Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:assesses |
gptkb:Mean_Squared_Error
gptkb:Root_Mean_Squared_Error R-squared Adjusted R-squared |
| gptkbp:assumes |
Linearity
Homoscedasticity Independence of errors Normality of errors |
| gptkbp:category |
Supervised Learning
Parametric Model Regression Model |
| gptkbp:detects |
gptkb:Outliers
Collinearity Influential Points |
| gptkbp:estimatedCost |
Relationship between variables
|
| gptkbp:fittedBy |
gptkb:Ordinary_Least_Squares
Gradient Descent |
| gptkbp:hasEquation |
y = β0 + β1x + ε
|
| gptkbp:hasType |
Multiple Linear Regression
Simple Linear Regression |
| https://www.w3.org/2000/01/rdf-schema#label |
Linear Regression
|
| gptkbp:implementedIn |
gptkb:Python
gptkb:SAS gptkb:MATLAB gptkb:SPSS gptkb:Excel R |
| gptkbp:input |
One or more Independent Variables
|
| gptkbp:introduced |
gptkb:Francis_Galton
gptkb:Karl_Pearson |
| gptkbp:limitation |
Sensitive to Outliers
Cannot model non-linear relationships without transformation Affected by Multicollinearity Assumes Linear Relationship Assumes no autocorrelation in errors |
| gptkbp:output |
Continuous Variable
|
| gptkbp:reduces |
Sum of Squared Errors
|
| gptkbp:relatedTo |
gptkb:Lasso_Regression
gptkb:Polynomial_Regression gptkb:Ridge_Regression Logistic Regression |
| gptkbp:requires |
Quantitative Variables
|
| gptkbp:usedFor |
Regression Analysis
Predictive Modeling |
| gptkbp:usedIn |
gptkb:Machine_Learning
gptkb:Data_Science Statistics |
| gptkbp:bfsParent |
gptkb:Linear_Model
|
| gptkbp:bfsLayer |
5
|