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Regression Analysis and Modeling
內容大綱
Business analytics is an emerging area in modern business decision-making, gaining interest and popularity. This book discusses the models based on fact-based data to measure past business performance in order to guide organizations in visualizing and predicting future business performance and outcomes. Bringing terms, tools, and methods of analytics together, a comprehensive overview of analytics is given with an emphasis on predictive analytics. The book provides an introduction to and types of business analytics, the importance of analytics, and business intelligence. It also discusses the most widely used predictive models, including regression analysis, forecasting, data mining, and an introduction to recent applications of predictive analytics - machine learning, neural networks, and artificial intelligence. The current state, job outlook, and certifications in analytics are also examined. Chapter 7 gives an introduction to regression and correlation analysis. Regression techniques allow us to explore the relationship between different variables. Essentials to developing regression models are explained, including dependent and independent variables, the least squares method, determining the best-fitting line through the data points, calculating the slope and y-intercept of the best fitting regression line, interpreting the meaning of the regression line, and measures of association between the covariance and the correlation coefficient. Developing regression models to predict one variable using the other variable or multiple variables is also discussed. Successful implementation of regression models requires an understanding of different types of models. A lack of understanding may lead to drawing wrong conclusions and selecting alternative courses of action.