main index

P00: frame around

P01: olicognography

P02: addictions


Registers of application docs

*discrete geometry optimization *

*scaling *



Similar user docs

*physics formalism *

*statistics processes *

*procedure biases*


Ordinary Least Square Uncertainty

Problems of Regression Model Using Ordinary Least Squares




Remedial action

Problems due to assumptions of least squares


not normal

inferential test procedures based on F test may be invalid

Rankit plot: Shapiro W test (and others)

Transform y values (Box-Cox transformation).

Use of different error models (Generalised linear modelling.)


Biased estimation of error variance and hence inferential test procedures may be invalid

Plot residuals against y . x’s & other variables. Anscombe’s test (and others)

Transform y variable y ˝ .log(y).y

not independent

Inferential test procedures may be invalid. Underestimate true sampling variance of regression estimates. Inflated R

Residual plots. Some tests (e.g.: Durbin-Watson; space: Moran)

Iterated generalised least squares

Non linearity of functional relationship

Poor fit; meaningless results: non independent residuals

Scatterplots of y against x’s. Added variable plots.

transforms x’s and/or y variables

Problems due to the nature of data

Multicollinearity amongst explanatory variable


Correlation measures. tests based on eigenvalues of (X

Transform explanatory variables. Delete variables. Ridge regression

Difficulties in performing: efficient analysis sifting out variables

Added variable plots for variable selection. Transform x’s and/or y to simplify model. Stepwise regression

Outliers and leverage effects

May severily distort model fit. Model fit is dependent on a few values.

Robust. resistant regression.

Data deletion

Inacurate data

Meaningless results

Exploratory data methods may highlights errors

Delete or replace inaccurate values

Incomplete data

Missing at random: could be wasteful of other information if this has to be discarded. Not missing at random: suspect inferences

Estimate missing values (missing at random). Reduce data matrix to the cases with full information


“Normal” linear regression model inappropriate

Generalised linear model (e.g. logistic regression).

Source: nfm

Places of use docs

*matrix analysis *

*formal hierarchy *