This would be the same as if population standard deviation was unknown and we were to use sample standard deviation, while n>30. Also in case n<30 and population standard deviation is known this formula is still valid given the underlying distribution of each sample observation is normal. c) Using item a) and the central limit theorem of sample proportion which is ~ N(0, 1) Vm/n Show that P(p – Za/2\ < p< p- %a/2 = 1- a Note that above is the confidence interval for population proportion; however, since it envolves the unkown population proportion p, sample proportion will be used instead. d) Where the students t distribution was discovered ? why it was called this way ? in what situation confidence intervals of population mean need to be made based on this distribution ? What was the application of it when it was discovered ? Which one is true: when its degree of freedom increases it diverges or converges to the normal distribution ?

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Author:Amos Gilat
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Chapter1: Starting With Matlab
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I am just needing help with part c and d

 

1. Construction of confidence intervals
a) Drawing picture show that if Z~N(0,1) (a random variable that is normally distributed with mean 0 and variance 1), then
P(-za/2 < Z < %a/2) = 1 – a
b) by Central limit theorem we know for n> 30 ;
X-H
Z =
~ N(0, 1)
plug in this Z in the formula of a) and show that
P(X – za/2"
Vn
<µ< X +%a/2)
= 1-a
Vn
Note that this is the formula for confidence interval of population mean when sample is large and population standard deviation is known.
This would be the same as if population standard deviation was unknown and we were to use sample standard deviation, while n>30.
Also in case n<30 and population standard deviation is known this formula is still valid given the underlying distribution of each sample observation is
normal.
TE
p– p
c) Using item a) and the central limit theorem of sample proportion which is
Show that
Z =
- N(0, 1)
P(p – 2
< p<p- Za/2
- Za/2\
= 1- a
n
Note that above is the confidence interval for population proportion; however, since it envolves the unkown population proportion p, sample
proportion will be used instead.
d) Where the students t distribution was discovered ? why it was called this way ? in what situation confidence intervals of population mean
need to be made based on this distribution ? What was the application of it when it was discovered ? Which one is true: when its degree of
freedom increases it diverges or converges to the normal distribution ?
Transcribed Image Text:1. Construction of confidence intervals a) Drawing picture show that if Z~N(0,1) (a random variable that is normally distributed with mean 0 and variance 1), then P(-za/2 < Z < %a/2) = 1 – a b) by Central limit theorem we know for n> 30 ; X-H Z = ~ N(0, 1) plug in this Z in the formula of a) and show that P(X – za/2" Vn <µ< X +%a/2) = 1-a Vn Note that this is the formula for confidence interval of population mean when sample is large and population standard deviation is known. This would be the same as if population standard deviation was unknown and we were to use sample standard deviation, while n>30. Also in case n<30 and population standard deviation is known this formula is still valid given the underlying distribution of each sample observation is normal. TE p– p c) Using item a) and the central limit theorem of sample proportion which is Show that Z = - N(0, 1) P(p – 2 < p<p- Za/2 - Za/2\ = 1- a n Note that above is the confidence interval for population proportion; however, since it envolves the unkown population proportion p, sample proportion will be used instead. d) Where the students t distribution was discovered ? why it was called this way ? in what situation confidence intervals of population mean need to be made based on this distribution ? What was the application of it when it was discovered ? Which one is true: when its degree of freedom increases it diverges or converges to the normal distribution ?
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