BUSINESS ANALYTICS 2ED
			 Communicating with Numbers
			
 
       	Alison Kelly , Kevin Lertwachara , Leida Chen y Sanjiv Jaggia
Editorial: McGraw-Hill Higher Education
       
       
        Edición:  2 
       
	      
		  Fecha Publicación: 2022 
    
       
      
       	ISBN:  9781265087685 
      
      
       	ISBN ebook:  9781265750640 
		
      
      
       	Páginas:  801 
		        
      
      
       	Grado:  Universitario 
		        
      
      
       	Área:  Economía y Empresa
		        
      
      
       	Sección:  Economía 
	
      
      	  
		
			Idioma:  Inglés 
		
      
	  
	  
	  
       
        
				
				
				 
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Edición: 2
Fecha Publicación: 2022
ISBN: 9781265087685
ISBN ebook: 9781265750640
Páginas: 801
Grado: Universitario
Área: Economía y Empresa
Sección: Economía
Idioma: Inglés
        
				
				
				 
    Tweet
  
  
 
	
	
		
		
		   
		🌎 Visita la edición en Español
		
		
		
	
Chapter 1. Introduction to Business Analytics
Chapter 2. Data Management and Wrangling
Chapter 3. Summary Measures
Chapter 4. Data Visualization
Chapter 5. Probability and Probability Distributions
Chapter 6. Statistical Inference
Chapter 7. Regression Analysis
Chapter 8. More Topics in Regression Analysis
Chapter 9. Logistic Regression
Chapter 10. Forecasting with Time Series Data
Chapter 11. Introduction to Data Mining
Chapter 12. Supervised Data Mining: k-Nearest Neighbors and Naïve Bayes
Chapter 13. Supervised Data Mining: Decision Trees
Chapter 14. Unsupervised Data Mining
Chapter 15. Spreadsheet Modeling
Chapter 16. Risk Analysis and Simulation
Chapter 17. Optimization: Linear Programming
Chapter 18. More Applications in Optimization
Apppendixes
*La edición digital no incluye códigos de acceso a material adicional o programas mencionados en el libro.
Business Analytics: Communicating with Numbers was written from the ground up to prepare students to understand, manage, and visualize the data, apply the appropriate tools, and communicate the findings and their relevance. Unlike other texts that simply repackage statistics and traditional operations research topics, this text seamlessly threads the topics of data wrangling, descriptive analytics, predictive analytics, and prescriptive analytics into a cohesive whole. It provides a holistic analytics process, including dealing with real life data that are not necessarily 'clean' and/or 'small' and stresses the importance of effectively communicating findings by including features such as a synopsis (a short writing sample) and a sample report (a longer writing sample) in every chapter. These features help students develop skills in articulating the business value of analytics by communicating insights gained from a non-technical standpoint.
Suffolk University
Kevin Lertwachara
California Polytechnic State University
Leida Chen
California Polytechnic State University
Sanjiv Jaggia
California Polytechnic State University
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