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Cogent Business & Management Volume 11, 2024 - Issue 1
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Management

Unsupervised learning analysis of European working condition

Olumide S. Adesinaa Department of Mathematics & Statistics, Redeemer’s University, Ede, Osun State, Nigeria;b Data Science & Business Analytics, University of London-LSE, London, United KingdomView further author information
,
Adedayo F. Adedotunc Department of Mathematics, Covenant University, Ota, Ogun State, NigeriaCorrespondence[email protected]
View further author information
,
Semiu A. Alayandea Department of Mathematics & Statistics, Redeemer’s University, Ede, Osun State, NigeriaView further author information
,
Emmanuel O. Efe-Imafidond Department of Business Management, Covenant University, Ota, Ogun State, NigeriaView further author information
,
Tolulope F. Adesinae Department of Banking and Finance, Covenant University, Ota, Ogun State, NigeriaView further author information
,
Hillary I. Okagbuec Department of Mathematics, Covenant University, Ota, Ogun State, NigeriaView further author information
&
Oluwakemi O. Onayemid Department of Business Management, Covenant University, Ota, Ogun State, NigeriaView further author information
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Article: 2316644 | Received 30 Dec 2022, Accepted 02 Feb 2024, Published online: 24 Feb 2024

Figures & data

Figure 1. Usual weekly working hours, by country and sex. Source: (Eurofound, Citation2016).

Figure 1. Usual weekly working hours, by country and sex. Source: (Eurofound, Citation2016).

Table 1. Summary of response (Q87a–87e).

Table 4. Response for (Q90a–Q90c, Q90f).

Figure 2. Scree plots for PCA analysis of Q 87 a–e.

Figure 2. Scree plots for PCA analysis of Q 87 a–e.

Table 2. Principal component analysis of European Working Conditions Survey conducted for Q87 a–e

Table 3. Principal component analysis for Q 87 a–e.

Figure 3. PCA for European working conditions survey for Q87a–e. Source: Author.

Figure 3. PCA for European working conditions survey for Q87a–e. Source: Author.

Figure 4. Scree plots for PCA analysis of Q90a–f.

Figure 4. Scree plots for PCA analysis of Q90a–f.

Figure 5. PCA for European working conditions survey for Q90a–f. Source: Author.

Figure 5. PCA for European working conditions survey for Q90a–f. Source: Author.

Figure 6. Plot showing relationship among the variables.

Figure 6. Plot showing relationship among the variables.

Table 5. Response (Q90a–Q90c, Q90f).

Table 6. Principal component analysis for Q90a–f.

Table 7. Principal component analysis for Q87 a–e.

Table 8. Principal component analysis for (Q90a–Q90c, Q90f).

Figure 7. Plot showing relationship among the variables for (Q90a–Q90c, Q90f).

Figure 7. Plot showing relationship among the variables for (Q90a–Q90c, Q90f).

Table 9. PC1 and PC2 PCA for 2016 and 2022 Survey for Q87a–e.

Table 10. PC1 and PC2 PCA for 2016 and 2022 survey for Q90a–f.

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