GET DATA

/TYPE=XLSX
/FILE='C:\Users\lita\Downloads\Employee data (1).xlsx'
/SHEET=name 'Employee data'
/CELLRANGE=FULL
/READNAMES=ON
/DATATYPEMIN PERCENTAGE=95.0
/HIDDEN IGNORE=YES.
EXECUTE.
DATASET NAME DataSet1 WINDOW=FRONT.
DESCRIPTIVES VARIABLES=Currentsalary Previoussalary Previousexperience
/STATISTICS=MEAN STDDEV VARIANCE RANGE MIN MAX SEMEAN.

Descriptives

Notes
Output Created 18-OCT-2024 15:16:20
Comments
Input Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data 474
File
Missing Value Handling Definition of Missing User defined missing
values are treated as
missing.
Cases Used All non-missing data are
used.
Syntax DESCRIPTIVES
VARIABLES=Currentsalar
y Previoussalary
Previousexperience
/STATISTICS=MEAN
STDDEV VARIANCE
RANGE MIN MAX
SEMEAN.
Resources Processor Time 00:00:00,00
Elapsed Time 00:00:00,01

[DataSet1]
Page 1
 Descriptive Statistics
N Range Minimum Maximum Mean
Statistic Statistic Statistic Statistic Statistic Std. Error
Current salary 474 119250 15750 135000 34419.57 784.311 17075.661
Previous salary 473 70980 9000 79980 16994.98 361.657 7865.530
Previous experience 474 476 0 476 95.86 4.804 104.586
Valid N (listwise) 473

Descriptive Statistics
Std. Deviation Variance
Statistic Statistic
Current salary 17075.661 291578214.5
Previous salary 7865.530 61866558.69
Previous experience 104.586 10938.281
Valid N (listwise)

SAVE OUTFILE='C:\Users\lita\Downloads\Untitled2.sav'
/COMPRESSED.
VARSTOCASES
/ID=id1
/MAKE score FROM Currentsalary Previoussalary Previousexperience
/INDEX=Index1(score)
/KEEP=id gender Education Jobcategory jobtime minority V10 V11 V12 V13 V14
/NULL=KEEP.

Variables to Cases

Page 2
 Notes
Output Created 18-OCT-2024 15:22:02
Comments
Input Data C:
\Users\mardi\Downloads\U
ntitled2.sav
Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
Syntax VARSTOCASES
/ID=id1
/MAKE score FROM
Currentsalary
Previoussalary
Previousexperience
/INDEX=Index1(score)
/KEEP=id gender
Education Jobcategory
jobtime minority V10 V11
V12 V13 V14
/NULL=KEEP.
Resources Processor Time 00:00:00,02
Elapsed Time 00:00:00,01

[DataSet1] C:\Users\lita\Downloads\Untitled2.sav

Generated Variables

Name Label
id1 <none>
Index1 <none>
score Current salary

Processing
Statistics
Variables In 14
Variables Out 14

Page 3
 * Chart Builder.
GGRAPH
/GRAPHDATASET NAME="graphdataset" VARIABLES=Index1 score MISSING=LISTWISE RE
PORTMISSING=NO
/GRAPHSPEC SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource(id("graphdataset"))
DATA: Index1=col(source(s), name("Index1"), unit.category())
DATA: score=col(source(s), name("score"))
DATA: id=col(source(s), name("$CASENUM"), unit.category())
GUIDE: axis(dim(1), label("Index1"))
GUIDE: axis(dim(2), label("Current salary"))
GUIDE: text.title(label("Simple Boxplot of Current salary by Index1"))
SCALE: linear(dim(2), include(0))
ELEMENT: schema(position(bin.quantile.letter(Index1*score)), label(id))
END GPL.

