69 pages of complete step-by-step instructions showing how to perform nearly every major type of nonparametric test and how to do them all in Excel. This e-manual will make you an expert on knowing exactly how and when to use and set up in Excel all types of nonparametric tests, such as the Mann Whitney U Test, the Kruskall Wallis Test, the Wilcoxon Rank Sum Test for both large and small samples, the Spearman Correlation Coefficient Test, the Sign Test, and the Wilcoxon Signed Rank Test for both large and small samples. This e-manual is loaded with completed examples and screenshots in Excel of all the above of nonparametric tests being performed. The instructions are clear and easy-to-follow but at the graduate level. If you are currently taking a difficult graduate-level statistics course that covers nonparametric or normality tests, you will find this e-manual to be an outstanding course supplement that will explain nonparametric tests much more clearly than your textbook does. If you are a business manager, you will really appreciate how easily and clearly this e-manual will show you how you can perform nonparametric tests in Excel to solve difficult statistical problems on your job. Nonparametric tests are the most important of all statistical tests in business, but are not widely understood. Nonparametric testing must nearly always be performed in place of most well-known statistics tests when it is not known that samples are being taken from a normally distributed population. This is more often the case than not, yet not many people have a working knowledge of nonparametric testing. You will. This e-manual will make you an Excel Statistical Master of nonparametric testing.
50 pages of complete step-by-step instructions showing how to perform a number of well-known Normality tests and how to do them all in Excel. This e-manual will make you an expert on knowing exactly how and when to use these types of Normality tests: the Histogram, the Normal Probability Plot using 2 different methods, and the Chi-Square Goodness-Of-Fit Test, and how to set them all up in Excel. This e-manual is loaded with completed problems and step-by-step, easy-to-follow screenshots in Excel of all these different types of Normality tests. The instructions are clear and easy-to-follow but at the graduate level. If you are currently taking a difficult graduate-level statistics course that covers Normality testing, you will find this e-manual to be an outstanding course supplement that will explain Normality tests much more clearly than your textbook does. If you are a business manager, you will really appreciate how easily and clearly this e-manual will show you how you can perform these useful and quick Normality tests in Excel to verify data distributions on your job. Normality testing should always be performed before any of the widely-used parametric statistical tests are applied to data. Not many know how to do Normality testing. This e-manual will make you an Excel Statistical Master of Normality testing.
56 pages of clear and simple yet complete instructions about what t-tests are, how and when to use them, and how to set them up and solve them in Excel. This e-manual provides a thorough explanation of all of the major types of t-tests and their underlying formulas. Before you even begin to solve t-tests in Excel, the e-manual ensures that you have a solid, intuitive grasp of what each of the different variations of t-tests do and when each should be used. The e-manual shows you how to do these t-tests by hand and also in Excel. All of the t-tests formulas and functions built-in to Excel are explained in deep detail. All of problems are solved using the built-in Excel t-tests with lots of screenshots for complete clarity. A number of the problems also have their t-values and p-values calculated by hand so you can also see how it would be done manually. The instructions are clear and easy-to-follow but at the graduate level. If you are currently taking a difficult graduate-level statistics course that covers t-tests, you will find this e-manual to be an outstanding course supplement that will explain t-tests much more clearly than your textbook does. If you are a business manager, you will really appreciate how easily and clearly this e-manual will show you how you can set up t-tests in Excel to solve difficult statistical problems on your job. This e-manual will make you an Excel Statistical Master of the t-test.
