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To maximize the impact of any piece of statistical work, it is important to tailor it to the right group. What kind of audience is your work aimed towards? For example, textbooks that are intended for students benefit from sections with problems and answers.
The analysis of statistics in business for better decision making is nowadays called Big Data Analytics."Big data analytics refers to the process of collecting, organizing and analyzing large sets of data (called big data) to discover patterns and other useful information. Big data analytics can help organizations to better understand the information contained within the data and will also help identify the data that is most important to the business and future business decisions. Analysts working with big data basically want the knowledge that comes from analyzing the data."The purpose of this textbook is to present an introduction to the BUSINESS STATISTICS & ANALYTICS FOR DECISION MAKING subject of Management & Commerce. The book contains the syllabus from basics of the subjects going into the intricacies of the subjects. All the concepts have been explained with relevant Numerals, examples and diagrams to make it interesting for the readers.An attempt is made here by the experts to assist the students by way of providing Study Material as per the curriculum with non-commercial considerations. However, it is implicit that these are exam-oriented Study Material and students are advised to attend regular lectures in the Institute and utilize reference books available in the library for In-depth knowledge.We owe to many websites and their free contents; we would like to specially acknowledge contents of website www.wikipedia.com and various authors whose writings formed the basis for this book. We acknowledge our thanks to them. At the end we would like to say that there is always a room for improvement in whatever we do. We would appreciate any suggestions regarding this study material from the readers so that the contents can be made more interesting and meaningful. Readers can email their queries and doubts to our authors on [email protected]. We shall be glad to help you immediately. Authors: Dr Mukul Burghate and Dr Padmakar Shahare
This new edition of one of the most widely read textbooks in its field introduces the reader to data analysis with the most powerful and versatile statistical package on the market: IBM SPSS Statistics 19. Each new release of SPSS Statistics features new options and other improvements. There remains a core of fundamental operating principles and techniques which have continued to apply to all releases issued in recent years and have been proved to be worth communicating in a small volume. This practical and informal book combines simplicity and clarity of presentation with a comprehensive treatment of the use of IBM SPSS Statistics 19 for the description, exploration and confirmation of data. As in earlier editions, coverage has been extended to address the issues raised by readers since the previous edition. In this edition, there is an introduction to the Analysis of Covariance (ANCOVA). Each statistical technique is presented in a realistic research context and is fully illustrated with annotated screen shots of SPSS dialog boxes and output. The first chapter sets the scene with a survey of typical research situations, key terms and clear signposts to the location of each technique in the book. It also offers guidance on the choice of statistical techniques, and advice (based on the American Psychological Association’s guidelines) on how to report the results of a statistical analysis. The next chapters introduce the reader to the use of SPSS, beginning with the entry, description and exploration of data. There is also a full description of the capabilities of the versatile Chart Builder. Each of the remaining chapters concentrates on one particular kind of research situation and the statistical techniques that are appropriate. In summary, IBM SPSS Statistics 19 Made Simple Gets you started with SPSS. Shows you how to describe and explore a data set with the help of SPSS’s extensive graphics and data-handling menus. Helps you to choose appropriate statistical techniques. Warns you of pitfalls arising from the misuse of statistics. Shows you how to report the results of a statistical analysis. Shows you how to use Syntax to implement some useful procedures and operations. Introduces the reader to the analysis of covariance (ANCOVA) Has a comprehensive glossary. Is now presented in an attractive two-colour format. The book’s accompanying website contains datasets for the chapters of the book, as well as a large body of exercises (with data sets), and notes on statistical terms. Instructor resources include a PowerPoint lecture course and Multiple-Choice Question tests, which are also available free of charge to lecturers adopting the book and their students. Please visit http://www.psypress.com/spss-made-simple for more details.
