 2.75 4
## 252 32 Male 570 89 41 75 2.60 2.50 4
## work_yrs frstlang salary satis
## 5 2 English 999 5
## 9 2 Other 999 4
## 21 4 English 999 4
## 26 5 English 999 6
## 30 4 English 999 4
## 78 3 English 999 5
## 87 3 English 999 1
## 91 2 English 999 4
## 99 4 English 999 3
## 101 4 Other 999 4
## 105 6 English 999 5
## 108 4 English 999 6
## 145 1 English 999 5
## 152 3 English 999 6
## 158 4 English 999 5
## 161 4 Other 999 6
## 166 0 English 999 5
## 170 2 Other 999 2
## 179 9 English 999 6
## 181 4 English 999 5
## 212 2 English 999 4
## 214 3 English 999 7
## 217 2 Other 999 4
## 221 3 Other 999 3
## 223 3 English 999 6
## 226 4 English 999 4
## 228 4 English 999 6
## 231 1 English 999 5
## 235 3 English 999 4
## 239 7 English 999 5
## 240 3 English 999 4
## 245 5 Other 999 4
## 246 6 Other 999 4
## 251 7 English 999 3
## 252 4 Other 999 3DataFrame-3
NotPlaced <- MBA_Data[which(MBA_Data$salary=='0'),]
NotPlaced## age sex gmat_tot gmat_qpc gmat_vpc gmat_tpc s_avg f_avg quarter
## 1 23 Female 620 77 87 87 3.40 3.00 1
## 2 24 Male 610 90 71 87 3.50 4.00 1
## 3 24 Male 670 99 78 95 3.30 3.25 1
## 4 24 Male 570 56 81 75 3.30 2.67 1
## 6 24 Male 640 82 89 91 3.90 3.75 1
## 7 25 Male 610 89 74 87 3.40 3.50 1
## 8 25 Female 650 88 89 92 3.30 3.75 1
## 22 27 Male 740 99 96 99 3.50 3.50 1
## 23 27 Male 750 99 98 99 3.40 3.50 1
## 24 28 Female 540 75 50 65 3.60 4.00 1
## 25 29 Male 580 56 87 78 3.64 3.33 1
## 27 31 Female 560 60 78 72 3.30 3.75 1
## 28 32 Male 760 99 99 99 3.40 3.00 1
## 29 32 Male 640 79 91 91 3.60 3.75 1
## 31 34 Female 620 75 89 87 3.30 3.00 1
## 32 37 Female 560 43 87 72 3.40 3.50 1
## 33 42 Female 650 75 98 93 3.38 3.00 1
## 34 48 Male 590 84 62 81 3.80 4.00 1
## 70 22 Male 600 95 54 83 3.00 3.00 2
## 71 23 Male 640 89 87 92 3.00 3.00 2
## 72 24 Male 550 73 63 69 3.10 3.00 2
## 73 24 Male 570 82 58 75 3.09 3.50 2
## 74 24 Male 620 82 84 87 3.10 3.50 2
## 75 25 Female 570 61 81 76 3.00 3.25 2
## 76 25 Male 660 94 84 94 3.27 3.75 2
## 77 25 Male 680 94 92 97 3.17 3.50 2
## 88 26 Female 560 64 71 72 3.20 3.25 2
## 89 26 Male 560 87 41 72 3.00 3.00 2
## 90 26 Male 530 68 54 62 3.09 3.17 2
## 92 27 Male 720 99 95 99 3.10 3.25 2
## 93 27 Male 590 60 87 81 3.00 2.75 2
## 97 28 Male 620 81 90 89 3.20 3.00 2
## 98 28 Female 610 85 78 86 3.10 3.00 2
## 100 29 Male 660 94 87 94 3.00 3.00 2
## 102 29 Male 510 57 50 55 3.27 3.40 2
## 103 29 Female 640 90 84 92 3.20 3.00 2
## 104 29 