• Aucun résultat trouvé

1 Linear regression

N/A
N/A
Info
Télécharger
Protected

Academic year: 2022

Partager "1 Linear regression"

Copied!
1
0
0

Chargement.... (Voir le texte intégral maintenant)

Texte intégral

(1)

Universit´e Joseph Fourier L2/STA230

TP8: Linear regression

The objective of this lecture is to realize some linear regressions and the associated confidence intervals and tests.

1 Linear regression

We want to realize a linear regression to study if the weight of the baby is linked to the weight or the age of the mother.

1. Open the data “BB.csv”.

2. We study the link between the baby’s weight and the mother’s age.

(a) Which variable is the explaining one ? Which variable is the explained one ? Which one is considered as deterministic ? Which one is considered as random

? We denote X the deterministic and explaining variable and Y the random and explained variable.

(b) Plot the scatter plot of the two variables. Comment (c) Write the equation for the regression line for Y onto X.

(d) Compute the correlation coefficient between X andY. Is there a strong corre- lation ?

(e) Find the coefficients of the regression line.

(f) Plot the regression line on the scatter plot. Comment.

(g) Find the estimated variance of the regression. Comment.

(h) What value would you predict for a 42 year old person ?

3. Now, we study the link between the baby’s weight and the mother’s weight. Proceed exactly as before.

Références

Télécharger maintenant ( PDF - 1 Page - 55.16 KB )

Documents relatifs

1. Linear recurrent sequences and iterations of linear maps

Typical Diophantine results which will be treated include: Skolem-Mahler-Lech the- orem on zeros of linear recurrent sequences, the solutions to Pisot’s conjecture on perfect powers

Adaptive and optimal online linear regression on $\ell^1$-balls

The resulting algorithm – called adaptive EG ± algorithm – can be applied to general convex and differentiable loss functions.. When applied to the square loss, it yields an

Linear Support Vector Regression with Linear Constraints

Outline. The article proceeds as follows: we introduce the optimization problem coming from the classical SVR and describe the modifications brought by adding linear constraints

Fuzzy Piecewise Linear Regression

It follows that it is impossible to have a decreasing (resp. increasing) spread of the model output for positive (resp. This restriction is acceptable in a measurement context where

Linear regression in the Bayesian framework

Keywords: linear regression, Bayesian inference, generalized least-squares, ridge regression, LASSO, model selection criterion,

Linear Quantile Regression and Endogeneity Correction

Finally, instead of imposing Assumption 2’, one may …rst test for which coe¢ cients the hypothesis of constant e¤ect is rejected or not in typical quantile regression estimation, so

Linear Quantile Regression and Endogeneity Correction

Chernozhukov V, Hansen C (2006) Instrumental Quantile Regression Inference for Structural and Treatment Effect Models.. Chernozhukov V, Hansen C (2008a) Instrumental Variable Quantile

Consider the standard linear regression model y = X

, 430 where price is the price paid in auction (in million $ ), area is the area of the painting (height times width, both measured in inches), ratio is the aspect ratio (height