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1. Sample Theory
1.1 Introduction
1.2 Population and Sample
1.3 Sampling
1.4 Parameter and Statistic
1.5 Tests of Significance
1.6 Formation of a Hypothesis 
1.7 Two-Tailed and Single Tailed Tests
1.8 Level of Significance
1.9 Critical Region
1.10 General Procedure for Hypothesis Testing
1.11 P-Value
1.12 Alternate Approach of Testing Hypothesis
1.13 Errors in Testing of Hypothesis
1.14 Power of a Hypothesis Test
1.15 Normal Test (Large Sample Significance Test)

2. Sampling Distributions 
2.1 Introduction 
2.2 Chi-square (x²) Distribution
2.3 The t-Distribution
2.4 F-Distribution

3. Estimation Theory-I: Point Estimation
3.1 Introduction
3.2 Point Estimation
3.3 Unbiasedness
3.4 Consistency
3.5 Efficiency
3.6 Sufficiency
3.7 Completeness
3.8 Rao-Blackwell Theorem
3.9 Cramer-Rao Inequality 
3.10 Methods of Estimation 

4. Estimation Theory-II: Interval Estimation 
4.1 Introduction
4.2 Interval Estimation
4.3 Confidence Interval for Means 
4.4 Confidence Interval for Difference of Means
4.5 Confidence Interval for Variance

5. Testing Hypothesis-I: Exact Sample Tests
5.1 Introduction
5.2 x2- test
5.3 t-test
5.4 F-test

6. Analysis of Variance
6.1 Introduction
6.2 Analysis of Variance (ANOVA)

7. Testing Hypothesis-II
7.1 Introduction 
7.2 Fundamental Concepts 
7.3 Classical Theory of Testing Hypothesis
7.4 The Neyman-Pearson Lemma
7.5 Likelihood Ratio Tests

8. Non-Parametric Tests
8.1 Introduction
8.2 Difference Between Parametric and Non-parametric Tests
8.3 The Sign Test
8.4 Wilcoxon Signed Rank Test 
8.5 Mann-Whitney Test

Appendix (I-IV)
Table (I-VIII)
Bibliograph
MATH 681S: Probability and Mathematical Statistics-II
Total Marks : 100
Theory : 80 Marks
Internal Assessment : 20 Marks
Time : 3 hrs.

Note: 1. The question paper will consist of 9 questions. Candidates will attempt total five questions.
2. Question No.1 is compulsory and will consist of short answer type questions covering the whole syllabus.
3. There will be four questions from each Unit and the candidates will be required to attempt two questions from each Unit.
4. All questions carry equal marks.

UNIT-I
Point and Interval Estimation: General concept of Point estimation, unbiasedness, consistency, efficiency and Sufficiency. Factorization theorem, completeness, Rao-Blackwell theorem, Cramer-Rao inequality.Maximum likelihood method of estimation and method of moments. Interval estimation, confidence intervals for means, difference of means and variances.

UNIT–II
Hypothesis Testing: The basic idea of significance test. Null and alternative hypothesis, Type-I and TypeII errors. Uniformly most powerful tests, Likelihood Ratio tests. t, Chi-square and F-distributions. Tests of significance based on t, Chi-square and F. One way and two way Analysis of Variance (ANOVA).
Non-Parametric Tests: Sign test, Wilcoxon signed rank test, Mann-whitney test. 

Spectrum Elements of Probability and Mathematical Statistics Volume-II for M.SC 2nd PU (English Medium) is an authoritative book authored by Dr. Bhanu Gupta and published by Sharma Publications Jalandhar. Designed specifically for M.SC 2nd-year students, this book provides an in-depth exploration of advanced statistical concepts and methodologies, making it an indispensable resource for mastering probability and mathematical statistics.

Comprehensive Coverage of Advanced Topics
The book is structured to cover a wide range of topics essential for M.SC 2nd PU students. It begins with Sampling Theory, introducing fundamental concepts such as population, sample, parameter, and statistic. It further elaborates on hypothesis testing, including the formation of hypotheses, types of tests (two-tailed and single-tailed), levels of significance, and critical regions. The chapter also discusses errors in hypothesis testing, the power of a test, and the normal test for large samples, providing a solid foundation for understanding statistical inference.

The second chapter focuses on Sampling Distributions, detailing key distributions such as the Chi-square (x²) distribution, t-distribution, and F-distribution. These distributions are crucial for various statistical tests and analyses, and the book explains their properties and applications with clarity.

Estimation Theory: Point and Interval Estimation
The book dedicates two chapters to Estimation Theory, dividing it into Point Estimation and Interval Estimation. In Point Estimation, students will learn about unbiasedness, consistency, efficiency, sufficiency, and completeness of estimators. The chapter also covers advanced topics like the Rao-Blackwell Theorem and the Cramer-Rao Inequality, along with various methods of estimation. Interval Estimation, on the other hand, provides a detailed understanding of confidence intervals for means, differences of means, and variances, equipping students with the tools to estimate population parameters with precision.

Hypothesis Testing: Exact Sample Tests and Advanced Concepts
The book extensively covers Hypothesis Testing across two chapters. The first chapter on hypothesis testing focuses on exact sample tests, including the x²-test, t-test, and F-test, which are fundamental for analyzing sample data. The second chapter delves into advanced concepts such as the classical theory of hypothesis testing, the Neyman-Pearson Lemma, and likelihood ratio tests. These topics are essential for students aiming to develop a deeper understanding of statistical decision-making.

