Suppose You've Run a Regression Relating Revenues to TV Ads

Suppose you've run a regression relating Revenues to TV Ads and Online Ads. You are willing to make the necessary assumptions to deduce causality and run hypothesis tests. Your results are as follows: $$\begin{array} { | l | c | c | c | c | } \hline & \text { Coefficients } & \text { Stardard Error } & \text { t-Stat } & \text { P-value } \\\hline \text { Iratercept } & 5988.043107 & 2202.136765 & 2.719196738 & 0.006881952 \\\hline \text { IV Ads } & 199.6320212 & 27.82658673 & 7.174146912 & 4.63154 \mathrm { E } - 12 \\\hline \text { Orline Ads } & 53.52092429 & 41.0818115 & 1.302788809 & 0.193533758 \\\hline\end{array}$$ If you tested the null hypothesis that Online Ads have no impact on Revenues at the 90% confidence level (i.e., 90% degree of support) , you would:

A) Reject, and conclude Online Ads do impact Revenues
B) Fail to reject, and conclude Online Ads do impact Revenues
C) Fail to reject, and conclude there is insufficient evidence to establish that Online Ads impact Revenues
D) None of the answers is correct