GGraph

Notes
Output Created 18-OCT-2024 15:28:10
Comments
Input Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data 1422
File

Page 4
 Notes
Syntax GGRAPH
/GRAPHDATASET
NAME="graphdataset"
VARIABLES=Index1 score
MISSING=LISTWISE
REPORTMISSING=NO
/GRAPHSPEC
SOURCE=INLINE.
BEGIN GPL
SOURCE: s=userSource
(id("graphdataset"))
DATA: Index1=col
(source(s), name
("Index1"), unit.category())
DATA: score=col(source
(s), name("score"))
DATA: id=col(source(s),
name("$CASENUM"), unit.
category())
GUIDE: axis(dim(1), label
("Index1"))
GUIDE: axis(dim(2), label
("Current salary"))
GUIDE: text.title(label
("Simple Boxplot of
Current salary by Index1"))
SCALE: linear(dim(2),
include(0))
ELEMENT: schema
(position(bin.quantile.letter
(Index1*score)), label(id))
END GPL.

Resources Processor Time 00:00:02,61

Page 5
 Simple Boxplot of Current salary by Index1
150000

85

94
52
307 1027
100000
Current salary

1336 316
100
1291
103 211 86
196
298 703 262
1366 385
157 265
1028
79 301
148 614
1 479
50000 95
518
410 101
599 308 104 212
590 80 158
188
197
53 452

162
288 9 408
0
Currentsalary Previousexperience Previoussalary

Index1

FREQUENCIES VARIABLES=gender Education Jobcategory jobtime

Frequencies

Page 6
 Notes
Output Created 18-OCT-2024 15:33:41
Comments
Input Active Dataset DataSet1
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data 1422
File
Missing Value Handling Definition of Missing User-defined missing
values are treated as
missing.
Cases Used Statistics are based on all
cases with valid data.
Syntax FREQUENCIES
VARIABLES=gender
Education Jobcategory
jobtime
/STATISTICS=STDDEV
VARIANCE RANGE
MINIMUM MAXIMUM
SEMEAN
/PIECHART FREQ
/ORDER=ANALYSIS.
Resources Processor Time 00:00:01,69
Elapsed Time 00:00:01,00

Statistics
gender Education Job category jobtime
N Valid 1422 1422 1422 1422
Missing 0 0 0 0
Std. Error of Mean .076 .020 .267
Std. Deviation 2.883 .773 10.054
Variance 8.311 .597 101.080
Range 13 2 35
Minimum 8 1 63
Maximum 21 3 98

Frequency Table

Page 7
 gender
Cumulative
Frequency Percent Valid Percent Percent
Valid f 648 45.6 45.6 45.6
m 774 54.4 54.4 100.0
Total 1422 100.0 100.0

Education
Cumulative
Frequency Percent Valid Percent Percent
Valid 8 159 11.2 11.2 11.2
12 570 40.1 40.1 51.3
14 18 1.3 1.3 52.5
15 348 24.5 24.5 77.0
16 177 12.4 12.4 89.5
17 33 2.3 2.3 91.8
18 27 1.9 1.9 93.7
19 81 5.7 5.7 99.4
20 6 .4 .4 99.8
21 3 .2 .2 100.0
Total 1422 100.0 100.0

Job category
Cumulative
Frequency Percent Valid Percent Percent
Valid 1 1089 76.6 76.6 76.6
2 81 5.7 5.7 82.3
3 252 17.7 17.7 100.0
Total 1422 100.0 100.0

