Statements (51)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:alternativeTo |
gptkb:Lasso_Regression
gptkb:Polynomial_Regression gptkb:Ridge_Regression Logistic Regression Nonlinear Regression |
| gptkbp:appliesTo |
Time series analysis
Forecasting Risk assessment Experimental data |
| gptkbp:assesses |
gptkb:t-test
F-test Adjusted R-squared Residual plots Durbin-Watson test |
| gptkbp:assumes |
Linearity
Homoscedasticity Independence of errors Normality of errors |
| gptkbp:canBe |
Multiple Linear Regression
Simple Linear Regression |
| gptkbp:developedBy |
gptkb:Francis_Galton
gptkb:Karl_Pearson |
| gptkbp:estimatedCost |
Regression coefficients
|
| gptkbp:firstDescribed |
19th century
|
| https://www.w3.org/2000/01/rdf-schema#label |
Linear Regression Analysis
|
| gptkbp:limitation |
Assumes linearity
Sensitive to outliers Multicollinearity can affect results |
| gptkbp:output |
Confidence intervals
P-values R-squared value Regression equation |
| gptkbp:purpose |
Model relationship between dependent and independent variables
|
| gptkbp:reduces |
Sum of squared residuals
|
| gptkbp:relatedTo |
gptkb:Ordinary_Least_Squares
Prediction ANOVA Residuals Correlation |
| gptkbp:software |
gptkb:Python
gptkb:SAS gptkb:SPSS gptkb:Stata R |
| gptkbp:usedIn |
gptkb:Machine_Learning
gptkb:Data_Science Statistics Econometrics |
| gptkbp:bfsParent |
gptkb:G.A.F._Seber
|
| gptkbp:bfsLayer |
8
|