One can conclude from your ANOVA table that none of the variables{1,x,x^2) has a decisive influence on Y (see https://en.wikipedia.org/wiki/Analysis_of_variance). Your parameter table show that the variance of the estimators of the coefficients is excessive (see https://en.wikipedia.org/wiki/Student%27s_t-test).
In addition, you should have a look on the R^2:
lm["RSquared"]
lm["AdjustedRSquared"]
giving respectively 0.608234 and 0.412351. Rather poor, because the model is too complex.
A degree one model is better:
lm = LinearModelFit[data, {x}, x]
lm["ANOVATable"]
lm["ParameterTable"]
lm["RSquared"]
lm["AdjustedRSquared"]
Show[ListPlot[data], Plot[lm[x], {x, 0, 10}], Frame -> True]
But the Pvalue of the intercept is very large, while the adjusted R^2 is just slightly better...
Thus, it's better to choose a simpler one:
m = LinearModelFit[data, {x}, x, IncludeConstantBasis -> False]
lm["ANOVATable"]
lm["ParameterTable"]
lm["RSquared"]
lm["AdjustedRSquared"]
Show[ListPlot[data], Plot[lm[x], {x, 0, 10}], Frame -> True]
giving 0.827217 as adjusted R^2, for Y=1.43 x.