

Using the actual number of samples in each bin and the expected number of samples, we can calculate what is called the Chi-Square Statistic in Excel. Understand the Chi-Squared Goodness-of-Fit test premise.īasically, the Chi-Squared Goodness-of-Fit test takes the number of samples in each bin on the histogram and compares that to the number of samples you might expect to find in each bin given a normal curve. If we reject the null, we accept the alternative. In statistical terms, we talk in terms of accepting or rejecting the null hypothesis.

You can also check the Confidence level for mean and the Kth largest and smallest boxes, though that information isn’t required in the Chi-Squared Goodness-of-Fit test, which is the test we are running to test for normality of the data. Ensure at least the Summary statistics box is checked.Select to output information in a new worksheet.In most statistical analysis, that will be the case, but if you have data grouped by rows, you should change the Grouped By selection. In this case, the data is grouped by columns.Click in the Input Range box and select your input range using the mouse.Select Data > Data Analysis > Descriptive Statistics.Use the Descriptive Statistics option in the Analysis ToolPak to quickly generate descriptive statistics for your data set in Sheet 1.

Creating a histogram using the Analysis ToolPak generates a chart and a data table, as seen below to get the ‘Frequency’ of the ‘Bin’ (Bin size is determined by the analyst).In this post, we will share on normality test using Microsoft Excel.įor the example of the normality test, we’ll use set of data below.
CREATING HISTOGRAM IN EXCEL 2016 SOFTWARE
Most us are relying to our advance statistical software such as Minitab, SigmaXL, JMP and many more to validate the data normality. However, deeper analysis is require to validate the normality of the data since it is affecting our analysis method.

CREATING HISTOGRAM IN EXCEL 2016 HOW TO
In our previous post, we have discussed what is normal distribution and how to visually identify the normal distribution.
