Regression Analysis New Formulas

Regression AnalysisHypothesis 5Ho = Involvement in decision making has no association with Organizational performance

H1 = Involvement in decision making is associated with Organizational performance

Org_ performance = + 1(Job_satisfactoin) + 2 (ANOVAa

ModelSum of SquaresdfMean SquareFSig.

1Regression2.02012.020.572.453b

Residual176.673503.533

Total178.69251

a. Dependent Variable: Performance_Increase

b. Predictors: (Constant), Involvement in Decisions

Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientstSig.

BStd. ErrorBeta

1(Constant)5.7001.1894.795.000

Involvement in Decisions.345.457.106.756.453


Ho = Job satisfaction has no association with Organizational performance

H1 = Job satisfaction is associated with Organizational performance

ANOVAa


1Regression2.79312.793.794.377b

Residual175.899503.518

Total178.69251


b. Predictors: (Constant), Job satisfaction

Coefficientsa


BStd. ErrorBeta

1(Constant)5.2461.5163.461.001

Job satisfaction.427.479.125.891.377


Ho = Empowerment has no association with Organizational performance

H1 = Empowerment is associated with Organizational performance

ANOVAa


1Regression4.23214.2321.213.276b

Residual174.460503.489

Total178.69251


b. Predictors: (Constant), Empowerment

Coefficientsa


BStd. ErrorBeta

1(Constant)5.2211.2584.149.000

Empowerment.449.408.1541.101.276


Ho = Employee promotion has no association with Organizational performance

H1 = Employee promotion is associated with Organizational performance

ANOVAa


1Regression17.733117.7335.509.023b

Residual160.959503.219

Total178.69251


b. Predictors: (Constant), Employee

Coefficientsa


BStd. ErrorBeta

1(Constant)3.3691.3892.425.019

Employee1.036.441.3152.347.023


Ho = Increase Professional standing has no association with Organizational performance

H1 = Increase Professional standing is associated with Organizational performance

ANOVAa


1Regression25.451125.4518.304.006b

Residual153.242503.065

Total178.69251


b. Predictors: (Constant), Professional standing

Coefficientsa


BStd. ErrorBeta

1(Constant)2.9301.2892.273.027

Professional standing1.054.366.3772.882.006


ANOVAa


1Regression5.43651.087.424.829b

Residual117.871462.562

Total123.30851


b. Predictors: (Constant), Professional standing, Involvement in Decisions, Empowerment, Job satisfactino, Employee

Final tablesCoefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientstSig.90.0% Confidence Interval for B

BStd. ErrorBetaLower BoundUpper Bound

1(Constant)3.9282.2751.727.091.1107.746

Involvement in Decisions.193.392.072.492.625-.465.851

Job satisfaction.105.432.037.244.809-.620.830

Empowerment.217.365.090.596.554-.395.830

Employee.149.427.055.349.728-.568.866

Professional standing.295.365.127.808.423-.317.907


Hypothesis regarding HRIS and componentsHRIS & BPR

ANOVAa


1Regression6.41816.4187.252.010b

Residual44.25550.885

Total50.67351

a. Dependent Variable: What is the degree(extent) of using HRIS in making SHRM decisions

b. Predictors: (Constant), What is the extent of application HRIS in making Business Process reengineering decision

Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientsTSig.

BStd. ErrorBeta

1(Constant)2.606.4585.690.000

What is the extent of application HRIS in making Business Process reengineering decision.314.116.3562.693.010


Recruitment & Selection

ANOVAa


1Regression6.03116.0316.755.012b

Residual44.64250.893

Total50.67351


b. Predictors: (Constant), What is the extent of applicaiotn HRIS in Recruitment & Selection

Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientsTSig.

BStd. ErrorBeta

1(Constant)-.0671.489-.045.964

What is the extent of application HRIS in Recruitment & Selection.808.311.3452.599.012


Collinearity98

Collinearity between variables is always present. A problem occurs if the degree of collinearity

is high enough to bias the estimates.

