Answer:
Inference for Regression
Step-by-step explanation:
Let us try to understand the difference between each of them.
1) Two sample t- test : the following assumptions must be used while applying the t- tests.
- The samples of n observations X₁,X₂,X₃,...........X n is selected randomly.
- The population from which the small sample is drawn is normal.This is essential for X` and s , the two components of the statistics t, to be independent. it has , however been shown that slight departures from normality do not seriously effect the tests.
- <em>In case of two small samples both the samples are selected randomly , both the populations are normal and both the populations have equal variances.</em>
2) Chi - squared Test for independence:
The two attributes A and B are said to be independent if the actual frequency equals the expected one , that is, if (AB) = (A)(B)/ N
Similarly α and β will be independent is (αβ) = ( α)( β)/ N and so on.
3) Inference for Regression
When both X and Y are observed at random i.e the sample values are from a bi variate population there are two regression equations , each obtained by choosing that variable as dependent whose average value is to be estimated and treating the other variable as independent .
4) A N O V A
The various sources of variation , degrees of freedom , the sum of squares and the mean squares associated with the sources are generally shown in a table called analysis of variance table or A N O V A table.
5) Matched pairs
It can be used when the experiment has only two events or possibilities and each variable can be grouped in either of the two conditions. Example people having C O V I D 19 and not having C O V I D 19.
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