Statements (48)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:alternativeTo |
gptkb:Ridge_regression
gptkb:Lasso_regression Principal component regression |
| gptkbp:assumes |
Linearity
Homoscedasticity Independence of errors Normality of errors No multicollinearity |
| gptkbp:canBe |
gptkb:t-test
ANOVA F-test |
| gptkbp:detects |
gptkb:Outliers
Influential points Multicollinearity |
| gptkbp:field |
Statistics
Data science Machine learning Econometrics |
| gptkbp:firstDescribed |
gptkb:Francis_Galton
19th century |
| gptkbp:form |
Y = β0 + β1X1 + β2X2 + ... + βnXn + ε
|
| https://www.w3.org/2000/01/rdf-schema#label |
Multiple linear regression
|
| gptkbp:inferenceMethod |
gptkb:Ordinary_least_squares
Maximum likelihood estimation |
| gptkbp:input |
Dependent variable
Multiple independent variables |
| gptkbp:limitation |
Sensitive to outliers
Affected by multicollinearity Assumes linear relationships |
| gptkbp:output |
R-squared
Residuals Intercept Adjusted R-squared Regression coefficients |
| gptkbp:relatedTo |
gptkb:Linear_regression
gptkb:Ordinary_least_squares gptkb:Regression_analysis |
| gptkbp:software |
gptkb:SAS
gptkb:SPSS gptkb:Stata R Python (scikit-learn) |
| gptkbp:usedFor |
Predicting a dependent variable using multiple independent variables
|
| gptkbp:visualizes |
Partial regression plots
Residual plots |
| gptkbp:bfsParent |
gptkb:General_linear_model
|
| gptkbp:bfsLayer |
7
|