I need a coder who is fluent in MATLAB and spectral estimations/DSP applications on it

Use MATLAB throughout, but do NOT use
any built-in functions for the spectral estimates.
a) Generate a 100-point Gaussian random sequence with zero-mean and unitvariance. Compute and plot the periodogram using dB scaling. How does the
periodogram compare to the true PSD of the Gaussian white noise process?
Comment on the variability of the PSD estimate.
b) Repeat part (a) using a 200-sample sequence from the process. Does the increase in the
number of samples result in a decrease in the estimator variance?
c) Repeat part (a) using a 1000-sample sequence. Does this improve the PSD estimate?
d) The variance itself is a random process. Using your code form parts (a) through (c) run
the experiment 1000 times. Compute the variance directly on the resulting periodogram
and record it for the 100-sample, 200-sample, and 1000-sample sequences for each of
the 1000 trials. Compute and plot histograms of the variances you computed. What
observations can you make regarding the distribution of the variance itself as the length
of the sequence is increased?
e) Repeat part (d) using 10,000 trials. What can you observe about the distribution of the
variance? Try 100,000 trials and observe the effect on the histograms. What observations
can you make?
f) Estimate the PSD from the 1000-point sequence of part (c) using the Welch method with
a segment length of 100 and no overlapping between segments. Use a rectangular
window on each segment. Repeat the experiment 1000 times again. Compare the
variability of the PSD estimate to that found in part (d).
g) Repeat part (f) using a triangular (Bartlett) window instead of the rectangular window.
How does this affect the PSD estimate?