Note: Collinearity means that two or more of the independent/explanatory variables in a

regression have a linear relationship. This causes a problem in the interpretation of theregression results. If the variables have a close linear relationship, then the estimated regression

coefficients and T-statistics may not be able to properly isolate the unique effect/role of each

variable and the confidence with which we can presume these effects to be true. The close

relationship of the variables makes this isolation difficult. ANOVAa


1Regression5.43651.087.424.829b

Residual117.871462.562

Total123.30851



Summary measures for testing and detecting collinearity include:

Running bivariate and partial correlations (see section 5.3). A bivariate or partial

correlation coefficient greater than 0.8 (in absolute terms) between two variables indicates

the presence of significant collinearity between them.

Collinearity is indicated if the R-square is high (greater than 0.7599) and only a few Tvalues

are significant.

In section 7.1, we asked SPSS for "Collinearity diagnostics" under the regression option

"statistics." Here we analyze the table that is produced. Significant collinearity is present if

the condition index is >10. If the condition index is greater than 30, then severe collinearity

is indicated (see next table). Check your textbook for more on collinearity diagnostics.

Model Summary

ModelRR SquareAdjusted R SquareStd. Error of the Estimate

1.210a.044-.0601.60076

a. Predictors: (Constant), Professional standing, Involvement in Decisions, Empowerment, Job satisfactino, Employee

Regression

Notes

Output Created10-NOV-2014 01:13:35

Comments

InputDataC:\Users\danish\Downloads\Documents\Non financial.sav

Active DatasetDataSet2

Filter

Weight

Split File

N of Rows in Working Data File52

Missing Value HandlingDefinition of MissingUser-defined missing values are treated as missing.

Cases UsedStatistics are based on cases with no missing values for any variable used.

SyntaxREGRESSION

/MISSING LISTWISE

/STATISTICS COEFF OUTS CI(95) R ANOVA

/CRITERIA=PIN(.05) POUT(.10)

/NOORIGIN

/DEPENDENT Performance_Increase

/METHOD=ENTER Involvement Job_satisfaction Empowerment Employee_promotions Professional_standing.

ResourcesProcessor Time00:00:00.02

Elapsed Time00:00:00.02

Memory Required2732 bytes

Additional Memory Required for Residual Plots0 bytes

[DataSet2] C:\Users\danish\Downloads\Documents\Non financial.sav

Variables Entered/Removeda

ModelVariables EnteredVariables RemovedMethod

1Professional standing, Involvement in Decisions, Empowerment, Job satisfactino, Employeeb.Enter


b. All requested variables entered.

Model Summary


1.210a.044-.0601.60076


ANOVAa


1Regression5.43651.087.424.829b

Residual117.871462.562

Total123.30851



Coefficientsa



1(Constant)3.9282.2751.727.091-.6508.506


Job satisfactino.105.432.037.244.809-.764.975

Empowerment.217.365.090.596.554-.517.952

Employee.149.427.055.349.728-.7101.009



Model Summary


1.210a.044-.0601.60076


ANOVAa


1Regression5.43651.087.424.829b

Residual117.871462.562

Total123.30851



Coefficientsa



1(Constant)3.9282.2751.727.091-.6508.506


Job satisfactino.105.432.037.244.809-.764.975

Empowerment.217.365.090.596.554-.517.952

Employee.149.427.055.349.728-.7101.009



Model Summary


1.210a.044-.0601.60076


Coefficientsa

ModelUnstandardized CoefficientsStandardized CoefficientstSig.Collinearity Statistics

BStd. ErrorBetaToleranceVIF

1(Constant)3.9282.2751.727.091

Involvement in Decisions.193.392.072.492.625.9841.016

Job satisfactino.105.432.037.244.809.8971.115

Empowerment.217.365.090.596.554.9181.090

Employee.149.427.055.349.728.8501.176

Professional standing.295.365.127.808.423.8401.190


pg. 11