Male 610 91 62 86 3.10 3.67 2
## 106 29 Male 580 79 67 78 3.00 3.25 2
## 107 30 Male 680 97 87 96 3.00 3.00 2
## 109 32 Female 610 64 89 86 3.25 0.00 2
## 110 35 Male 540 43 78 65 3.20 3.25 2
## 111 35 Male 630 66 95 90 3.08 3.25 2
## 112 36 Female 530 48 71 62 3.00 2.50 2
## 113 36 Male 650 87 89 93 3.00 3.20 2
## 114 43 Male 630 82 87 89 3.10 3.00 2
## 140 23 Male 720 95 98 99 2.80 2.50 3
## 141 24 Female 640 94 78 92 2.90 3.25 3
## 142 24 Male 710 96 97 99 2.80 2.75 3
## 143 24 Male 670 94 89 96 2.70 3.00 3
## 144 24 Female 710 97 97 99 2.80 3.00 3
## 146 24 Male 600 89 62 83 2.90 3.00 3
## 147 24 Female 640 96 71 91 2.70 2.50 3
## 150 25 Male 550 72 58 69 2.90 3.00 3
## 151 25 Male 710 99 91 98 2.90 3.25 3
## 159 26 Male 560 56 81 72 2.80 3.25 3
## 160 26 Male 540 52 71 65 2.70 2.75 3
## 162 26 Female 570 48 89 75 2.82 2.50 3
## 163 26 Male 610 82 81 86 2.90 2.75 3
## 164 27 Male 650 89 84 93 2.90 3.00 3
## 165 27 Female 550 66 63 69 2.90 3.00 3
## 167 27 Male 610 97 45 86 2.70 2.50 3
## 168 27 Female 630 82 89 89 2.70 3.25 3
## 169 27 Female 560 61 74 73 2.80 3.25 3
## 180 29 Male 590 92 58 81 2.80 2.75 3
## 182 32 Male 550 52 78 71 2.70 2.75 3
## 183 34 Male 610 79 81 86 2.80 3.00 3
## 184 34 Male 610 82 78 86 2.70 3.00 3
## 185 43 Male 480 49 41 45 2.90 3.25 3
## 213 25 Male 730 98 96 99 2.40 2.75 4
## 218 25 Male 700 99 87 98 2.00 2.00 4
## 219 26 Male 660 93 87 95 2.60 2.00 4
## 220 26 Male 450 28 46 34 2.10 2.00 4
## 222 26 Male 600 75 78 83 2.20 2.25 4
## 227 27 Female 560 59 74 73 2.40 2.50 4
## 229 27 Male 630 93 78 91 2.10 2.50 4
## 230 27 Male 580 84 58 78 2.70 2.75 4
## 232 27 Male 670 89 91 95 3.60 3.25 4
## 233 27 Male 580 74 70 78 3.40 3.25 4
## 234 28 Male 560 74 67 73 3.60 3.60 4
## 236 28 Male 710 94 98 99 3.40 3.75 4
## 237 28 Male 570 69 71 0 2.30 2.50 4
## 238 29 Male 530 35 81 62 3.30 2.75 4
## 241 29 Male 670 91 91 95 3.30 3.25 4
## 242 29 Male 630 99 50 89 2.90 3.25 4
## 243 29 Female 680 89 96 96 2.80 3.00 4
## 244 30 Male 650 88 92 93 3.45 3.83 4
## 250 31 Male 570 75 62 75 2.80 3.00 4
## 253 32 Male 510 79 22 54 2.30 2.25 4
## 254 35 Male 570 72 71 75 3.30 4.00 4
## 255 39 Female 700 89 98 98 3.30 3.25 4
## work_yrs frstlang salary satis
## 1 2 English 0 7
## 2 2 English 0 6
## 3 2 English 0 6
## 4 1 English 0 7
## 6 2 English 0 6
## 7 2 English 0 5
## 8 2 English 0 6
## 22 3 English 0 6
## 23 1 Other 0 5
## 24 5 English 0 5
## 25 3 English 0 5
## 27 10 English 