Fundamentals Of Applied Statistics By Gupta And Kapoor 13.pdfl: A Comprehensive Guide
If you are looking for a book that covers the basics of applied statistics in a clear and concise manner, then you might want to check out Fundamentals Of Applied Statistics By Gupta And Kapoor 13.pdfl. This book is written by two renowned authors, S. C. Gupta and V. K. Kapoor, who have decades of experience in teaching and research in statistics. The book is divided into 13 chapters, each covering a different topic of applied statistics, such as probability distributions, sampling theory, estimation, testing of hypotheses, analysis of variance, regression and correlation, design of experiments, non-parametric methods, and more. The book also includes numerous solved examples, exercises, and tables to help the readers understand and apply the concepts.
In this article, we will provide you with a comprehensive guide on what you can expect from Fundamentals Of Applied Statistics By Gupta And Kapoor 13.pdfl, how you can download it for free, and why you should read it if you want to learn more about applied statistics.
What You Can Expect From Fundamentals Of Applied Statistics By Gupta And Kapoor 13.pdfl
Fundamentals Of Applied Statistics By Gupta And Kapoor 13.pdfl is a book that aims to provide a solid foundation of applied statistics to students, researchers, and practitioners of various disciplines. The book covers the following topics:
Chapter 1: Introduction: This chapter introduces the basic concepts and terminology of statistics, such as population, sample, parameter, statistic, variable, data, etc. It also explains the difference between descriptive and inferential statistics, and the types of data (qualitative and quantitative).
Chapter 2: Probability: This chapter deals with the theory of probability, such as events, sample space, axioms of probability, conditional probability, Bayes' theorem, etc. It also introduces some important probability distributions, such as binomial, Poisson, normal, etc.
Chapter 3: Random Variables And Probability Distributions: This chapter expands on the concept of random variables and their properties, such as expectation, variance, moment generating function, etc. It also discusses some more probability distributions, such as uniform, exponential, gamma, beta, etc.
Chapter 4: Sampling Theory: This chapter explains the concept and methods of sampling from a population, such as simple random sampling, stratified sampling, systematic sampling, cluster sampling, etc. It also discusses the sampling distribution of various statistics, such as mean, proportion, variance, etc.
Chapter 5: Estimation: This chapter deals with the problem of estimating an unknown population parameter from a sample statistic. It covers the concepts of point estimation and interval estimation,
and the criteria for choosing a good estimator. It also introduces some common estimators,
such as maximum likelihood estimator (MLE), method of moments estimator (MME), etc.
Chapter 6: Testing Of Hypotheses: This chapter deals with the problem of testing a claim or an assumption about a population parameter based on a sample statistic. It covers the concepts of null hypothesis and alternative hypothesis,
type I error and type II error,
level of significance and power of test,
p-value and confidence interval,
and some common tests,
such as z-test,
Chapter 7: Analysis Of Variance: This chapter deals with the problem of comparing the means of more than two populations based on samples from each population. It covers the concepts of one-way ANOVA,
and factorial ANOVA,
and their assumptions and applications.
Chapter 8: Regression And Correlation: This chapter deals with the problem of studying the relationship between two or more variables based on data. It covers the concepts of simple linear regression,
multiple linear regression,
and nonlinear regression,
and their methods of estimation and testing. It also covers the concepts of correlation coefficient,