Suggest me best statistical test I have data of frequency of 10 males( birds) voice across age. And I want to study how peak frequency ( one of the acoustic parameter in bird's call) of males call changes as they age. Call data taken from particular age is dependent from its previous. My dependent variable is peak frequently of call at that particular age and independent variable is Age. In 2weeks I have taken 6 readings each day...so I am considering it age 1...then next 2 weeks I did same, that I called age 2...this goes on...I did same for all 10 birds till they died I want to look at peak frequency ( one of the acoustic parameter in bird's call) and see how it changes with age. Please suggest me suitable statistical method.
Till now, I have done Wilcoxin matched pairs test to see inter week  Significant differences. Is it right to do?

What is not clear from your question is if your objective is:

*

*Show peak frequency is dependent on age

*Show how peak frequency changes with age

One way to achieve both is using a regression model:
$ f_{peak} = w_1. age + w_2 .bird + \epsilon$,
where:

*

*bird is a categorical predictor that takes values $\{bird_1, bird_2,\ldots, bird_{10}\}$.

*$f_{peak}$ is the peak acoustic frequency for a bird

*$w_1, w_2$ are model parameters that you estimate using least squares.

*$\epsilon$ is the model error

You may need to transform $f_{peak}$ (see box-cox transforms) to get linearity.
HTH.
Edit: If a linear model is not a good fit, you can explore spline regression with GAMs so $f_peak = \beta_a s(age) + \beta_b s(bird) + \epsilon$ where $s(.)$ is a smoothing spline.
