Can you please review my analysis based on the following plots. It is a multiple predictors linear regression model 
The model form assumption is met based on the plots above. we can see that there is no clear pattern in the residual vs fitted plot. This indicates meet the assumption that there is a linear relationship between the predictors and the outcome variable, so our form is correct. Looking at the red line it is close to flat on 0, which indicates that the residual errors have a mean value of zero. No extreme outliers are present. The constant variance assumption is met. There does seem to be slight deviation from the flat red line in the Scale-Location plot, but not enough to be concerning. We assume that these observations were obtained independently. The normality assumption is also met since the histogram follows a bell-shaped curve that is evenly distributed around zero with no extreme outliers. Looking at the Normal Q-Q plot do not deviate from the trend line too much. We can conclude that there are no problems with linearity, constant variance, and normal assumptions. After exploring my data distributions, I can see that predictors and response variables are non-normal distributions. The predictors are positively skewed distributions and the response is slightly negatively skewed distribution. I consider logarithmic transformations for achieving a linear relationship between variables and to make sure there is homoscedasticity. How do I interpreted the R outcome because of the log transformation is that how the median change when a predictor changes by e.
