PROBABILITY AND DISTRIBUTIONS IP

Probability and Distributions IP

Probability and Distributions IP

Frequency Distribution

The following tables shows the frequency distribution for the given variables

Statistics

Gender

Age

Department

Position

Tenure

N

Valid

50

50

50

50

50

Missing

0

0

0

0

0

Gender

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

male

19

38.0

38.0

38.0

female

31

62.0

62.0

100.0

Total

50

100.0

100.0

Age

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

16 - 21

8

16.0

16.0

16.0

22 - 49

42

84.0

84.0

100.0

Total

50

100.0

100.0

Department

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Human Resources

16

32.0

32.0

32.0

Information Technology

33

66.0

66.0

98.0

Administration

1

2.0

2.0

100.0

Total

50

100.0

100.0

Position

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Hourly Employee (Overtime Eligible)

40

80.0

80.0

80.0

Salaried Employee (No Overtime)

10

20.0

20.0

100.0

Total

50

100.0

100.0

Tenure

Frequency

Percent

Valid Percent

Cumulative Percent

Valid

Less than 2 years

24

48.0

48.0

48.0

2 to 5 years

24

48.0

48.0

96.0

Over 5 Years

2

4.0

4.0

100.0

Total

50

100.0

100.0

Graphical Representation

A "normal" circulation of variety outcomes in a exact bell-shaped bend, with the largest issue in the middle and easily arching symmetrical gradients on both edges of center. The characteristics of the benchmark usual circulation are tabulated in most statistical quotation works, permitting the somewhat so straightforward estimation of localities under the bend at any point.

A "skewed" circulation is one that is not symmetrical, but rather has a long follow in one direction. If the follow expands to the right, the bend is said to be right-skewed, or positively skewed. If the follow expands to the left, it is contrary skewed. PathMaker calculates the skewness of a histogram, and exhibitions it with the other statistics. Where skewness is present, vigilance should generally be concentrated on the follow, which could continue after the method specification restricts, and where much of the promise for enhancement usually lies.

Regression analysis

Descriptive Statistics

Mean

Std. Deviation

N

Age

58.78

5.441

100

Gender

145.80

36.843

100

Satisfaction

1.21

.891

100

Correlations

Age

Gender

Satisfaction

Pearson Correlation

Age

1.000

.691

.158

Gender

.691

1.000

.046

Satisfaction

.158

.046

1.000

Sig. (1-tailed)

Age

.

.000

.059

Gender

.000

.

.326

Satisfaction

.059

.326

.

N

Age

100

100

100

Gender

100

100

100

Satisfaction

100

100

100

This table gives details of the correlation between each pair of variables. We do not want strong correlations between the criterion and the predictor variables. The values here are acceptable.

Variables Entered/Removedb

Model

Variables Entered

Variables Removed

Method

1

Satisfaction, Gendera

.

Enter

a. All requested variables entered.

b. Dependent Variable: Age

Model Summary

Model

R

R Square

Adjusted R Square

Std. Error of the Estimate

1

.703a

.494

.483

3.912

a. Predictors: (Constant), Satisfaction, Gender

This table is important. The Adjusted R Square value tells us that our model accounts for 70.3% of variance in the ages it can be concluded that it is very good model.

ANOVAb

Model

Sum of Squares

df

Mean Square

F

Sig.

1

Regression

1446.898

2

723.449

47.279

.000a

Residual

1484.262

97

15.302

Total

2931.160

99

a. Predictors: (Constant), Satisfaction, Gender

b. Dependent Variable: Age

This table ...