Business Statistics Using Python
1st Edition
9355327889
·
9789355327888
© 2025 | Published: June 17, 2024
Overview:This book redefines the way business statistics is approached by seamlessly integrating the power of Python into statistical analysis. The book adopts a descriptive writing style, explaining relevant statistical concepts within theframework …
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1. Introduction to Business Statistics and Python
2. Descriptive Statistics Using Python
3. Graphical Display of Data
4. Probability Distributions
5. Sampling and Confidence Interval
6. Single Sample Hypothesis Testing
7. Two-Sample Hypothesis Testing
8. More Than Two-Sample Hypothesis Testing
9. Simple and Multiple Regressions
10. Some Important Aspects of Regression Analysis
11. Time Series Forecasting
12. Nonparametric Methods
2. Descriptive Statistics Using Python
3. Graphical Display of Data
4. Probability Distributions
5. Sampling and Confidence Interval
6. Single Sample Hypothesis Testing
7. Two-Sample Hypothesis Testing
8. More Than Two-Sample Hypothesis Testing
9. Simple and Multiple Regressions
10. Some Important Aspects of Regression Analysis
11. Time Series Forecasting
12. Nonparametric Methods
Overview:
This book redefines the way business statistics is approached by seamlessly integrating the power of Python into statistical analysis. The book adopts a descriptive writing style, explaining relevant statistical concepts within the
framework of business management. Subsequently, it guides the reader through the exploration of statistical models, illustrating how Python can be effectively employed for their study.
Authored with a focus on practicality and real-world applications, this book serves as a comprehensive guide for graduate and postgraduate management students looking to leverage Python in business data analytics.
Key Features:
• Practical Integration: Seamlessly integrates business statistics principles with Python programming, providing readers with practical skills applicable to various domains.
• Enhanced Data Analysis: Utilizes the advantages of Python, such as its open-source nature and versatility, to perform advanced data analysis tasks beyond the capabilities of traditional tools.
• Application-Oriented Learning: Offers case studies and practice questions that demonstrate the application of statistical concepts in real-world scenarios, empowering readers to make informed decisions and solve complex business challenges.
• Holistic Approach: Covers descriptive statistics, data visualization, statistical inference, and modelling techniques, providing a comprehensive understanding of statistical analysis methods and their practical implementations.
• Over 150 solved examples using Python, making it highly relevant for those aspiring to pursue a career in the analytics industry.
This book redefines the way business statistics is approached by seamlessly integrating the power of Python into statistical analysis. The book adopts a descriptive writing style, explaining relevant statistical concepts within the
framework of business management. Subsequently, it guides the reader through the exploration of statistical models, illustrating how Python can be effectively employed for their study.
Authored with a focus on practicality and real-world applications, this book serves as a comprehensive guide for graduate and postgraduate management students looking to leverage Python in business data analytics.
Key Features:
• Practical Integration: Seamlessly integrates business statistics principles with Python programming, providing readers with practical skills applicable to various domains.
• Enhanced Data Analysis: Utilizes the advantages of Python, such as its open-source nature and versatility, to perform advanced data analysis tasks beyond the capabilities of traditional tools.
• Application-Oriented Learning: Offers case studies and practice questions that demonstrate the application of statistical concepts in real-world scenarios, empowering readers to make informed decisions and solve complex business challenges.
• Holistic Approach: Covers descriptive statistics, data visualization, statistical inference, and modelling techniques, providing a comprehensive understanding of statistical analysis methods and their practical implementations.
• Over 150 solved examples using Python, making it highly relevant for those aspiring to pursue a career in the analytics industry.
