Statistical Thinking for Managers
Focusing on the analysis of data using modern statistical and spreadsheet software, Hildebrand and Ott emphasize making sense of data and discuss not only how a statistical method is applied, but why and why not. Throughout the book, the authors integrate computer use into the development of statistical concepts, emphasizing the value of looking at data to make sure the right questions are being asked. The real-life applications and examples throughout challenge students to think like managers. The case that concludes every chapter asks students to deal with a relatively unstructured situation and to explain the statistical reasoning in nontechnical language. Modern statistical methods, including resampling and bootstrapping are included. In addition, the authors emphasize quality control and improvement throughout the book and include three full chapters on regression and correlation methods.
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Summarizing Data About One Variable
R First Look at Probability
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Analysis of Variance ANOVA approximation assume assumption autocorrelation average binomial boxplot Calculate chapter Coef coefficient column computer package confidence interval constant continuous random variable correlation cost covariance customers data of Exercise Definition difference effect Empirical Rule equal estimate example expected value factor Find the probability forecast formula frequencies fY(y histogram income independent variables indicate interaction joint probability linear manager marginal probabilities Mean Square measure median method Minitab Minitab output multiple regression normal distribution null hypothesis obtained outliers p-value particular plot population mean predictive value predictors previous exercise probability density probability distribution probability tree problem proportion PY(y randomly chosen reasonable regression equation regression model rejected research hypothesis residual standard deviation sample mean scatterplot selected shown in Figure simple random sample skewness slope Solution SOURCE OF SS standard error SUBO Sum of Squares Suppose Total trend trials yields