Complete and practical yet easy-to-understand graduate-level statistics course with all of the problems worked out in Excel. Thoroughly covers all topics of an intense graduate statistics course using nothing but step-by-step, simple explanations. Loaded with completed, real-world problems all in Excel, this e-manual is an outstanding supplement to a graduate statistics course. Very clear explanations are used to show exactly how the Excel formulas integrate with the statistical frameworks being applied. The reader will learn how to master and apply graduate-level statistics much faster than a student in a normal graduate statistics course because this e-manual's emphasis is entirely on problem solving, not on useless, forgettable theory that fills up many statistics courses. This e-manual achieves two goals: teaching graduate-level statistical frameworks in an easy-to-understand way and then showing how to implement all of it in Excel. The widely-used Microsoft Excel program provides a very simple but incredibly complete platform to perform heavy-duty, advanced statistical analysis. All other statistical software packages, such as Minitab, SyStat, and SPSS, are expensive, require lots of user training, and expect that the user is an expert statistician right from the start. Not this e-manual nor Microsoft Excel. The ability to perform graduate-level statistics in Excel is an extremely useful and powerful tool for any graduate statistics student and business manager. Homework assignments can be quickly checked with Excel. Once difficult statistical business problems are now readily solvable in Excel. The easy-to-follow frameworks in this e-manual can be cleanly and swiftly duplicated in the real world and on statistics exams by hand (without Excel) right away. The lessons are all in bite-size chunks that are quickly absorbed for immediate use. More than half of the lessons in this e-manual are supplemented with step-by-step videos for more convenient learning. Some of the major topics covered in detail include regression, ANOVA, hypothesis tests, confidence intervals, combinations, permutations, correlation, covariance, t-tests, histograms, and charting. This e-manual also contains two complete chapters with numerous videos showing exactly how to create user-interactive graphs of the 10 major distributions in Excel. These user-interactive Excel graphs allow the user to vary the cells containing all of the distribution's parameters, such as mean, standard deviation, and degrees of freedom, and watch the graphed distribution instantly change right on the spreadsheet to conform to the new parameters. This is an excellent and unique tool to fully grasp the functionality of the distributions discussed in this e-manual. All problem-solving techniques are presented as step-by-step frameworks that can be readily applied to similar problems, not as seemingly unrelated and difficult-to-apply statistical theorems like most statistics course do. A number of problem-solving techniques are presented in this e-manual that do not appear in any other statistical text. One example of a statistical technique presented only in this e-manual and nowhere else is a detailed description showing how to solve every type of hypothesis test using the same four steps. A number of widely-used and complicated statistical tests, such as the chi-square independence test, the chi-square population variance test, and conjoint analysis using dummy variable regression are described from top to bottom and also in Excel. Graduate statistics students and business managers will find this e-manual to be, by far, the easiest and fastest way to master graduate-level statistics and to apply advanced statistics in Excel to solve difficult, real-world problems, homework assignments, and exam questions. The reader of this e-manual will quickly become an Excel Statistical Master.
Designed for both students and practitioners, the new edition of this popular text has been thoroughly revised. It incorporates the latest thinking in public administration and nonprofit management. The book integrates both quantitative and qualitative approaches to research, and also provides specific instruction in the use of commonly available statistical software programs such as Excel and SPSS. The book is exceptionally well illustrated, with plentiful exhibits, tables, figures, and exercises.
The third edition of Research Methods for Political Science retains its effective approach to helping students learn what to research, why to research and how to research. The text integrates both quantitative and qualitative approaches to research in one volume and covers such important topics as research design, specifying research problems, designing questionnaires and writing questions, designing and carrying out qualitative research and analyzing both quantitative and qualitative research data. Heavily illustrated, classroom tested, exceptionally readable and engaging, the text presents statistical methods in a conversational tone to help students surmount "math phobia." Updates to this new edition include: Research topics chapters have been upgraded and expanded. Two mixed methods design chapters have been added. A new chapter on hermeneutic analysis designs and research with large data sets. The chapter on multivariate statistics has been expanded, with an expanded discussion on logistic regression. Tools on how to prepare and present research findings are now featured in the appendix, allowing instructors more flexibility when teaching their courses. Research Methods for Political Science will give students the confidence and knowledge they need to understand the methods and basics skills for data collection, presentation and analysis.
Comprehensively teaches the basics of testing statistical assumptions in research and the importance in doing so This book facilitates researchers in checking the assumptions of statistical tests used in their research by focusing on the importance of checking assumptions in using statistical methods, showing them how to check assumptions, and explaining what to do if assumptions are not met. Testing Statistical Assumptions in Research discusses the concepts of hypothesis testing and statistical errors in detail, as well as the concepts of power, sample size, and effect size. It introduces SPSS functionality and shows how to segregate data, draw random samples, file split, and create variables automatically. It then goes on to cover different assumptions required in survey studies, and the importance of designing surveys in reporting the efficient findings. The book provides various parametric tests and the related assumptions and shows the procedures for testing these assumptions using SPSS software. To motivate readers to use assumptions, it includes many situations where violation of assumptions affects the findings. Assumptions required for different non-parametric tests such as Chi-square, Mann-Whitney, Kruskal Wallis, and Wilcoxon signed-rank test are also discussed. Finally, it looks at assumptions in non-parametric correlations, such as bi-serial correlation, tetrachoric correlation, and phi coefficient. An excellent reference for graduate students and research scholars of any discipline in testing assumptions of statistical tests before using them in their research study Shows readers the adverse effect of violating the assumptions on findings by means of various illustrations Describes different assumptions associated with different statistical tests commonly used by research scholars Contains examples using SPSS, which helps facilitate readers to understand the procedure involved in testing assumptions Looks at commonly used assumptions in statistical tests, such as z, t and F tests, ANOVA, correlation, and regression analysis Testing Statistical Assumptions in Research is a valuable resource for graduate students of any discipline who write thesis or dissertation for empirical studies in their course works, as well as for data analysts.
