Statistics For Business And Economics, 2Ed by Dr. Seema Sharma, Wiley India

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Wiley India Statistics For Business And Economics, 2Ed by Dr. Seema Sharma

Statistical analysis is inevitable for decision making in business, economics, and other fields of scientific enquiry. Strategies for optimum utilization of resources need to be backed by appropriate analysis. Also, insightful statistical analysis is becoming increasingly important in today's world of information load. This book is an attempt to present the understanding of the statistical tools in an easy, interesting, and useful manner and to equip the readers with knowledge of what, why, and how to use the wide-ranging statistical tools. The book not only focuses on the conceptual understanding of the statistical methods, but also provides sufficient exposure through case studies along with solved examples. About the Author Dr. Seema Sharma is a faculty of Economics and Statistics in the Department of Management Studies, Indian Institute of Technology (IIT), New Delhi, India. Her research interests are Sustainable Development, Productivity and Efficiency Analysis, Renewable Energy Policy Analysis, and Socio-economic Analysis. Her research has been published in international and national refereed journals, such as Energy Policy (Elsevier), Renewable Energy (Elsevier), Renewable and Sustainable Energy Reviews (Elsevier), Scientometrics (Springer), Journal of Advances in Management Research (Emerald), Indian Economic Journal (Indian Economic Society), Economic and Political weekly, to name a few. TABLE OF CONTENTS Preface About the Author Acknowledgments Chapter 1: Introduction to Statistics Introduction Statistics: Definition Types of Data Statistical Software for Analysis: Microsoft Excel and IBM SPSS Primary Data and Secondary Data 10 Applications of Statistics in Business and Economics Presenting Data in Business The Numeric Data Continuous Frequency Distribution Class Interval Graphic Presentation of Data Graphic Presentation of Categorical Data Cross-Tabulation Summary Key Terms Multiple-Choice Questions Assignments Case Study 1 Case Study 2 Chapter 2: Descriptive Statistical Analysis Introduction Measures of Central Tendency Measures of Dispersion Measure of Skewness Symmetrical Distribution Kurtosis Descriptive Statistics Using Excel Summary Key Terms Multiple-Choice Questions Assignments Case Study 1 Chapter 3: Probability Theory and Distributions Probability Theory Fundamental Concepts of Probability Definitions of Probability Laws of Probability Bayes Theorem Random Variable Probability Distribution Theoretical Probability Distribution Summary Multiple-Choice Questions Assignments Case Study 1 Chapter 4: Sampling Theory and Sampling Distribution Introduction to Sampling Theory Probabilistic Sampling Techniques Sampling Error and Non-Sampling Error Central Limit Theorem Summary Multiple-Choice Questions Assignments Case Study 1 Chapter 5: Hypothesis Testing—Large Sample Tests Introduction to Hypothesis Testing Null Hypothesis (H0) Hypothesis Testing for Large Samples Test for Single Proportion Test for Difference of Proportions Summary Multiple-Choice Questions Assignments Case Study 1 Case Study 2 Chapter 6: Hypothesis Testing—Small Sample Tests Introduction t-Test Applications of t-Test Summary Multiple-Choice Questions Assignments Case Study 1 Chapter 7: Analysis of Variance Introduction to Analysis of Variance Two-Way Factorial ANOVA Design with SPSS Multivariate Analysis of Variance Summary Multiple-Choice Questions Assignments Chapter 8: Chi-Square Test and Its Applications Introduction Applications of the c2 Test Testing the Goodness of Fit Multiple-Choice Questions Assignments Chapter 9: Nonparametric Test Introduction Testing the Normality of Data Sign Test Wilcoxon–Mann–Whitney Test Wilcoxon Signed Rank Test The Kruskal–Wallis (H-Test) Summary Multiple-Choice Questions Assignments Chapter 10: Correlation Analysis Introduction Types of Correlation Simple Correlation Partial Correlation Multiple Correlation Chapter 11: Regression Analysis Introduction to Regression Analysis The Simple Regression Model Multiple Regression Analysis Stepwise Regression Analysis Multicollinearity Summary Multiple-Choice Questions Assignments Case Study 1 Chapter 12: Logistic Regression Analysis Introduction Assumptions of Binary Logistic Regression Multinomial Logistic Regression Logistic Regression vs. Discriminant Analysis Summary Multiple-Choice Questions Assignments Case Study 1 Chapter 13: Business Forecasting Introduction Qualitative Forecasting Methods Quantitative Forecasting Methods Forecast Accuracy Smoothing Techniques Analysis of Seasonal Variations References Summary Multiple-Choice Questions Assignments Chapter 14: Statistical Quality Control Introduction Few Basic Concepts Control Charts Control Charts for Attribute Data Process Capability Summary Multiple-Choice Questions Assignments Case Study 1 Chapter 15: Decision Analysis Introduction Payoff Table Decision Making Under Certainty Decision Making Under Uncertainty Decision Making Under Risk Decision Making with Revised Probabilities Decision Trees Summary Multiple-Choice Questions Assignments Chapter 16: Index Numbers Introduction Construction of Index Numbers Popular Indexes in India Summary Multiple-Choice Questions Assignments Appendix: Statistical Tables Index