Multiple Linear Regression using fitlm function

Hello Sir, why i am not getting the intercept for the other variables?

>> lm=fitlm(X,TAG)

Warning: Regression design matrix is rank deficient to within machine precision.

> In classreg.regr.CompactTermsRegression/checkDesignRank (line 35)

In LinearModel.fit (line 1237)

In fitlm (line 121)

lm =

Linear regression model:

y ~ 1 + x1 + x2 + x3 + x4 + x5 + x6 + x7

Estimated Coefficients:

Estimate SE tStat pValue

__________ __ _____ ______

(Intercept) 0 0 NaN NaN

x1 0 0 NaN NaN

x2 0.00037516 0 Inf NaN

x3 0.00021467 0 Inf NaN

x4 -0.16078 0 -Inf NaN

x5 0.68268 0 Inf NaN

x6 -0.0013354 0 -Inf NaN

x7 0 0 NaN NaN

Number of observations: 5, Error degrees of freedom: 0

R-squared: 1, Adjusted R-Squared: NaN

F-statistic vs. constant model: NaN, p-value = NaN

Answer 1

[Star Strider]

In a linear regression of any sort, there is only one intercept.

[ANURAG DEEPAK]

Then sir how to deal with such situation, 7 independent variable and 1 dependent variable?

[Star Strider]

If you want to ssee what the intercepts of the individual variables are, you need to regress them individually. However, the entire idea of a multiple linear regression is to regress the various predictor variables together, to get a unified idea of how they all interact. Note that they do not have to be individual predictors, and can be interaction terms (predictors multiplised together) that distorts the idea of an intercept.

If you want to see what variables best predict the dependent variable, use the stepwiselm function.

Answer 2

[Image Analyst]

Why not do

coefficients = y \ x;

where y and x are tall matrices that have your observation values listed? (Or maybe it's y/x - I don't know which off the top of my head.)