Removing Outliers Using IQR Method
In real-world datasets, extreme values or outliers can distort statistical results and visualizations. One common and effective way to detect and remove these outliers is by using the Interquartile Range (IQR) Method.
What Is IQR?
The Interquartile Range (IQR) is a measure of statistical dispersion and is calculated as:
The Interquartile Range (IQR) is a measure of statistical dispersion and is calculated as:
IQR=Q3−Q1IQR=Q3−Q1
Q1 = 25th percentile (first quartile)
Q3 = 75th percentile (third quartile)
Values lying below Q1−1.5×IQR or above Q3+1.5×IQR are typically considered outliers.
Calculating IQR and Detecting Outliers
Step 1: Calculate Quartiles and IQR
q1_placement <- quantile(df$placement_exam_marks, 0.25)
q3_placement <- quantile(df$placement_exam_marks, 0.75)
iqr_placement <- q3_placement - q1_placement
Step 2: Define Upper and Lower Boundaries
Thresh_hold <- 1.5
upper_boundary <- q3_placement + (Thresh_hold * iqr_placement)
lower_boundary <- q1_placement - (Thresh_hold * iqr_placement)
Step 3: Identify and Remove Outliers
# Display Outliers
df[df$placement_exam_marks > upper_boundary | df$placement_exam_marks < lower_boundary,]
# Create Cleaned Dataset
df2 <- df[df$placement_exam_marks <= upper_boundary & df$placement_exam_marks >= lower_boundary,]
View(df2)
Summary
- IQR method is useful when the data is not normally distributed;
- It is non-parametric, meaning it does not assume a specific data distribution;
- Best suited for small to medium-sized datasets with clear extremes.
Tak for dine kommentarer!
Spørg AI
Spørg AI
Spørg om hvad som helst eller prøv et af de foreslåede spørgsmål for at starte vores chat
Can you explain why the IQR method is preferred for non-normally distributed data?
What are some limitations of using the IQR method for outlier detection?
Can you show how to interpret a box plot in the context of outlier detection?
Awesome!
Completion rate improved to 4Removing Outliers Using IQR Method
Stryg for at vise menuen
In real-world datasets, extreme values or outliers can distort statistical results and visualizations. One common and effective way to detect and remove these outliers is by using the Interquartile Range (IQR) Method.
What Is IQR?
The Interquartile Range (IQR) is a measure of statistical dispersion and is calculated as:
The Interquartile Range (IQR) is a measure of statistical dispersion and is calculated as:
IQR=Q3−Q1IQR=Q3−Q1
Q1 = 25th percentile (first quartile)
Q3 = 75th percentile (third quartile)
Values lying below Q1−1.5×IQR or above Q3+1.5×IQR are typically considered outliers.
Calculating IQR and Detecting Outliers
Step 1: Calculate Quartiles and IQR
q1_placement <- quantile(df$placement_exam_marks, 0.25)
q3_placement <- quantile(df$placement_exam_marks, 0.75)
iqr_placement <- q3_placement - q1_placement
Step 2: Define Upper and Lower Boundaries
Thresh_hold <- 1.5
upper_boundary <- q3_placement + (Thresh_hold * iqr_placement)
lower_boundary <- q1_placement - (Thresh_hold * iqr_placement)
Step 3: Identify and Remove Outliers
# Display Outliers
df[df$placement_exam_marks > upper_boundary | df$placement_exam_marks < lower_boundary,]
# Create Cleaned Dataset
df2 <- df[df$placement_exam_marks <= upper_boundary & df$placement_exam_marks >= lower_boundary,]
View(df2)
Summary
- IQR method is useful when the data is not normally distributed;
- It is non-parametric, meaning it does not assume a specific data distribution;
- Best suited for small to medium-sized datasets with clear extremes.
Tak for dine kommentarer!
