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Data analysis on Excel filtered data with XLSTAT


This tutorial shows how to perform a statistical analysis on Excel filtered data using the XLSTAT software.           


An Excel sheet with both the data and the results can be downloaded by clicking on the button below:
Download the data

Goal of this tutorial

The data represent the results of a survey on the amount of money people spend on online shopping on a monthly average basis. Rows correspond to respondents and columns to the amount of money spent as well as the age group they belong to.

Here, we want to compute descriptive statistics for the survey responses. We will focus on the younger population of the respondents thus we will first exclude the age group >45 using the Excel filter option and then carry out the analysis using XLSTAT.

Filter data in Excel

On the Data tab of your Excel spreadsheet, in the Sort & Filter group, click Filter.

 Arrows in the column headers appear. Click the arrow next to Age class. Click the check box next to >45 in order to deselect this specific age group. Then click OK.

Run an analysis on Excel filtered data with XLSTAT

Once XLSTAT is open, select the XLSTAT / Describing data / Descriptive statistics command as shown below.

The Descriptive Statistics dialog box appears.

In the General tab, select column B in the Quantitative data field and column A in the Subsamples field. Click OK.

A new window pops up (see below). Click on the button 1 in order to run the calculations on the filtered table.

The age class >45 is successfully excluded from both tables and graphs of the XLSTAT output. For more interpretation of descriptive statistics, read our Mean, median, standard deviation & more tutorial.

Similarly, we can perform any data analysis method on a filtered data table.Simply select the appropriate feature from the XLSTAT menu, once you have filtered your data, configure the dialog box and start the computations.

Note: The ability of analyzing filtered data is currently available in most of the essential XLSTAT features (e.g. ANOVA, Histograms, Normality tests, PCA, etc) plus  a few advanced features (e.g. Conjoint analysis, Mann-Kendall, Random forest, etc).

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