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gptkbp:instanceOf
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gptkb:statistical_analysis
|
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gptkbp:alternativeTo
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gptkb:LASSO_regression
gptkb:principal_component_regression
robust regression
|
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gptkbp:assumes
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homoscedasticity
linearity
independence of errors
no multicollinearity
errors are normally distributed
|
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gptkbp:BLUE
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gptkb:Best_Linear_Unbiased_Estimator
|
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gptkbp:category
|
gptkb:estimation_theory
gptkb:statistical_analysis
statistical inference
|
|
gptkbp:estimatedCost
|
regression coefficients
|
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gptkbp:introduced
|
gptkb:Carl_Friedrich_Gauss
gptkb:Adrien-Marie_Legendre
|
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gptkbp:output
|
gptkb:adjusted_R-squared
R-squared
F-statistic
confidence intervals
residuals
fitted values
prediction intervals
standard error
t-statistics
|
|
gptkbp:reduces
|
sum of squared residuals
|
|
gptkbp:relatedTo
|
gptkb:generalized_least_squares
ridge regression
linear model
weighted least squares
|
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gptkbp:requires
|
design matrix
|
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gptkbp:solvedBy
|
gptkb:algebra
QR decomposition
singular value decomposition
normal equation
|
|
gptkbp:suffersFrom
|
outlier sensitivity
|
|
gptkbp:used_in
|
gptkb:machine_learning
statistics
econometrics
|
|
gptkbp:usedFor
|
hypothesis testing
linear regression
parameter estimation
ANOVA
forecasting
model diagnostics
trend estimation
|
|
gptkbp:yield
|
gptkb:BLUE_estimator
|
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gptkbp:bfsParent
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gptkb:Method_of_Least_Squares
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gptkbp:bfsLayer
|
6
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|
https://www.w3.org/2000/01/rdf-schema#label
|
Ordinary least squares
|