Can you guys help me with going through this question:
So to start off, is the null and alternative hypothesis.... H0 = 95 versus H1 < 95. Or is this wrong??
OR is it H0 < 95 versus H1 >= 95
Can you guys help me with going through this question:
So to start off, is the null and alternative hypothesis.... H0 = 95 versus H1 < 95. Or is this wrong??
OR is it H0 < 95 versus H1 >= 95
Last edited by Flop21; 18 Jun 2017 at 4:44 PM.
nvm
Last edited by Flop21; 18 Jun 2017 at 5:09 PM.
Last edited by InteGrand; 18 Jun 2017 at 4:45 PM.
Help pls someone,
So we have to use R commands instead of distribution tables (wtf it's so much easier with the tables)?
How would you then find the region of rejection (when doing a hypothesis test) for both z and t distributions?
E.g. for T test, I would find the degrees of freedom (n-1), and look up n-1 on the T table with the respective value of alpha, say 0.05. How would you do this in the test????
R's free to download. You can always just investigate briefly :P
Here's an example of something that might appear
Tbh all I've seen is p___, q___, r___ where ___ is a distribution.
p - probability
q - quantile
r - just a bunch of random simulations
Sxc avatar made by a sxc person: carrotontheground http://community.boredofstudies.org/...otontheground/
All this stat stuff looks so much more complicated than what I've done. Only done stat171 where had z, various t tests, and chi squared test for independence and GOF test... rip
2nd Year Applied Finance/Actuarial Studies @ MQ
GPA 6.67 WAM 87%
Part 1's:CT7
Sxc avatar made by a sxc person: carrotontheground http://community.boredofstudies.org/...otontheground/
Last edited by BenHowe; 29 Jun 2017 at 7:07 PM.
2nd Year Applied Finance/Actuarial Studies @ MQ
GPA 6.67 WAM 87%
Part 1's:CT7
I think it should be Ho: U = 95, Ha: U < 95
R output is mainly qnorm / pnorm / qt IIRC. Qnorm is % shaded eg qnorm(0.5) = 0 i.e. 50% shaded is at 0 on the normal distribution, etc. Pnorm is opposite so Pnorm(0.5) = the % shaded at z = 0.5 (or the pnorm/qnorm may have been other way around)
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