0 7
## 28 5 English 0 5
## 29 7 English 0 6
## 31 7 English 0 6
## 32 9 English 0 6
## 33 13 English 0 5
## 34 22 English 0 6
## 70 1 English 0 5
## 71 2 English 0 7
## 72 0 Other 0 5
## 73 2 English 0 6
## 74 1 English 0 5
## 75 3 English 0 4
## 76 2 English 0 5
## 77 2 English 0 6
## 88 3 English 0 6
## 89 3 English 0 6
## 90 4 Other 0 5
## 92 5 English 0 5
## 93 3 English 0 6
## 97 4 English 0 6
## 98 5 English 0 6
## 100 1 English 0 6
## 102 5 English 0 5
## 103 3 English 0 5
## 104 7 English 0 5
## 106 4 English 0 6
## 107 4 English 0 5
## 109 11 English 0 7
## 110 8 English 0 5
## 111 12 English 0 5
## 112 7 English 0 5
## 113 18 English 0 6
## 114 16 English 0 5
## 140 1 English 0 5
## 141 2 Other 0 4
## 142 2 English 0 7
## 143 2 English 0 7
## 144 2 English 0 7
## 146 1 English 0 6
## 147 2 English 0 6
## 150 3 English 0 6
## 151 1 English 0 6
## 159 4 English 0 6
## 160 2 English 0 6
## 162 3 English 0 5
## 163 3 English 0 6
## 164 2 English 0 6
## 165 3 English 0 4
## 167 4 Other 0 5
## 168 5 English 0 6
## 169 5 English 0 6
## 180 3 Other 0 5
## 182 7 English 0 6
## 183 11 English 0 6
## 184 12 English 0 5
## 185 22 English 0 5
## 213 2 English 0 6
## 218 1 English 0 7
## 219 2 English 0 5
## 220 4 English 0 6
## 222 2 English 0 6
## 227 2 English 0 5
## 229 4 English 0 5
## 230 1 English 0 5
## 232 5 English 0 6
## 233 3 English 0 6
## 234 5 English 0 5
## 236 6 English 0 6
## 237 5 English 0 5
## 238 6 English 0 7
## 241 3 English 0 5
## 242 1 Other 0 4
## 243 4 English 0 5
## 244 2 English 0 6
## 250 1 English 0 6
## 253 5 Other 0 5
## 254 8 English 0 6
## 255 5 English 0 5DataFrame-4
Placed <- MBA_Data[which(MBA_Data$salary>999),]
dim(Placed)## [1] 103 13Two-way contingency Table of Sex & FirstLanguage
sex_Flang <- xtabs(~MBA_Data$sex+MBA_Data$frstlang)
addmargins(sex_Flang)## MBA_Data$frstlang
## MBA_Data$sex English Other Sum
## Female 60 8 68
## Male 182 24 206
## Sum 242 32 274One-way contingency Table of Satisfaction value
satisfaction <- xtabs(~MBA_Data$satis)
addmargins(satisfaction)## MBA_Data$satis
## 1 2 3 4 5 6 7 998 Sum
## 1 1 5 17 74 97 33 46 274prop.table(satisfaction)*100## MBA_Data$satis
## 1 2 3 4 5 6
## 0.3649635 0.3649635 1.8248175 6.2043796 27.0072993 35.4014599
## 7 998
## 12.0437956 16.7883212We can clearly see that morethan 74% of students have mentioned a satisfaction value of more than four. (i.e,>4).
Mean of Placed MBA Students as per Gender.
