apstat unit 4a inference part 1. apstat chapter 18 sampling distributions and sample means
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APSTAT UNIT 4A
INFERENCE PART 1
APSTAT Chapter 18
Sampling Distributionsand Sample Means
Lets Just DO IT!!!!
1 2 3 4 5 6 7 8 9 10 11 12 13
.60 .65 .55 .70 .85 .80 .75 .70 .65 .75 .80 .85 .75
Proportion of correct answers on last AP Stat Exam
Regular Old Distribution:
.55-.59 .60-.64 .65-.69 .70-.74 .75-.79 .80-.84 .85-.89
Lets Just DO IT!!!!
1 2 3 4 5 6 7 8 9 10 11 12 13
.60 .65 .55 .70 .85 .80 .75 .70 .65 .75 .80 .85 .75
Proportion of correct answers on last AP Stat Exam
• Now, Everyone take a 5-person random sample
• Do randint(1,13,5) to choose your subjects
• Add their scores and divide by 5 to get x-bar (sample mean)
• Now we will do a distribution of our sample means – a SAMPLING DISTRIBUTION!!!!!
Lets Just DO IT!!!!
1 2 3 4 5 6 7 8 9 10 11 12 13
.60 .65 .55 .70 .85 .80 .75 .70 .65 .75 .80 .85 .75
Proportion of correct answers on last AP Stat Exam
Class Sample Means:
.55-.59 .60-.64 .65-.69 .70-.74 .75-.79 .80-.84 .85-.89
Sampling Distribution
We Just DID IT!!!!
Give me at least 2 things that are different between the regular distribution and the sampling distributions:
1.
2.
3.
4.
5.
Bias
Unbiased StatisticMean of Sampling distribution should
equal True population mean. How did ours look earlier? The true
mean of the population was about 72.3….
Sample Proportions
Mean of Sample Proportion In last section:
Proportion is just outcome divided by n, so….
ˆ
ˆ
p
p
np
np pnp
μ
μ
μ
=
= =
=
Standard Deviation of Sample Proportion In last section:
Proportion is just outcome divided by n, so throw down a little Algebra….
ˆ 22
ˆ
(1 )
(1 ) (1 ) (1 )
(1 )
X
p
p
np p
np p np p np p
n nn
p p
n
σ
σ
σ
= −
− − −= = =
−=
Now try it…Sample Proportions Find mean and standard deviation for:
60 samples of 10 coin flips, p=.5
60 samples of 50 coin flips, p=.5
What does this say about variability in regards to sample size?
2 Rules of Thumb - Assumptions/Conditions Population size large enough
Population should be at least 10 times the sample size
10% Condition Normalness
n should be large enough to produce an approximately normal sampling distribution.
np > 10 AND n(1-p) >10
Try them out
A San Jose firm decide to sample 25 residents to determine if they oppose off-shore oil drilling. They predict that P(oppose) = 0.4Large enough population?
Normalness?
Example….
If the true percentage of students who pass the APStat exam is .64, what is the probability that a random sample of 100 students will have at least 70 students pass?
=.64, n=100, 70 or more
Check Conditions - Briefly Explain10%np and n(1-p) > 10
Draw Picture- (Find SD too)
Find P-Value
Conclusion
Same Problem Data…
Within what range would we expect to find 95% of sample proportions of size 100.
Sample Means
Take a whole bunch of samples and find the means
Why sample means? Remember our sample of class
scores?Less variableMore normal
Mean and Standard Deviation of X-BAR If we take all the possible samples from a
population, the mean of the sampling distribution will equal the population mean (if the population mean was accurate in the first place, but more on that later)
Xx μ=
Mean and Standard Deviation of X-BAR Standard Deviation of a sampling
distribution is:
22
22
s n
s n
sn
σ
σ
σ
=
=
=
Let’s try it!
If adult males have height N(68,2) what would be the mean and standard deviation for the distribution if:n=10n=40
What happened to the Standard deviation when n was quadrupled?