Page 8
 jobtime
Cumulative
Frequency Percent Valid Percent Percent
Valid 63 9 .6 .6 .6
64 24 1.7 1.7 2.3
65 45 3.2 3.2 5.5
66 54 3.8 3.8 9.3
67 45 3.2 3.2 12.4
68 30 2.1 2.1 14.6
69 63 4.4 4.4 19.0
70 39 2.7 2.7 21.7
71 9 .6 .6 22.4
72 42 3.0 3.0 25.3
73 36 2.5 2.5 27.8
74 24 1.7 1.7 29.5
75 21 1.5 1.5 31.0
76 30 2.1 2.1 33.1
77 39 2.7 2.7 35.9
78 66 4.6 4.6 40.5
79 42 3.0 3.0 43.5
80 45 3.2 3.2 46.6
81 69 4.9 4.9 51.5
82 45 3.2 3.2 54.6
83 57 4.0 4.0 58.6
84 42 3.0 3.0 61.6
85 30 2.1 2.1 63.7
86 39 2.7 2.7 66.5
87 33 2.3 2.3 68.8
88 39 2.7 2.7 71.5
89 21 1.5 1.5 73.0
90 48 3.4 3.4 76.4
91 39 2.7 2.7 79.1
92 45 3.2 3.2 82.3
93 69 4.9 4.9 87.1
94 42 3.0 3.0 90.1
95 18 1.3 1.3 91.4
96 48 3.4 3.4 94.7
97 33 2.3 2.3 97.0
42 3.0 3.0 100.0 Page 9
 jobtime
Cumulative
Frequency Percent Valid Percent Percent
98 42 3.0 3.0 100.0
Total 1422 100.0 100.0

Pie Chart

gender
f
m

Page 10
 Education
8
12
14
15
16
17
18
19
20
21

Job category
1
2
3

Page 11
 jobtime
63 81
64 82
65 83
66 84
67 85
68 86
69 87
70 88
71 89
72 90
73 91
74 92
75 93
76 94
77 95
78 96
79 97
80 98

REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF OUTS R ANOVA
/CRITERIA=PIN(.05) POUT(.10)
/NOORIGIN
/DEPENDENT Currentsalary
/METHOD=ENTER Previoussalary
/RESIDUALS ID(gender).

Regression

Page 12
 Notes
Output Created 18-OCT-2024 15:51:30
Comments
Input Active Dataset DataSet2
Filter <none>
Weight <none>
Split File <none>
N of Rows in Working Data 474
File
Missing Value Handling Definition of Missing User-defined missing
values are treated as
missing.
Cases Used Statistics are based on
cases with no missing
values for any variable
used.
Syntax REGRESSION
/MISSING LISTWISE
/STATISTICS COEFF
OUTS R ANOVA
/CRITERIA=PIN(.05)
POUT(.10)
/NOORIGIN
/DEPENDENT
Currentsalary
/METHOD=ENTER
Previoussalary
/RESIDUALS ID(gender).
Resources Processor Time 00:00:00,05
Elapsed Time 00:00:00,03
Memory Required 2880 bytes
Additional Memory 0 bytes
Required for Residual Plots

a
Variables Entered/Removed
Variables Variables
Model Entered Removed Method
1 Previous . Enter
salary b

a. Dependent Variable: Current salary

Page 13
 b
Model Summary
Adjusted R Std. Error of the
Model R R Square Square Estimate
1 .880a .774 .773 8122.341

a. Predictors: (Constant), Previous salary

a
ANOVA
Sum of
Model Squares df Mean Square F Sig.
1 Regression 1.063E+11 1 1.063E+11 1611.772 .000b
Residual 3.107E+10 471 65972423.16
Total 1.374E+11 472
a. Dependent Variable: Current salary
b. Predictors: (Constant), Previous salary

a
Coefficients
Standardized
Unstandardized Coefficients Coefficients
Model B Std. Error Beta t Sig.
1 (Constant) 1941.248 889.952 2.181 .030
Previous salary 1.908 .048 .880 40.147 .000
a. Dependent Variable: Current salary

a
Residuals Statistics
Minimum Maximum Mean Std. Deviation N
Predicted Value 19115.45 154562.67 34371.83 15009.357 473
Residual -35374.102 49312.938 .000 8113.732 473
Std. Predicted Value -1.016 8.008 .000 1.000 473
Std. Residual -4.355 6.071 .000 .999 473
a. Dependent Variable: Current salary

Page 14