# STATISTICAL METHODS II

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## Details

• Language English

• Edition 4th

• Publish Year 2013

• Book Format Printed

• Publishers Vision Publication

1. Detailed Review/ Revision of Theory of Probability
1. Counting Techniques
2. Deterministic and non Deterministic Model
3. Sample Spaces
4. Events
5. Probability
6. Theorems of Probability
Examples
1. Conditional Probability
2. Bayes’ Theorem
3. Definition of Sensitivity of a Procedure, Specificity of Procedure
4. Independence of Two Events
3. Continuous Random Variable
1. Introduction
2. Continuous Type Random Variables
3. Probability Density Function (P.D.F.)
4. Distribution Function (D.F.) or Cumulative Distribution Function (C.D.F.)
4. Standard Continuous Probability Distributions
1. Introduction
2. Uniform Distribution
3. Exponential Distribution
4. Normal Distribution
5. Pareto Distribution
5. Concepts and Definitions Related to Testing of Hypothesis
1. Introduction
2. Population and Random Sample
3. Parameter and Statistic
4. Sampling Distribution of a Statistic
5. Standard Error (S.E.) of a Statistic
6. Statistical Hypothesis
7. Tests of Significance
8. Null Hypothesis and Alternative Hypothesis
9. Errors of Sampling (Type I and II errors)
10.Critical Region
11.Level of Significance (l.o.s.)
12.One Sided and Two Sided Tests
13.Procedure for Testing of Hypothesis
14.P-value (Descriptive Level)
6. Large Sample Tests
1. Introduction
2. Test of Significance for Single Population Mean
3. Test of Significance for Difference of Means
4. Tests for Single Proportion
5. Test of Significance for Difference of Proportions
7. Tests Based on t-Distribution
1. Introduction
2. t - test for Single Mean
3. t-test for Equality of Two Population Means
4. Paired t-Test
5. Test of Significance of Correlation Coefficient for Bivariate Raw Data
6. Test of Significance of Regression Coefficients for Bivariate Data
8.Tests Based on Chi-Square Distribution
1. Introduction
2. Chi-Square Test for Goodness of Fit
3. Chi - Square Test for Independence of Attributes
4. Test for Significance of Variation for a Population

9. Non-Parametric Tests
1. Introduction
2. Run Test
3. Sign Test
4. Kolmogrov-Smirnov Test
5. Mann-Whitney Test
10. Simulation
1. Introduction
2. Simulation
3. Random Number Generation
4. Testing Requirements of a Good Random Number Generator Using Various Tests
5. Model Sampling from Uniform and Exponential Distributions
6. Model Sampling from Normal Distribution using Box–Muller Transformation

• Stream Science

• Branch Computer Science

• Standard/Year Firstyear

• Medium English

• Board/University Pune

• Subject Statistics

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