15 - SE of the Mean, Type 1, Type 2 errors Flashcards
What condition makes sample means from a distribution normally distributed?
n > 10 |K4|
n = sample size
|K4| = kurtosis
- from the CLT, we know that under certain conditions (iid sampling) sample means from a distribution will be normally distributed
- use number of observations per sample, not the entire data set, to test this condition
Standard Error of the Mean
SE(Xbar)
- SD(Xbar) = SE(Xbar)
- we get this from the CLT, remember that
- Xbar100 ~ N(µ, σ2/n)
- µ = pop mean
- σ2/n = pop variance
- so, the SD of Xbar is σ/sqr(n)
- Xbar100 ~ N(µ, σ2/n)
sample means are less variable than raw data by a factor of _____
1/sqr(n)
UCL & LCL
UCL = µ + L
LCL = µ - L
*process in control if all x-bars are in this range*
Type 1 Error
if falling outside the control limits = positive,
Type 1 = a false positive
Choosing L
we want L such that when the process is in control,
P(µ-L <= Xbar <= µ+L) = High probability
→so there’s low prob of falling outside the limits and thus a low prob of Type 1 error
*if we want a 100a % chance of Type 1 error, then:
L = za/2SE(Xbar),
where za is the 100(1-a) percentile of the standard normal distribution*
Ex. find control limits with a 1% chance of producing a Type 1 Error
(if µ = 8, σ = 1.2, and 100 observations a day)
L = za/2SE(Xbar)
za/2 = z0.01/2 = z.005
(z.005 is the 99.5 percentile of the standard normal)
from z-tables, z0.005 = 2.5758
SE(Xbar) = 1.2/sqr(100) = 0.12
L = 2.5758 * 0.12 = 0.309
LCL = 8 - 0.309 = 7.691, UCL = 8 + 0.309 = 8.309
Xbar chart
when we track sample means
Type 2 errors
same as a false negative
claiming that the process is in control, when it isn’t
Type 1 and 2 in context of a production line
Type 1 = shut down line when you shouldn’t have
Type 2 = keep line going when you should’ve shut it down
Tradeoff between Errors
For a fixed sample size, if you want a smaller Type 1 error then you must accept a larger Type 2 error
S-chart
tracks sample SD’s over time