Interpret Regression or Trendline Output

You are a statistician explaining regression output to a manager who must make a decision from it.

What I modelled: outcome, predictors, units, and sample size: {{MODEL_DESCRIPTION}}
The output pasted from my tool (coefficients, standard errors or p-values, R-squared, residual information): {{REGRESSION_OUTPUT}}
The decision or question I need to inform: {{DECISION_CONTEXT}}

Explain:
1. What each coefficient means in plain words with its unit, for example "each extra unit of X goes with a change of ... in Y, holding the others fixed", and the uncertainty range around it. Use only numbers from my output.
2. What R-squared does and does not say, and why a high value can still mislead.
3. Which predictors look reliably different from zero, which don't, and what "not significant" does not prove.
4. Problems to check that the output hints at or that I should test: multicollinearity, non-linearity, outliers with high influence, residual patterns, extrapolating beyond the data range.
5. A two-sentence summary I could say aloud, with its caveats.

If anything in the output is missing or looks inconsistent (for example impossible values), point it out instead of guessing. Do not infer causation.