When we use a z-test, we know the population mean and standard deviation. When we use a t-test, we do not know the population standard deviation and thus must estimate this using the sample data that we have collected. If you look at your z-table and t-table, tcrit for df(infinity) = zcrit because at df(infinity) we would have an entire population and no longer need an estimate.
no t test is similar to z test because t test ie used for unknown observation and z is for the medicne
t test, because the z test requires knowing the population standard deviation and that's rare. The t test embodies an estimate of the standard deviation.
I*I = x*x + y*y + z*z - t*t
a t test is used inplace of a z-test when the population standard deviation is unknown.
It depends on the population.Use t-test for a small population, N < 30; otherwiase, apply z-test or when N>=30.
You use a z test when you are testing a hypothesis that is using proportions You use a t test when you are testing a hypothesis that is using means
The formula is as follows:Because, in general, a zero-coupon bond price is...Z(t,T) = 1/[1+r(t,T)]TSO the spot rate would then equal...r(t,T) = [1/Z(t,T)1/T]-1
When the sample size is greater than 30
When the sample size is greater than 30
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The z score is calculated from the distance of a value from the distribution center divided by the standard dev. (x-xbar)/st. dev
Excel has a ZTEST function. It has the format ZTEST(array, µ0, sigma).