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Step-by-Step Business Math and Statistics is written to help those who need a quick refresher on mathematics and statistics as the foundation of a rigorous MBA program. This book fills the gap left by many textbooks that are often dedicated to either mathematics or statistics, but not both. It also serves as both a textbook that describes basic concepts and a workbook that shows plenty of examples and exercise problems. This book covers only the most fundamental topics in business mathematics and statistics and truly lays down the basic concepts step by step. Step-by-Step Business Math and Statistics covers the essentials of mathematics and statistics, including: - Algebra Review - Calculus Review - Optimization Methods - Applications to Economics - Data Collection Methods - Probability Theory - Sampling Distributions - Multiple Regression Analysis Jin Choi is Associate Professor of Economics in the Kellstadt Graduate School of Business at DePaul University (Chicago, Illinois). He specializes in teaching quantitative topics such as business mathematics, statistics, forecasting, and quantitative investment analysis. He also teaches topics on money and banking and serves as a member of the board of directors of a $555 million community bank in Chicago. He received the Excellence in Teaching award in 2007 from DePaul University and emphasizes practical use of theory in his teaching.
This book is useful for B.Com, B.A., B.B.A., B.C.A., B.B.M., etc. of all universities in Maharashtra. The book has been written in simple and lucid manner to make the subject matter easy to understand. An ample number of practical problems under both solution and exercise section has been given for practice to the students.
Mathematical Statistics for Economics and Business, Second Edition, provides a comprehensive introduction to the principles of mathematical statistics which underpin statistical analyses in the fields of economics, business, and econometrics. The selection of topics in this textbook is designed to provide students with a conceptual foundation that will facilitate a substantial understanding of statistical applications in these subjects. This new edition has been updated throughout and now also includes a downloadable Student Answer Manual containing detailed solutions to half of the over 300 end-of-chapter problems. After introducing the concepts of probability, random variables, and probability density functions, the author develops the key concepts of mathematical statistics, most notably: expectation, sampling, asymptotics, and the main families of distributions. The latter half of the book is then devoted to the theories of estimation and hypothesis testing with associated examples and problems that indicate their wide applicability in economics and business. Features of the new edition include: a reorganization of topic flow and presentation to facilitate reading and understanding; inclusion of additional topics of relevance to statistics and econometric applications; a more streamlined and simple-to-understand notation for multiple integration and multiple summation over general sets or vector arguments; updated examples; new end-of-chapter problems; a solution manual for students; a comprehensive answer manual for instructors; and a theorem and definition map. This book has evolved from numerous graduate courses in mathematical statistics and econometrics taught by the author, and will be ideal for students beginning graduate study as well as for advanced undergraduates.
1. Averages, 2. Ratio, 3. Proportion, 4. Percentage, 5. Profit and Loss, 6. Simple Interest, 7. Compound Interest, 8. Annuities, 9. True Discount and Banker’s Discount, 10. Basic Concepts of Set Theory, 11. Simultaneous Equations, 12. Quadratic Equations (In One Variable Inequalities), 13. Linear Programming (Two Variable).
Text Book For Ca, Icwa And Cs (Foundation And Professional Education Course 1) # Ms 8 And Ms 95 Courses Of Ignou # Bca, Mca, Mba # B.Com, M.Com, Business Mathematics And Statistics For Ca, Icwa And Cs (Foundation And Pe Course 1) Spread Over In More Than Fourty Chapter And Four-Figure Pages In The Twin Sections, The Textbook Encompasses Tailormade Topics Of The Three Professional Bodies - Ca, Icwa And Cs. Content Highlights : - Preface - Mathematics # Averages # Percentage, Mixtures, Ratio & Proportion And Variation # Mathematics Of Finance # Indices And Surds # Number System # Theory Of Equations # Logarithms # Elements Of Set Theory # Truth Table & Its Applicartions To Statements # Determinants # Linear Quadratic, Exponential & Logarithmic Functions : Concept Of Breakeven Point # Progression # Permutations And Combinations # Matrics # Graph Of Inequalities, Linear Programming Techniques - Basics # Vector Algebra # Functions, Limits And Continuity # Differentiation # Successive Differentiation # Application Of Differential Coefficients # Maxima And Minima # Integration : Rules Of Integration # Definite Integrals # Application Of Integral Calculas # Plane Analytical Geometry (Co-Ordinate Geometry) # MensurationStatistics : # Quantative Techniques : Meaning, Scope And Limitations Of Statistics, Collection And Presentation Of Statistical Data # Measure Of Central Tendency # Measures Of Dispersion, Skewness And Kurtosis # Correlation Analysis # Regression Analysis # Probability And Expected Value # Binomial Distribution # Poission Distribution # Normal Distribution # Theory Of Sampling And Test Of Hypothesis # Chi-Square Test # Fisher'S Transformation, F-Test And Analysis Of Variance # Time Series Analysis And Forecasting # Index Numbers # Model Test Paper # Appendices.
Taken literally, the title "All of Statistics" is an exaggeration. But in spirit, the title is apt, as the book does cover a much broader range of topics than a typical introductory book on mathematical statistics. This book is for people who want to learn probability and statistics quickly. It is suitable for graduate or advanced undergraduate students in computer science, mathematics, statistics, and related disciplines. The book includes modern topics like non-parametric curve estimation, bootstrapping, and classification, topics that are usually relegated to follow-up courses. The reader is presumed to know calculus and a little linear algebra. No previous knowledge of probability and statistics is required. Statistics, data mining, and machine learning are all concerned with collecting and analysing data.
Intended as the text for a sequence of advanced courses, this book covers major topics in theoretical statistics in a concise and rigorous fashion. The discussion assumes a background in advanced calculus, linear algebra, probability, and some analysis and topology. Measure theory is used, but the notation and basic results needed are presented in an initial chapter on probability, so prior knowledge of these topics is not essential. The presentation is designed to expose students to as many of the central ideas and topics in the discipline as possible, balancing various approaches to inference as well as exact, numerical, and large sample methods. Moving beyond more standard material, the book includes chapters introducing bootstrap methods, nonparametric regression, equivariant estimation, empirical Bayes, and sequential design and analysis. The book has a rich collection of exercises. Several of them illustrate how the theory developed in the book may be used in various applications. Solutions to many of the exercises are included in an appendix.