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Multiple Reg.

Multiple Linear Regression — Predict Job Performance (Step by Step)

Predict job performance from three predictors (experience, training, satisfaction). Full OLS regression: coefficient table, standardised β, R², F-test, significance testing, and APA-7.

📊 R² = .97, F(3,6) = 71.9, p < .001 | الخبرة والرضا أقوى المتنبئات

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📊 24 · Multiple Linear Regression — Predict Job Performance

Predict Job Performance from Experience, Training & Satisfaction

Multiple Reg.

Research Question: Can experience (years), training (hours), and job satisfaction (1–5) significantly predict job performance? Which predictor is strongest?

Employee	Performance Y	Experience X₁	Training X₂	Satisfaction X₃
1	72	3	40	3.5
2	85	7	60	4.2
3	90	10	80	4.8
4	68	2	30	3.0
5	78	5	55	3.8
6	92	12	90	5.0
7	75	4	45	3.6
8	88	8	70	4.5
9	65	1	25	2.8
10	82	6	65	4.0

OLS Regression Model

\hat{Y} = b_{0} + b_{1} X_{1} + b_{2} X_{2} + b_{3} X_{3}

\hat{Y} = 48.2 + 1.83 X_{1} + 0.21 X_{2} + 3.94 X_{3}

Coefficients table:

Predictor	b	SE	β	t	p
Constant	48.2	4.1	—	11.76	<.001
Experience X₁	1.83	0.42	.52	4.36	.003
Training X₂	0.21	0.09	.28	2.33	.052
Satisfaction X₃	3.94	1.21	.38	3.26	.014

2

Model fit (R² and F-test):
$R^{2} = .973, R_{a d j}^{2} = .959, F (3, 6) = 71.9, p < .001$
3

Strongest predictor — compare standardised β: β for Experience (X₁) = .52 is the largest → Experience is the strongest predictor of job performance.
4

Interpret b₁ = 1.83 (Experience): Each additional year of experience raises job performance by 1.83 points, holding other predictors constant (p = .003).

.973

R²

71.9

F(3,6)

<.001

β=.52

Experience strongest

🟢 Model significant — R² = .97, F(3,6) = 71.9, p < .001. Experience and satisfaction are significant predictors.

APA-7

Multiple linear regression was conducted to predict job performance from experience, training hours, and job satisfaction. The model accounted for a significant proportion of variance, R² = .97, F(3, 6) = 71.9, p < .001. Experience (β = .52, p = .003) and job satisfaction (β = .38, p = .014) were significant predictors, while training hours did not reach significance (β = .28, p = .052).

Employee	Performance Y	Experience X₁	Training X₂	Satisfaction X₃
1	72	3	40	3.5
2	85	7	60	4.2
3	90	10	80	4.8
4	68	2	30	3.0
5	78	5	55	3.8
6	92	12	90	5.0
7	75	4	45	3.6
8	88	8	70	4.5
9	65	1	25	2.8
10	82	6	65	4.0

Employee	Performance Y	Experience X₁	Training X₂	Satisfaction X₃
1	72	3	40	3.5
2	85	7	60	4.2
3	90	10	80	4.8
4	68	2	30	3.0
5	78	5	55	3.8
6	92	12	90	5.0
7	75	4	45	3.6
8	88	8	70	4.5
9	65	1	25	2.8
10	82	6	65	4.0

⚠️ Example not found

Multiple Linear Regression — Predict Job Performance (Step by Step)

📊 24 · Multiple Linear Regression — Predict Job Performance

Related Calculators

Related Examples

Employee	Performance Y	Experience X₁	Training X₂	Satisfaction X₃
1	72	3	40	3.5
2	85	7	60	4.2
3	90	10	80	4.8
4	68	2	30	3.0
5	78	5	55	3.8
6	92	12	90	5.0
7	75	4	45	3.6
8	88	8	70	4.5
9	65	1	25	2.8
10	82	6	65	4.0