Read Now Multiple Regression and Correlation (STATISTICS: A UNIVERSAL GUIDE TO THE UNKNOWN Book 17) DOC

The academic discipline of Statistics is a branch of mathematics that develops and uses techniques for the careful collection, effective presentation, and proper analysis of numerical information. These techniques can be applied to find answers to questions that arise in all areas of human endeavor. Medical researchers use them to test the safety and effectiveness of new drugs or to appraise the effects of lifestyle changes; nutritionists use them to investigate health claims associated with foods or dietary supplements; business executives use them to assess the results of marketing campaigns or the effect of new methods of production on product quality. Economists use them to forecast the business cycle; politicians to predict the outcome of future elections. Spies use them decipher coded messages. The list goes on. No wonder that Statistics has been called a universal guide to the unknown. Consider your own health, one among millions of topics that Statistics could address. Every day we meet new health-related stories ̶ about prescription and over-the-counter drugs, medical devices and procedures, the lifestyle we should adopt, foods we should favor, and dietary supplements that would surely add years to our lives. Rightly, we dismiss many of these stories as pure snake oil. Mayonnaise prevents Alzheimer’s? Chelation therapy blasts arterial plaque? Food coloring lowers bad cholesterol? Cinnamon clobbers diabetes? Grapefruit erases breast cancer? Watermelon slashes prostate cancer? Come on! But what about more serious-sounding claims? True enough, reports about ACE inhibitors and beta blockers, Advil and Motrin, 64-slice CT scans and PSA tests, drug-coated stents and the DASH diet appear to be far removed from snake oil, but false claims about any of these may well occur, which makes them snake oil no less than absurd and fantastic claims about mayonnaise and Alzheimer’s. Consider how glowing press releases of one time, even by renowned medical journals or the FDA, are often followed by conflicting stories at a later time, which makes us ask all too often: Whom can we believe?A knowledge of Statistics offers a remedy. It helps us separate bogus claims from the real thing. The field, however, is so vast that no single book can reasonably cover all of it. Nor can it anticipate which topics will be of interest to any given person. This author, therefore, has divided the field into 24 sections that are made available as separate electronic books from which prospective students and teachers can select the subset that is most useful to them. After reading Book 17 of this series, you will be able to employ multiple regression and correlation techniques to test whether and how the value of one variable, Y, is affected by the values of two or more other variables, X1 , X2 , X3 , and so on. Among other things, you will learn to 1. construct least-squares multiple regression equations that summarize the relationship among the variables mathematically, 2. estimate the average value of Y in the population of interest that is associated with any given set of X values, 3. estimate the next value of Y likely to be encountered by sampling the population of interest, given any set of X values, 4. develop confidence intervals and conduct hypothesis tests about the coefficients of true regression equations, 5. compute coefficients of multiple determination and similar indexes that summarize the strength of association among the variables of interest in a single number, and 6. perform regression diagnostics to determine whether you can use an estimated regression equation to make valid inferences about the underlying true regression.Book 17 concludes with numerous tests (over 80 Practice Problems, almost 100 True-False and Multiple-Choice questions, and a lengthy Key Terms Recognition exercise), along with solutions and answers for all of these.