Outliers Calculator

Guide to Using the Outliers Calculator

Step 1: Enter Your Data

Begin by entering the numbers you want to analyze in the calculator. In the input field labeled Enter Number, input each number individually. Press Enter or Return on your keyboard after typing each number to add it to the dataset.

Step 2: Select an Outlier Detection Method

Once your numbers are entered, you need to choose an outlier detection method. The calculator supports two methods:

• Interquartile Range (IQR)
• Z-Score

Select your preferred method from the dropdown menu labeled Outlier Detection Method. Ensure that you pick a method that suits your statistical analysis needs.

Step 3: Specify the Threshold Value

Next, enter a threshold value in the field labeled Threshold Value. The recommended default values are:

• 1.5 for the IQR method
• 3 for the Z-Score method

This threshold affects how aggressive or lenient the outlier detection will be. Adjust it based on your analytical requirements.

Step 4: Review the Calculated Results

After inputting your data and settings, the calculator will provide various statistical metrics:

• Mean: The average of your dataset.
• Median: The midpoint value when the dataset is ordered.
• First Quartile (Q1) and Third Quartile (Q3): Useful if using IQR method.
• Interquartile Range (IQR): Q3 minus Q1, relevant if using the IQR method.
• Standard Deviation: Measures the dispersion of the dataset, crucial if using the Z-Score method.
• Lower Bound and Upper Bound: These values define the limits beyond which data points are considered outliers based on your chosen method and threshold.
• Outliers: A list of data points that are identified as outliers.
• Number of Outliers: Total count of the detected outliers in your dataset.

Step 5: Analyze Your Outliers

Review the identified outliers and use this information to make data-driven decisions. The calculated bounds and metrics can provide insights into the nature of outliers within your dataset.