Features basic statistical concepts as a tool for thinking critically, wading through large quantities of information, and answering practical, everyday questions Written in an engaging and inviting manner, Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know presents the more subjective side of statistics—the art of data analytics. Each chapter explores a different question using fun, common sense examples that illustrate the concepts, methods, and applications of statistical techniques. Without going into the specifics of theorems, propositions, or formulas, the book effectively demonstrates statistics as a useful problem-solving tool. In addition, the author demonstrates how statistics is a tool for thinking critically, wading through large volumes of information, and answering life’s important questions. Beyond Basic Statistics: Tips, Tricks, and Techniques Every Data Analyst Should Know also features: Plentiful examples throughout aimed to strengthen readers’ understanding of the statistical concepts and methods A step-by-step approach to elementary statistical topics such as sampling, hypothesis tests, outlier detection, normality tests, robust statistics, and multiple regression A case study in each chapter that illustrates the use of the presented techniques Highlights of well-known shortcomings that can lead to false conclusions An introduction to advanced techniques such as validation and bootstrapping Featuring examples that are engaging and non-application specific, the book appeals to a broad audience of students and professionals alike, specifically students of undergraduate statistics, managers, medical professionals, and anyone who has to make decisions based on raw data or compiled results.
Utilizing the latest software, this book presents the essential statistical procedures for drawing valuable results from data in the social sciences. Mobilizing interesting real-world examples from the field of education, Understanding Educational Statistics Using Microsoft Excel and SPSS supplies a seamless presentation that identifies valuable connections between statistical applications and research design. Class-tested to ensure an accessible presentation, the book combines clear, step-by-step explanations and the use of software packages that are accessible to both the novice and professional alike to present the fundamental statistical practices for organizing, understanding, and drawing conclusions from educational research data. The book begines with an introduction to descriptive and inferential statistics and then proceeds to acquaint readers with the various functions for working with quantitative data in the Microsoft Excel environment, such as spreadsheet navigation; sorting and filtering; and creating pivot tables. Subsequent chapters treat the procedures that are commonly-employed when working with data across various fields of social science research, including: Single-sample tests Repeated measure tests Independent t-tests One way ANOVA and factorial ANOVA Correlation Bivariate regression Chi square Multiple regression Individual chapters are devoted to specific procedures, each ending with a lab exercise that highlights the importance of that procedure by posing a research question, examining the question through its application in Excel and SPSS, and concluding with a brief research report that outlines key findings drawn from the results. Real-world examples and data from modern educational research are used throughout the book, and a related Web site features additional data sets, examples, and labs, allowing readers to reinforce their comprehension of the material. Bridging traditional statistical topics with the latest software and applications in the field of education, Understanding Educational Statistics Using Microsoft Excel and SPSS is an excellent book for courses on educational research methods and introductory statistics in the social sciences at the upper-undergraduate and graduate levels. It also serves as a valuable resource for researchers and practitioners in the fields of education, psychology, and the social sciences who require a statistical background to work with data in their everyday work.
This text provides the reader with a single book where they can find accounts of a number of up-to-date issues in nonparametric inference. The book is aimed at Masters or PhD level students in statistics, computer science, and engineering. It is also suitable for researchers who want to get up to speed quickly on modern nonparametric methods. It covers a wide range of topics including the bootstrap, the nonparametric delta method, nonparametric regression, density estimation, orthogonal function methods, minimax estimation, nonparametric confidence sets, and wavelets. The book’s dual approach includes a mixture of methodology and theory.
