Question: Regression Analsyis Regression Statistics Multiple R 0 . 6 5 5 6 6 7 3 5 7 R Square 0 . 4 2 9 8

Regression Analsyis
Regression Statistics
Multiple R 0.655667357
R Square 0.429899683
Adjusted R Square 0.412753809
Standard Error 5926.636889
Observations 138
ANOVA
df SS MS F Significance F
Regression 43522768655880692163.825.07306880.00
Residual 133467162830035125024.82
Total 1378194396956
Coefficients Standard Error t Stat P-value Lower 95% Upper 95% Lower 95.0% Upper 95.0%
Intercept 15526.025178105.7372021.9154365340.057583385-506.80903231558.85937-506.80903231558.85937
Productivity 661.5257995189.8136153.4851335590.000666703286.08182171036.969777286.08182171036.969777
Salary 3.5994605012.2757078271.5816883250.11609594-0.9018014288.100722431-0.9018014288.100722431
HiringCost -3.596376682.275641278-1.5803794360.116395322-8.0975069790.904753619-8.0975069790.904753619
BradfordFactor -16.042272214.069418063-3.9421538820.000129912-24.09142351-7.993120918-24.09142351-7.993120918
Correlation
Profitability Productivity Salary HiringCost BradfordFactor
Profitability 1
Productivity 0.5921411911
Salary 0.2112636490.2996011241
HiringCost 0.2102610270.3005650160.9998928491
BradfordFactor -0.606919654-0.716639056-0.288847747-0.289378071
Description of Key Metrics and Proposed Causal Relationships
Define and describe the key business outcome metrics (dependent variables) and other metrics (independent variables) identified in Assignment 2.
Propose possible causal relationships between these metrics based on theoretical considerations and prior research.
Discuss the importance of understanding these relationships for improving organizational performance

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