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Small Sampling Theory
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Introduction
t - Distribution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Properties of t-Distribution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Steps involved in testing of hypothesis.  ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Applications of  t  - distribution ,[object Object],[object Object],[object Object],[object Object]
1. Test of Hypothesis about the population mean( σ  unknown and small sample size ) ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Test of hypothesis about the difference between two means ,[object Object],[object Object],[object Object],[object Object]
(a) Case of equal variances ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
(b) Case of unequal variances ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Confidence interval for the difference between two means Two samples of sizes n 1  and n 2  are randomly and independently drawn from two normally distributed populations with unknowns but equal variances. The 100(1- α )% confidence interval for µ 1  - µ 2  is given by:
(3) Test of hypothesis about the difference between two means with dependent samples (paired t-test) ,[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
(4) Testing of hypothesis about coefficient of correlation. ,[object Object],[object Object],[object Object]
Case 1: testing the hypothesis when the population coefficient of correlation equals zero, i.e., H o  :  ρ =0 ,[object Object],[object Object],[object Object],[object Object],[object Object]
Case 2:  testing the hypothesis when the population coefficient of correlation equals some other value than zero, i.e., H o :  ρ =  ρ o ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
[object Object],[object Object],[object Object]
Case 3: testing the hypothesis for the difference between two independent correlation coefficients ,[object Object],[object Object],[object Object]
The F - Distribution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Probability density function ,[object Object]
Properties of F-distribution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Testing of hypothesis for equality of two variances ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Confidence interval for the ratio of two variances ,[object Object],[object Object],[object Object]

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Small Sampling Theory Presentation1

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  • 11. Confidence interval for the difference between two means Two samples of sizes n 1 and n 2 are randomly and independently drawn from two normally distributed populations with unknowns but equal variances. The 100(1- α )% confidence interval for µ 1 - µ 2 is given by:
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