Solved – How to capture & present lm model output from R

linear modelmodelingrregressionregression coefficients

After running iterations of lm() in R, I am now stuck with which components of the model's output to present and how to present them. I know that the $R^{2}$ value, coefficients plot and intercept are of central importance. Is there any free resource which shows: how to interpret output, and then visually represent of model outputs,especially output from R. I read Interpretation of R's lm() output but I find it difficult to translate that into what it means in my domain.

My domain is marketing. I am trying to model impact of TV advertising on lead generation.
My $R^{2}$ value is high but when I plot my coefficients using coefplot in R, they are on the 0 Line. I don't know what to make of it. Happy to share more details & output.

Here is the model output & plots: 

Call:
lm(formula = Leads.T ~ ImpressionsM, data = allmodelsetdaily)

Residuals:
Min      1Q  Median      3Q     Max 
-213.81  -60.69   11.81   71.74  178.02 

Coefficients:
Estimate Std. Error t value Pr(>|t|)    
(Intercept)  337.08397   22.22891   15.16   <2e-16 ***
ImpressionsM   0.06898    0.00427   16.15   <2e-16 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 97.15 on 89 degrees of freedom
Multiple R-squared:  0.7457,    Adjusted R-squared:  0.7428 
F-statistic: 260.9 on 1 and 89 DF,  p-value: < 2.2e-16"

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Best Answer

Based on the question, it sounds like this might be one of your first uses of regression. My suggestions below assume that is the case.

In terms of understanding how to interpret regression output, I'd break down the question into: (a) what are the components and what do they mean (b) how do I map from those components to the output of lm()

for (a), a good free source is Khan Academy. https://www.khanacademy.org/math/probability/regression

for (b), in addition to lm, summary(your.model) or summary(lm(…)) produces more of the output components in a more readable form. ?summary

A non-free, but inexpensive alternative that answers both is A Handbook of Statistical Analyses Using R.

Regarding visual representation: If you are running univariate regression, you can plot in xy space using plot() and abline(your.model).

If you have multivariate regression, consider whether your audience be able to interpret a more complicated visualization.

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