Applied Univariate, Bivariate, and Multivariate Statistics Using Python A practical, “how-to” reference for anyone performing essential statistical analyses and data management tasks in Python Applied Univariate, Bivariate, and Multivariate Statistics Using Python delivers a comprehensive introduction to a wide range of statistical methods performed using Python in a single, one-stop reference. The book contains user-friendly guidance and instructions on using Python to run a variety of statistical procedures without getting bogged down in unnecessary theory. Throughout, the author emphasizes a set of computational tools used in the discovery of empirical patterns, as well as several popular statistical analyses and data management tasks that can be immediately applied. Most of the datasets used in the book are small enough to be easily entered into Python manually, though they can also be downloaded for free from www.datapsyc.com. Only minimal knowledge of statistics is assumed, making the book perfect for those seeking an easily accessible toolkit for statistical analysis with Python. Applied Univariate, Bivariate, and Multivariate Statistics Using Python represents the fastest way to learn how to analyze data with Python. Readers will also benefit from the inclusion of: A review of essential statistical principles, including types of data, measurement, significance tests, significance levels, and type I and type II errors An introduction to Python, exploring how to communicate with Python A treatment of exploratory data analysis, basic statistics and visual displays, including frequencies and descriptives, q-q plots, box-and-whisker plots, and data management An introduction to topics such as ANOVA, MANOVA and discriminant analysis, regression, principal components analysis, factor analysis, cluster analysis, among others, exploring the nature of what these techniques can vs. cannot do on a methodological level Perfect for undergraduate and graduate students in the social, behavioral, and natural sciences, Applied Univariate, Bivariate, and Multivariate Statistics Using Python will also earn a place in the libraries of researchers and data analysts seeking a quick go-to resource for univariate, bivariate, and multivariate analysis in Python.
Computational analysis of natural science experiments often confronts noisy data due to natural variability in environment or measurement. Drawing conclusions in the face of such noise entails a statistical analysis. Parametric statistical methods assume that the data is a sample from a population that can be characterized by a specific distribution (e.g., a normal distribution). When the assumption is true, parametric approaches can lead to high confidence predictions. However, in many cases particular distribution assumptions do not hold. In that case, assuming a distribution may yield false conclusions. The companion book Statistics is Easy! gave a (nearly) equation-free introduction to nonparametric (i.e., no distribution assumption) statistical methods. The present book applies data preparation, machine learning, and nonparametric statistics to three quite different life science datasets. We provide the code as applied to each dataset in both R and Python 3. We also include exercises for self-study or classroom use.
An excellent book for commerce students appearing in competitive, professional and other examinations. Business Statistics 1.Statistics : Concept, Nature and Limitations, 2.Statistics : Scope and Significance, 3.Types and Collection of Data, 4. Classification and Tabulation of Data, 5. Frequency Distribution, 6. Graphic Presentation of Data, 7. Measures of Central Tendency (Mean, Median, Mode), 8. Measures of Variation or Dispersion (Rang, Q. D., M. D. & S. D.), 9. Measures of Skewness, 10. Measures of Kurtosis, 11. Correlation, 12. Regression Analysis, 13. Probability Theory, 14. Probability Distributions (Binomial, Poisson and Normal), 15. Sampling Theory and Tests of Significance. 16. Appendix. SYLLABUS Unit I : Statistics : Concept, Significance & Limitation, Type of Data, Classification & Tabulation, Frequency Distribution & Graphical Representation. Unit II : Measures of Central Tendency (Mean, Median, Mode), Measures of Variation : Significance & Properties of a Good Measure of Variation : Range, Quartile Deviation, Mean Deviation and Standard Deviation, Measures of Skewness & Kurtosis. Unit III :Correlation : Significance of Correlation, Types of correlation, Simple Correlation, Scatter Diagram Method, Karl Pearson Coefficient of Correlation. Regression : Introduction, Regression Lines, Regression Equation & Regression Coefficient. Unit IV :Probability : Concept, Events, Addition Law, Conditional Probability, Multiplication Law & Baye’s Theorem [Simple Numerical], Probability Distribution : Binomial, Poisson and Normal. Unit V : Sampling Method of Sampling, Sampling and Non-Sampling Errors. Test of Hypothesis, Type-I and Type-II Errors, Large Sample Tests.
It’s not too late to learn new math tricks—and help kids learn them, too! Teaching Your Kids New Math, Grades 6-8, For Dummies teaches you the new standard way of teaching kids math. It’s all about thinking through how to solve problems and using strategies, rather than just memorizing the procedures. In this book, parents, guardians, and tutors will learn how to use these methods and standards to effectively teach kids Common Core math for grades 6-8. Teaching Your Kids New Math, Grades 6-8, For Dummies shows you how schools are teaching kids math these days, and gives you tools to support kids through the homework and test prep process. You’ll love this book’s clear explanations and examples organized by grade level. With Teaching Your Kids New Math, Grades 6-8, For Dummies?? you’ll also get access to online tools, including dozens of math worksheets for additional support. Learn how to teach 6th through 8th grade math according to the Common Core Discover the new methods and formulas that are standard for math instruction Get best teaching practices, example problems, and tips about common math pitfalls Help your kids with math homework and enhance the homeschool journey This is the perfect Dummies guide for anyone who needs guidance on how to teach kids math using new methods and concepts—they’re different from what we learned in school! Future math teachers will also love this user-friendly guide to middle-grade math.
