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gptkbp:instanceOf
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gptkb: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
|
|
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
|
gptkb: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
|
|
https://www.w3.org/2000/01/rdf-schema#label
|
Linear regression
|