Analysis of Variance (ANOVA)
A dedicated chapter on Analysis of Variance (ANOVA) introduces students to the techniques used to analyze the differences among group means. This chapter is particularly useful for students involved in research or experimental studies, as it provides a systematic approach to understanding variance and its implications.

Non-Parametric Tests
The book also includes a chapter on Non-Parametric Tests, which are used when the assumptions of parametric tests are not met. It covers the differences between parametric and non-parametric tests, along with specific tests such as the Sign Test, Wilcoxon Signed Rank Test, and Mann-Whitney Test. These tests are invaluable for analyzing data that do not follow a normal distribution.

Appendices and Tables
To aid in practical applications, the book features an Appendix with supplementary materials and Tables (I-VIII) that provide critical values for various statistical tests. These resources are designed to assist students in performing statistical analyses and interpreting results accurately.

Key Features
1. Tailored for M.SC 2nd PU Students: The content is specifically designed to meet the academic requirements of M.SC 2nd-year students.
2. Clear and Concise Explanations: Complex statistical concepts are explained in a straightforward manner, making them accessible to students.
3. Practical Examples: The book includes numerous examples to illustrate the application of statistical methods.
4. Comprehensive Coverage: From basic sampling theory to advanced hypothesis testing, the book covers all essential topics in probability and mathematical statistics.
5. Useful Appendices and Tables: The inclusion of appendix tables enhances the book's utility for both students and educators.

Why Choose This Book?
Spectrum Elements of Probability and Mathematical Statistics Volume-II for M.SC 2nd PU (English Medium) is an ideal choice for students seeking a thorough understanding of advanced statistical concepts. Its structured approach, detailed explanations, and practical examples make it a valuable resource for academic success. Whether you are preparing for exams or conducting research, this book will serve as a reliable guide throughout your M.SC 2nd PU journey.

1. Sample Theory
1.1 Introduction
1.2 Population and Sample
1.3 Sampling
1.4 Parameter and Statistic
1.5 Tests of Significance
1.6 Formation of a Hypothesis 
1.7 Two-Tailed and Single Tailed Tests
1.8 Level of Significance
1.9 Critical Region
1.10 General Procedure for Hypothesis Testing
1.11 P-Value
1.12 Alternate Approach of Testing Hypothesis
1.13 Errors in Testing of Hypothesis
1.14 Power of a Hypothesis Test
1.15 Normal Test (Large Sample Significance Test)

2. Sampling Distributions 
2.1 Introduction 
2.2 Chi-square (x²) Distribution
2.3 The t-Distribution
2.4 F-Distribution

3. Estimation Theory-I: Point Estimation
3.1 Introduction
3.2 Point Estimation
3.3 Unbiasedness
3.4 Consistency
3.5 Efficiency
3.6 Sufficiency
3.7 Completeness
3.8 Rao-Blackwell Theorem
3.9 Cramer-Rao Inequality 
3.10 Methods of Estimation 

4. Estimation Theory-II: Interval Estimation 
4.1 Introduction
4.2 Interval Estimation
4.3 Confidence Interval for Means 
4.4 Confidence Interval for Difference of Means
4.5 Confidence Interval for Variance

5. Testing Hypothesis-I: Exact Sample Tests
5.1 Introduction
5.2 x2- test
5.3 t-test
5.4 F-test

6. Analysis of Variance
6.1 Introduction
6.2 Analysis of Variance (ANOVA)

7. Testing Hypothesis-II
7.1 Introduction 
7.2 Fundamental Concepts 
7.3 Classical Theory of Testing Hypothesis
7.4 The Neyman-Pearson Lemma
7.5 Likelihood Ratio Tests

8. Non-Parametric Tests
8.1 Introduction
8.2 Difference Between Parametric and Non-parametric Tests
8.3 The Sign Test
8.4 Wilcoxon Signed Rank Test 
8.5 Mann-Whitney Test

Appendix (I-IV)
Table (I-VIII)
Bibliograph

Have Doubts Regarding This Product ? Ask Your Question

MATH 681S: Probability and Mathematical Statistics-II
Total Marks : 100
Theory : 80 Marks
Internal Assessment : 20 Marks
Time : 3 hrs.

Note: 1. The question paper will consist of 9 questions. Candidates will attempt total five questions.
2. Question No.1 is compulsory and will consist of short answer type questions covering the whole syllabus.
3. There will be four questions from each Unit and the candidates will be required to attempt two questions from each Unit.
4. All questions carry equal marks.

UNIT-I
Point and Interval Estimation: General concept of Point estimation, unbiasedness, consistency, efficiency and Sufficiency. Factorization theorem, completeness, Rao-Blackwell theorem, Cramer-Rao inequality.Maximum likelihood method of estimation and method of moments. Interval estimation, confidence intervals for means, difference of means and variances.

UNIT–II
Hypothesis Testing: The basic idea of significance test. Null and alternative hypothesis, Type-I and TypeII errors. Uniformly most powerful tests, Likelihood Ratio tests. t, Chi-square and F-distributions. Tests of significance based on t, Chi-square and F. One way and two way Analysis of Variance (ANOVA).
Non-Parametric Tests: Sign test, Wilcoxon signed rank test, Mann-whitney test. 

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