- #1
andyb177
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Going over my Lecture Notes my Lecturer as Started with
Show that a Gaussian Distribution Corresponds to a CTS random variable.
Then she has
i) Taken the f(x) = [p.d.f] and shown a) f(x) >= 0 for all x member of real numbers. b) Integral over all real numbers = 1
ii) Found the M.G.F then taken the first two derivatives of MGF and calculated variance.
iii) Taken two independent Gaussians and taken a linear combination i.e. aX+bY and found a new mean and variance.
My Problems are.
1) How does this shove the initial problem? (this is my only stats module and is this ticking off a definition?)
2) Why Calculate the Variance from the M.G.F
3) What does finding the new mean and variance achieve in case iii)
This is a bit of a complicated question any help would be really appreciated.
Thanks.
Show that a Gaussian Distribution Corresponds to a CTS random variable.
Then she has
i) Taken the f(x) = [p.d.f] and shown a) f(x) >= 0 for all x member of real numbers. b) Integral over all real numbers = 1
ii) Found the M.G.F then taken the first two derivatives of MGF and calculated variance.
iii) Taken two independent Gaussians and taken a linear combination i.e. aX+bY and found a new mean and variance.
My Problems are.
1) How does this shove the initial problem? (this is my only stats module and is this ticking off a definition?)
2) Why Calculate the Variance from the M.G.F
3) What does finding the new mean and variance achieve in case iii)
This is a bit of a complicated question any help would be really appreciated.
Thanks.