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F test variance analysis

The f-test is named in honour of the great statistician

The calculated value of F is compared with the table value for

Since F test is based on the ratio of two variances, it is also known as the variance ratio test. The ratio of two variances follows a distribution called the f distribution named after the famous statistician

Assumptions in

Normality, the values in each group are normally distributed,

Homogeneity, the variance within each group should be equal for all groups

Independence of error. It states that the error (variation of each value around its own group mean) should be independent for each value.

Some of its main topics are:

Variance analysis

Variance analysis-techniques

Irregular variations

f-test applications

Inverse interpolation

Extrapolation

Data coding

Semi averages method

Ratio-to-trend method

Trend origin shifting

Seasonal index-uses

**R.A. Fisher**. The object of the f-test is to find out whether the two independent estimates of population variance differ significantly, or whether the two samples may be regarded as drawn from the normal populations having the same variance. For carrying out the test of significance, we calculate the ratio F. F is defined as:The calculated value of F is compared with the table value for

**u**and_{1}**u**at_{2}**5% or 1%**level of significance. If calculated value of F is greater than the table value then the F ratio is considered significant and the null hypothesis is rejected. On the other hand, if the calculated value of F is less than the table value the null hypothesis is accepted and it is inferred that both the samples have come from the population having same variance.Since F test is based on the ratio of two variances, it is also known as the variance ratio test. The ratio of two variances follows a distribution called the f distribution named after the famous statistician

**R.A. Fisher**.Assumptions in

**F-test**: The F test is based on the following assumptions:Normality, the values in each group are normally distributed,

Homogeneity, the variance within each group should be equal for all groups

**(σ**this assumption is needed in order to combine or pool the variance within the groups into a single within groups source of variation._{1}^{2}= σ_{2}^{2}= ……..= σ_{c}^{2})Independence of error. It states that the error (variation of each value around its own group mean) should be independent for each value.

Some of its main topics are:

Variance analysis

Variance analysis-techniques

Irregular variations

f-test applications

Inverse interpolation

Extrapolation

Data coding

Semi averages method

Ratio-to-trend method

Trend origin shifting

Seasonal index-uses

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Assignment Help

Business Forecasting
Central Value Measures
Correlation Regression Analysis
Data Tabulation
Diagrammatic Graph Presentation
Dispersion Measures
Experimental Designs
F Test Variance Analysis
Index Numbers
Partial Multiple Correlation
Probability Expected Value
Sampling Designs
Skewness Moments Kurtosis
Statistical Survey
Decision Theory
Statistical Fallacies
Statistical Quality Control
Statistics What Why
Theoretical Distribution
Vital Statistics
F2 Test
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