Statements (50)
| Predicate | Object |
|---|---|
| gptkbp:instanceOf |
statistical analysis
|
| gptkbp:assumes |
homoscedasticity
linear relationship independence of errors no multicollinearity normality of errors |
| gptkbp:can_be_extended_to |
gptkb:polynomial_regression
logistic regression |
| gptkbp:can_be_regularized_by |
gptkb:lasso_regression
ridge regression |
| gptkbp:evaluated_by |
gptkb:adjusted_R-squared
mean squared error R-squared |
| gptkbp:hasModel |
relationship between dependent and independent variables
|
| gptkbp:hasType |
gptkb:multiple_linear_regression
gptkb:simple_linear_regression |
| https://www.w3.org/2000/01/rdf-schema#label |
Linear regression
|
| gptkbp:implementedIn |
gptkb:SAS
gptkb:SPSS gptkb:scikit-learn gptkb:statsmodels R |
| gptkbp:introduced |
gptkb:Francis_Galton
|
| gptkbp:limitation |
assumes linearity
sensitive to outliers cannot model non-linear relationships requires large sample size for stability |
| gptkbp:output |
regression coefficients
intercept predicted values |
| gptkbp:popularizedBy |
gptkb:Karl_Pearson
|
| gptkbp:reduces |
sum of squared errors
|
| gptkbp:relatedTo |
statistical analysis
ANOVA correlation |
| gptkbp:requires |
dependent variable
independent variables |
| gptkbp:solvedBy |
gptkb:ordinary_least_squares
gradient descent |
| gptkbp:used_in |
gptkb:machine_learning
data analysis statistics |
| gptkbp:usedFor |
feature selection
trend analysis forecasting predicting continuous outcomes quantifying relationships |
| gptkbp:visualizes |
scatter plot
|
| gptkbp:bfsParent |
gptkb:Method_of_Least_Squares
|
| gptkbp:bfsLayer |
6
|