JMP Means Business: Statistical Models for Management
JMP Means Business: Statistical Models for Management, by Josef Schmee and Jane Oppenlander, covers basic methods and models of classical statistics. Designed for business and MBA students, as well as industry professionals who need to use and interpret statistics, JMP Means Business covers data collection, descriptive statistics, distributions, confidence intervals and hypothesis tests, analysis of variance, contingency tables, simple and multiple regression, and exponential smoothing of time series. The easy-to-use format includes verbal and graphical explanations and promotes standard problem-solving techniques, with a limited use of formulas. Examples from business and industry serve to introduce each topic. Each example starts with a problem definition and data requirements, followed by a step-by-step analysis with JMP. Relevant output from this analysis is used to explain each method and to provide the basis for interpretation. Each chapter ends with a summary and a collection of problems for further study. SAS Products and Releases: JMP software: 8.0.2, 8.0.1, 8.0, 7.01, 7.0.2, 7.0 Operating Systems: All
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alternative hypothesis assumptions average beta box plot branch profits brand calculate Chi-Square Click column confidence interval contains Copyright correlation coefficient customers data table default equation example factor level combinations Fit Model formula function graph histogram hypothesis test interaction intercept Jane Oppenlander Josef level means leverage plots linear loans LSMeans Mean0 Means Business measures menu method Models for Management multiple regression nominal variable Normal distribution null hypothesis Number of Orders observations outcome outliers p-value Parameter Estimates platform point estimate population prediction intervals Prob probability problem proportion quantile questionnaire random variable regression analysis regression model relationship represents requires residuals response RMSE Sales sample mean sampling units SAS Figure scatterplot Schmee selected shown in Figure shows simple regression slope specific standard deviation Statistical Models statistically significant stepwise regression Student’s Sum of Squares Summary t-Ratio test statistic Total two-sided values variance versus X-factor X-variables