دانلود کتاب Quality of Work-Life During Pandemic: Data Analysis and Mathematical Modeling

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Preface
The key contributions presented in the scope of this book are listed as follows:
Acknowledgements
Contents
About the Editors
1 Introduction
1.1 Introduction to Quality of Work-Life
1.1.1 Characteristics of Quality of Work-Life
1.1.2 Stages of QWL Process
1.1.3 Importance of Quality of Work-Life
1.1.4 Methodology to Improve QWL
1.2 Effects of Pandemic on Quality of Work-Life of Employees
1.3 Strategies to Reduce the Pandemic Impact on Employees
1.3.1 From Company’s Perspectives
1.4 Work-Life Balance: Tips to Reduce Pandemic Pressure
1.4.1 Reduce Multitasking
1.4.2 Deep Breath and Meditation
1.4.3 Exercise and Eating
1.4.4 Define Individual Space at Home
1.5 Employee Engagement Practices During COVID‐19
1.5.1 Virtual Yoga or Workout
1.5.2 Online Happy Hour
1.5.3 Cook Together Online
1.5.4 Virtual Team Building Activities
1.5.5 Games on House Party
1.5.6 Virtual Group Training Programs
1.5.7 Host Virtual Lunch and Learning
1.6 Discussion
1.7 Summary
References
2 Data Analysis and Mathematical Modeling for COVID-19 Pandemic
2.1 Introduction
2.2 Need of Data Analysis and Mathematical Modeling
2.3 Tools for Mathematical Modeling
2.4 Pragmatic Approach for Mathematical Modeling
2.4.1 Rough Set Theory
2.4.2 Conditional Probability
2.4.3 Graph Theory
2.5 Summary
References
3 Influence of COVID-19 on Quality of Work-Life in Real Estate Sector
3.1 Introduction
3.2 Literature Survey
3.3 Dataset and Data Description
3.4 Descriptive Statistics
3.5 Mathematical Modeling
3.6 Data Analysis and Visualization
3.7 Summary
References
4 Impact of COVID-19 Pandemic on Education Sector
4.1 Introduction
4.2 Literature Survey
4.3 Dataset and Data Description
4.4 Descriptive Statistics
4.5 Mathematical Modeling
4.6 Machine Learning-Based Analysis and Visualization
4.7 Summary
References
5 Influence of COVID-19 on Quality of Work-Life in Information Technology/Software Industries
5.1 Introduction
5.2 Literature Work
5.3 Dataset and Data Description
5.4 Descriptive Statistics
5.5 Mathematical Modeling
5.6 Machine Learning-Based Analysis and Visualization
5.7 Summary
References
6 Impact of COVID-19 Pandemic on Agriculture Sector
6.1 Introduction
6.2 Literature Work
6.3 Dataset and Data Description
6.4 Descriptive Statistics
6.5 Mathematical Modeling
6.6 Machine Learning-Based Analysis and Visualization
6.7 Summary
References
7 Exploratory Data Analysis
7.1 Principle Component Analysis Overview
7.2 PCA for the Categorical Survey Dataset
7.3 Case Studies
7.3.1 PCA Computations
7.3.2 Interpreting the PCA
7.4 Summary
References
8 Discussion
8.1 Sample Size and Estimation of Sample Size
8.2 Data Collection Challenges
8.3 Problems with Less Sample Size
8.4 Validity and Reliability of Questionnaires
8.5 Summary
References
9 Conclusions
9.1 Conclusions
Index