PlacedSalaryMean <- aggregate(Placed$salary,list(Gender=Placed$sex),mean)
PlacedSalaryMean## Gender x
## 1 Female 98524.39
## 2 Male 104970.97Placed Students as per Age.
table(Placed$age)##
## 22 23 24 25 26 27 28 29 30 31 32 33 34 39 40
## 1 5 16 23 14 14 8 6 6 4 1 1 1 1 2Mean of Placed MBA Students as per Age.
AgeSalaryMean <- aggregate(Placed$salary,list(Gender=Placed$age),mean)
AgeSalaryMean## Gender x
## 1 22 85000.00
## 2 23 91651.20
## 3 24 101518.75
## 4 25 99086.96
## 5 26 101665.00
## 6 27 102214.29
## 7 28 103625.00
## 8 29 102083.33
## 9 30 109916.67
## 10 31 100500.00
## 11 32 107300.00
## 12 33 118000.00
## 13 34 105000.00
## 14 39 112000.00
## 15 40 183000.00Drawing Box Plots / Bar Plots to visualize the distribution of each variable independently
Histograms
Using ‘lattice’ to have a clear idea of percent value of distribution based on “GMAT Score”, “Sex”,“Age”, “WorkExperience”
library(lattice)
histogram(Placed$gmat_tot,main="Histogram of Total GMAT Score", xlab = "GMAT Total",ylab = "Percent",col= "Yellow",las=1)
library(lattice)
histogram(Placed$sex, main="Histogram of Total GMAT Score", xlab = "GMAT Total",ylab = "Percent",col= "Yellow",las=1)
library(lattice)
histogram(Placed$age, main="Histogram of Total GMAT Score", xlab = "GMAT Total",ylab = "Percent",col= "Yellow",las=1)
library(lattice)
histogram(Placed$work_yrs,main="Histogram of Total GMAT Score", xlab = "GMAT Total",ylab = "Percent",col= "Yellow",las=1)
BoXplots
par(mfrow=c(1,2))
boxplot(Placed$salary~Placed$gmat_tot,col=c("red","blue","yellow","orange","green"),horizontal=TRUE,main="GMAT Score Based Salary Distribution",xlab="Salary of MBA Students",ylab="GMAT Score" )
boxplot(Placed$salary~Placed$work_yrs,horizontal=TRUE,col=c("red","blue","yellow","orange","green"),main="Work Exp. Based Salary Distribution",xlab="Salary of MBA Students",ylab="Work Exp.")
boxplot(Placed$salary~Placed$age,horizontal=TRUE,col=c("red","blue","yellow","orange","green"),main="Age wise Salary Distribution",xlab="Salary of MBA Students",ylab="Age")
par(mfrow=c(3,1))
boxplot(Placed$salary~Placed$sex,horizontal=TRUE,col=c("Yellow"),main="Gender Based Salary Distribution",xlab="Salary of MBA Students",ylab="Sex")
boxplot(Placed$salary~Placed$quarter,horizontal=TRUE,col=c("red","blue","yellow","orange"),las=1,main="Quarter Ranking Based Salary Distribution")
boxplot(Placed$salary~Placed$frstlang,horizontal=TRUE,las=1,col=c("Yellow"), main="First Language Based Salary Distribution")
Scatterplots
par(mfrow=c(1,3))
plot(x=Placed$gmat_tot,y=Placed$salary,pch=19,col=c("red","yellow"),xlab="GMAT Score",ylab = "Salary")
plot(x=Placed$work_yrs,y=Placed$salary,pch=19,col=c("red","yellow"),xlab="Work Experience",ylab = "Salary")
plot(x=Placed$age,y=Placed$salary,pch=19,col=c("red","yellow"),xlab="Age",ylab = "Salary")
ScatterPlotMatrix
library(car)##
## Attaching package: 'car'## The following object is masked from 'package:psych':
##
## logitscatterplotMatrix(~Placed$gmat_tot+Placed$work_yrs+Placed$sex+Placed$age+Placed$salary)
OBSERVATIONS: 1) Salary decreases with the increase in TOtal Gmat Score. 2) Salary Increases with the increase in Work Experience. 3) Salary Increases with the increase in Age. 4) Salary of Females is less than the Salary of Males.
Corrgram
library(corrgram)
corrgram(Placed,upper.panel = panel.pie,lower.panel = panel.shade,text.panel = panel.txt)
From the Corrgram, we can deduce that “Salary” is only positively correlated with the “Age”, “S_avg”,“Work_Yrs”