Step‑by‑Step Tutorial: 1.5×IQR Statistical Outlier Rule in Excel
Step‑by‑step tutorial: 1.5×IQR Statistical Outlier Rule in Excel
This step‑by‑step tutorial shows you exactly how to use the Interactive Statistics Education Add‑In for Excel to apply the 1.5×IQR Statistical Outlier Rule, detect outliers, and generate automated statistical reports and visuals.
In practice, you only need to complete Steps 1–4; after that, the Add‑In guides you with on‑screen messages and builds the full report for you. This workflow has been tested with Canadian university business analytics students and is designed to be intuitive, educational, and fast.
Getting started with the 1.5×IQR Statistical Outlier Add‑In
Step 1: Add the Add‑In in Excel Install and load the Interactive Statistics Education Add‑In so it appears in your Excel ribbon.
Step 2: Select the “Outlier Analysis” tab Click on the Outlier Analysis tab to access the 1.5×IQR Statistical Outlier Rule features.
Step 3: Click the “Find Outliers” button Press the Find Outliers button to launch the automated analysis process.
Step 4: Select your column of data Highlight the numeric column you want to analyze (excluding the header). The Add‑In will now begin generating your outlier and statistics report.
Automated analysis and report generation
Once your data is selected, the Add‑In automatically walks you through the following stages:
Step 5: Outlier Analysis conclusion Review a summary of which values are classified as statistically high or low outliers using the 1.5×IQR rule.
Steps 6–9: Descriptive statistics report and charts Explore key descriptive statistics (mean, median, standard deviation, percentiles) and supporting charts to understand the distribution and shape of your data.
Step 10: Histogram analysis conclusion Interpret the histogram, including bin settings and interval width, to see how outliers affect the overall distribution.
Step 11: Trend analysis conclusion Review trend measures such as R and R² to determine whether your data shows a positive, neutral, or negative trend over time or sequence.
Step 12: With vs. without outliers analysis conclusion Compare how your results change when outliers are included versus excluded, helping you understand their impact on your analysis.
Step 13: With vs. without outliers box‑and‑whisker plot View side‑by‑side box‑and‑whisker plots to visualize how removing outliers changes the spread and center of your data.
Step 14: Student’s t‑test analysis conclusion Evaluate statistical differences between your original dataset and the dataset without outliers using a t‑test summary.
Advanced outlier handling and forecasting
Step 15: Replace statistical outliers with box‑and‑whisker limits Automatically replace extreme values with the upper and lower box‑and‑whisker limits to create a “winsorized” dataset.
Step 16: Descriptive statistics with outliers vs. with outliers replaced Compare descriptive statistics for three versions of your data: original, without outliers, and with outliers replaced.
