Linear Regression
Fit Linear Regression Model
Fitted Values & Residual Analysis
Enter Data:
From Textbook
Upload File
Enter Own
Choose Dataset:
Movie Ratings
Organic Foods
Bad Drivers
Rail Trail
Baseball Scoring
Buchanan and Perot
Internet & Facebook
House prices in FL
Cereals: Sodium & Sugar
Animal Longevity
Subway Sandwiches
U.S. Statewide Crime
Mountain Bikes
Tour de France Performance
Female Athletes: Maximum Bench Press
Male Athletes Strength
Car Weight & Mileage
Cell Phone Specs
Georgia Student Survey: High School & College GPA
Beer Alcohol and Calories
Fuel Efficiency and Speed (nonlinear)
More info on dataset
Choose CSV File:
Browse...
Select X-Variable:
Select Y-Variable:
Name of X-Variable:
Name of Y-Variable:
Copy and paste columns from a spreadsheet, or enter observations directly:
Submit Data
Show Summary Statistics
Plot Options:
Smooth Trend
Regression Line
Degree of Smoothness
Click to Remove Points
Drag Points
Select Variable(s) for Hover Info
ID-Variable(s):
Country
Title & Subtitle
Axis Labels
Title:
Subtitle:
X-Axis Label:
Y-Axis Label:
Regression Options:
Find Predicted Value
x-Value:
Show Residuals on Plot
Show Standard Errors & P-values
Confidence Interval for Slope
Confidence Level:
Confidence/Prediction Interval
Confidence Level:
For Mean Response
For Individual Response
x-Value:
ANOVA Table
Linear Regression Equation:
Model Summary:
Dataset:
Type of Residuals:
Raw
Standardized
Plot Residuals:
Versus Explanatory Variable
Versus Fitted Values
Select Variable(s) for Hover Info
ID-Variable(s):
Histogram/Boxplot of Residuals
Change Binwidth
Superimpose Normal Curve
Dataset, with new columns for fitted values and residuals added at the end: