Whether to use weight or its z-score or percentile? I am trying to do regression analysis with level of a chemical in blood as dependent variable and age, gender and weight of children as predicting variables. The sample size is about 5000. Age and weight are highly correlated in children. My doubts are: 


*

*Should I use z-scores or percentiles for weight rather than raw values? 

*Should I use some other technique rather than ordinary linear regression? 

*Do I need to check if data has normal distribution at this sample size?
Edit: I want to clarify regarding z-score or percentile here: I have ages as 5,6,7,8 etc with no fractional ages. I thought for each age I can calculate z-score or percentile of weight for that individual child and use it instead of raw weight. By this I can answer the question that 'Is being overweight for age has any effect on blood level of the chemical'? Is this reasonable argument? Also, this question differs from the earlier question and is not a duplicate. My questions 2 and 3 do not figure in the title. 
Regarding a comment on biological issues by @DLDahly: The ages are 5-15 years. Biologically, I want to determine if the weight is a predictor of blood level of chemical, independent of age? Chemical level rises with age, but it is not clear if being overweight increases it further. Actually, one cannot rule out the possibility that this rise may be related mainly to weight and not to age as such. 
 A: The difficulty's in equating say an eight-year-old whose weight is two standard deviations above the mean for his age (fat), with a fourteen-year-old whose weight is two standard deviations above the mean for his age (shooting up). And even if you're happy with that for the population, you still need to be happy with it for your sample.
Rather than try to stipulate how age moderates the effect of weight on the blood concentration of some chemical, as you've got 5000 observations you can afford to be more flexible: an additive model with some non-linear terms in age already allows the effect of weight to be controlled for age; including interaction terms allows the slope to vary.
Suppose you were considering
$$
\operatorname{E} Y = \beta_0 + \beta_a a + \beta_w w'
$$
where $Y$ is blood concentration of the chemical, $a$ is age, $w'$ weight standardized within each age, & the $\beta$s the coefficients
then the model 
$$
\operatorname{E} Y = \beta_0 + \beta_{a} a + ... + \beta_{a^{10}}a^{10} + \beta_w w
+  \beta_{wa} wa + ... + \beta_{wa^{10}} w a^{10}
$$
where $w$ is unstandardized weight, would include the first as a special case while being much more flexible—it doesn't rigidly assume it's the no. standard of deviations from the mean weight within each age group that's what counts, while still allowing slope & intercept for weight to vary within each age group. Of course you likely needn't go up to a 10th-order polynomial for a good fit, & it'd be sensible to allow for non-linearity in the effect of weight as well (I'd suggest a natural spline basis).
