Practical Statistics is an introduction and reference guide for those studying business and management at postgraduate level, MBA students and professionals. It gives the reader a solid understanding of statistics without being too simple or mind-numbingly complex. It turns statistics, often perceived as being difficult and pointless, into something approachable and sensible. John Buglear emphasizes the importance of working back from results rather than working out results. He starts the book with basic concepts that get increasingly more complex, from simple diagrams to multivariate methods,…mehr
Practical Statistics is an introduction and reference guide for those studying business and management at postgraduate level, MBA students and professionals. It gives the reader a solid understanding of statistics without being too simple or mind-numbingly complex. It turns statistics, often perceived as being difficult and pointless, into something approachable and sensible. John Buglear emphasizes the importance of working back from results rather than working out results. He starts the book with basic concepts that get increasingly more complex, from simple diagrams to multivariate methods, as well as using simple metaphors to aid understanding. The book has a two-level approach, with sections headed 'The essentials' and 'Tell me more' to support learning at all levels. A rare book on business statistics that students and professionals can really understand, Practical Statistics supports active learning with use of applications and case studies, invaluable online learning materials and fully worked solutions to questions in the review section.
John Buglear is Head of the Nottingham Business School Division of Management. He teaches quantitative methods and basic accounting on undergraduate and graduate programs. He has lectured in statistics, operational research and research methods on graduate international programs with: BP Exploration, PricewaterhouseCoopers, and Lloyds TSB. He is also the author of Stats Means Business (Butterworth-Heinemann) and Quantitative Methods for Business and Management (Pearson).
Inhaltsangabe
Introduction 01 What you need to know before you start analysing data What is data? Populations and samples Measuring risk The normal distribution and sampling distributions 02 Analysing univariate data Histograms and boxplots Measures of location and spread Inference: estimation and hypothesis testing of population means Representing non-metric data: pie charts and bar charts Estimating and hypothesis testing of population proportions using sample proportions 03 Analysing bivariate data Scatter diagrams Correlation analysis Simple linear regression Non-linear regression Cluster and stacked bar charts Contingency analysis Logistic regression 04 Analysing multivariate data with dependency Preliminary analysis for multiple regression Multiple regression analysis Multiple regression model-building Discriminant analysis Multivariate analysis of variance An outline of canonical correlation analysis 05 Analysing multivariate data for interdependency Principal components analysis Factor analysis Cluster analysis Cronbach's α Coda: Some tips on writing up projects involving data analysis
Introduction 01 What you need to know before you start analysing data What is data? Populations and samples Measuring risk The normal distribution and sampling distributions 02 Analysing univariate data Histograms and boxplots Measures of location and spread Inference: estimation and hypothesis testing of population means Representing non-metric data: pie charts and bar charts Estimating and hypothesis testing of population proportions using sample proportions 03 Analysing bivariate data Scatter diagrams Correlation analysis Simple linear regression Non-linear regression Cluster and stacked bar charts Contingency analysis Logistic regression 04 Analysing multivariate data with dependency Preliminary analysis for multiple regression Multiple regression analysis Multiple regression model-building Discriminant analysis Multivariate analysis of variance An outline of canonical correlation analysis 05 Analysing multivariate data for interdependency Principal components analysis Factor analysis Cluster analysis Cronbach's α Coda: Some tips on writing up projects involving data analysis
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