Definition of F-Test Formula
F-test formula is used in order to perform the statistical test that helps the person conducting the test in finding that whether the two population sets that are having the normal distribution of the data points of them have the same standard deviation or not.
F-Test is any test that uses F-distribution. F value is a value on the F distribution. Various statistical tests generate an F value. The value can be used to determine whether the test is statistically significant. In order to compare two variances, one has to calculate the ratio of the two variances, which is as under:
While F-test in ExcelF-test In ExcelF-test in excel is a statistical tool that helps us decide whether the variances of two populations having normal distribution are equal or not. F-test is an essential part of the analysis of variance (ANOVA) model., we need to frame the null and alternative hypotheses. Then, we need to determine the level of significance under which the test has to be carried out. Subsequently, we have to find out the degrees of freedom of both the numerator and denominator. It will help determine the F table value. The F Value seen in the table is then compared to the calculated F value to determine whether or not to reject the null hypothesis.
Step by Step Calculation of an F-test
Below are the steps where the F-Test formula is used to the null hypothesis
that the variances of two populations are equal:
- Firstly, frame the null and alternate hypothesis.
The null hypothesis assumes that the variances are equal. H0: σ12 = σ22. The alternate hypothesis states that the variances are unequal. H1: σ12 ≠ σ22. Here σ12 and σ22 are the symbols for variances.
- Calculate the test statistic
(F distribution). i.e., = σ12 / σ22 Where σ12 is assumed to be larger sample variance, and σ22 is the smaller sample variance
- Calculate the degrees of freedom.
Degree of freedom (df1) = n1 – 1 and Degree of freedom (df2) = n2 – 1 where n1 and n2 are the sample sizes.
- Look at the F value in the F table.
For two-tailed tests, divide the alpha by 2 for finding the right critical value. Thus, the F value is found, looking at the degrees of freedom in the numerator and the denominator in the F table. Df1 is read across in the top row. Df2 is read down the first column.
Note: There are different F Tables for different levels of significance. Above is the F table for alpha = .050.
- Compare the F statistic obtained in Step 2 with the critical value obtained in Step 4. If the F statistic is greater than the critical value at the required level of significance, we reject the null hypothesis. If the F statistic obtained in Step 2 is less than the critical value at the required level of significance, we cannot reject the null hypothesis.
A statistician was carrying out F-Test. He got the F statistic as 2.38. The degrees of freedom obtained by him were 8 and 3. Find out the F value from the F Table and determine whether we can reject the null hypothesis at 5% level of significance (one-tailed test).
We have to look for 8 and 3 degrees of freedom in the F Table. The F critical value obtained from the table is 8.845. Since the F statistic (2.38) is lesser than the F Table Value (8.845), we cannot reject the null hypothesis.
An insurance company sells health insurance and motor insurance policies. Premiums are paid by customers for these policies. The CEO of the insurance company wonders if premiums paid by either of insurance segments (health insurance and motor insurance) are more variable as compared to another. He finds the following data for premiums paid:
Conduct a two-tailed F-test with a level of significance of 10%.
- Step 1: Null Hypothesis H0: σ12 = σ22
Alternate Hypothesis Ha: σ12 ≠ σ22
- Step 2: F statistic = F Value = σ12 / σ22 = 200/50 = 4
- Step 3: df1 = n1 – 1 = 11-1 =10
df2 = n2 – 1 = 51-1 = 50
- Step 4: Since it is a two-tailed test, alpha level = 0.10/2 = 0.050. The F value from the F Table with degrees of freedom as 10 and 50 is 2.026.
- Step 5: Since F statistic (4) is more than the table value obtained (2.026), we reject the null hypothesis.
The bank has a Head Office in Delhi and a branch at Mumbai. There are long customer queues at one office, while customer queues are short at the other office. The Operations Manager of the bank wonders if the customers at one branch are more variable than the number of customers at another branch. A research study of customers is carried out by him.
The variance of Delhi Head Office customers is 31, and that for the Mumbai branch is 20. The sample size for Delhi Head Office is 11, and that for the Mumbai branch is 21. Carry out a two-tailed F-test with a level of significance of 10%.
- Step 1: Null Hypothesis H0: σ12 = σ22
Alternate Hypothesis Ha: σ12 ≠ σ22
- Step 2: F statistic = F Value = σ12 / σ22 = 31/20 = 1.55
- Step 3: df1 = n1 – 1 = 11-1 = 10
df2 = n2 – 1 = 21-1 = 20
- Step 4: Since it is a two-tailed test, alpha level = 0.10/2 = 0.05. The F value from the F Table with degrees of freedom as 10 and 20 is 2.348.
- Step 5: Since F statistic (1.55) is lesser than the table value obtained (2.348), we cannot reject the null hypothesis.
Relevance and Uses
F-Test formula can be used in a wide variety of settings. F-Test is used to test the hypothesis that the variances of two populations are equal. Secondly, it is used for testing the hypothesis that the means of given populations that are normally distributedNormally DistributedNormal Distribution is a bell-shaped frequency distribution curve which helps describe all the possible values a random variable can take within a given range with most of the distribution area is in the middle and few are in the tails, at the extremes. This distribution has two key parameters: the mean (µ) and the standard deviation (σ) which plays a key role in assets return calculation and in risk management strategy., having the same standard deviation, are equal. Thirdly, it is used to test the hypothesis that a proposed regressionRegressionRegression Analysis is a statistical approach for evaluating the relationship between 1 dependent variable & 1 or more independent variables. It is widely used in investing & financing sectors to improve the products & services further. model fits the data well.
F-Test Formula in Excel (with Excel Template)
Workers in an organization are paid daily wages. The CEO of the organization is concerned about the variability in wages between males and females in the organization. Below is the data are taken from a sample of males and females.
Conduct a one-tailed F test at a 5% level of significance.
- Step 1: H0: σ12 = σ22, H1: σ12 ≠ σ22
- Step 2: Click on Data Tab > Data Analysis in Excel.
- Step 3: The below-mentioned window will appear. Select F-Test Two-Sample for Variances and then click on OK.
- Step 4: Click on the Variable 1 range box and select the range A2: A8. Click on the Variable 2 range box and select the range B2: B7. Click A10 in the output range. Select 0.05 as alpha as a level of significance is 5%. Then click on OK.
The values for F statistic and F table value will be displayed along with other data.
- Step 4: From the above table, we can see F statistic (8.296) is greater than F critical one-tail (4.95), so we will reject the null hypothesis.
Note 1: Variance of variable 1 has to be higher than the variance of variable 2. Otherwise, calculations made by Excel will be wrong. If not, then swap the data.
Note 2: If the Data analysis button is not available in Excel, go to File > Options. Under Add-ins, select Analysis ToolPak and click on the Go button. Check Analysis Tool Pack and click on OK.
Note 3: There is a formula in Excel to calculate the F table value. Its syntax is:
This article has been a guide to F-Test Formula. Here we learn how to perform F-Test to determine whether or not to reject the null hypothesis along with examples and a downloadable excel template. You can learn more about statistical modeling from the following articles –