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-squared value, Co-efficients 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 http://stats.stackexchange.com/questions/5135/interpretation-of-rs-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-squared 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"

![enter image description here][1]


![enter image description here][2]


  [1]: https://i.sstatic.net/oFTT5.png
  [2]: https://i.sstatic.net/gnN2i.png