A residual plot (meaning, residuals on one axis, conventionally the $y$ axis) and predicted or fitted on the other, conventionally the $x$ axis) is a kind of overall health check on a regression-type model.
Unless this plot is mislabelled it doesn't refer to a particular "independent variable", nor can it help much in determining which independent variables to use in a model. I am with those who think that "independent variable" is an outdated and unsatisfactory term; unfortunately we can't all agree on which term is best, but "predictor" or "covariate" suits many people better.
The main message from this plot that I pick up is to note that residuals all lie above a line with negative slope. The key question to answer is whether the response is positive by definition, or at least non-negative. If so, negative predicted values don't make much sense, and a logarithmic transformation or using a logarithmic link seems strongly indicated. I would want a more symmetric and more nearly patternless scatter to declare the regression satisfactory.
On that and on other grounds I tend to disagree with @user1669710. Experienced users of statistics are typically reluctant to declare data points as outliers without very strong reasons, particularly whenever heteroscedasticity is a more plausible explanation. In particular, the appearance of outliers is often illusive and is not maintained when using an appropriate non-linear scale.
If this is a plot from a standard regression, the mean residual will be zero, period, and is not, and cannot be, diagnostic.
EDIT: As the response variable has now been explained as non-negative, a Poisson regression is strongly indicated. Note that (raw) residuals are now expected to be heteroscedastic, but standardization can help with that.