What would happen to the standard deviation if n was multiplied by 9?
CLT – The Central Limit Theorem If the population we are sampling from is
already normal with N(μσ), the sampling distribution will be normal as well with mean μ and standard deviation
But what if the population we are sampling from is not normal?
nσ
Age of Pennies
Riebhoff has 50 pennies, he took the current year and subtracted it from the date on the penny to obtain the following data…
Penny Ages
1 0 11 11 21 2 31 2 41 3
2 1 12 1 22 8 32 1 42 28
3 2 13 12 23 9 33 7 43 7
4 4 14 2 24 5 34 3 44 4
5 14 15 5 25 15 35 17 45 7
6 3 16 5 26 6 36 1 46 4
7 3 17 6 27 0 37 19 47 0
8 7 18 0 28 4 38 4 48 0
9 8 19 1 29 1 39 23 49 10
10 0 20 0 30 1 40 2 50 5
Sample Size n=1
0
1
2
3
4
5
6
7
0 3 6 9 12 15 18
# 0f Pennies
Sample Size n=4(everyone do 3 SRS)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
Sample Size n=8(everyone do 3 SRS)
0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19
What happened?
The distribution got “normaler” as the sample size increased. Cool?
Central Limit Theorem says that even if a distribution is not normal, the distribution of the sampling distribution will approach normalcy when n is large.
Allows us to use z-scores and such, even when the larger population is not normally distributed.
Assumptions/Conditions
Random Sample - Always describe Independence - Describe 10% Condition
Try it
If the APSTAT EXAM 2005 had a mean score of 3.2 with a standard deviation of 1.2…Old Skool - Find the probability that a
single student would have a score of 4 or higher?
New Skool – find the probability that an SRS of 20 students would have a score of 4 or higher?
Old Skool - Find the probability that a single student would have a score of 4 or higher?
New Skool – find the probability that an SRS of 20 students would have a score of 4 or higher?
Check Conditions - Briefly Explain10%Independence and Random Sample
Draw Picture- (Find SD too)
Find P-Value
Conclusion
Standard Error
Sometimes we do not have the population standard deviation. If we have to estimate it, we call it Standard Error and roll an SE.
APSTAT Chapter 19
Confidence Intervals for Proportions
Confidence Interval for a Proportion (aka One-Proportion Z-Interval)
At Woodside High, 80 students are surveyed and 32% of them had tried marijuana.
How confident am I that the true proportion of WH students that have tried marijuana is at or near 32%?
CONFIDENCE INTERVAL!!!
The Dealio…
If I do know the population mean• If I sample, I know the sample mean might be quite
different than the population mean
BUT…That difference is predictable. For instance, if N(0.70,0.1) and n=4
Sample Mean = 0.70, Sample SD=0.1/sqrt4=.05 We expect 95% of samples (Empirical Rule) to
fall between 2 SD of the mean Therefore 95% of samples will fall between 0.6
and 0.8.
Confidence Intervals
Work in reverse (From Woodside High Example) I
sampled 80 and got sample p = .32 I want to know the true population
proportion. The true population proportion will lie
within 2SD of the Sample Proportion in 95/100 samples of this size.
Let’s Do It!!!!
Do It!
List what you knowp-hat=.32, n=80
Conditions/Assumptions10% for Independence
• Woodside HS has over 800 studentsnp and nq > 10 to use Normal Model
• Both .32 x 80 and .68 x 80 > 10 Find Standard Error
SE(p-hat)=
Do It!
Draw the Picture
Conclusion:We are 95% confident that the TRUE
mean proportion of ____________ falls between ____ and ____
Do It!
We can also write confidence intervals in the form:
(estimate) ± (margin of error)
ˆ ˆ(1 )ˆ *
p pp z
n
−±
Standard error
What Does 95% Confidence Mean?
If we did a whole bunch of confidence intervals at this sample size, we would expect 95 out of 100 intervals to contain the true mean.
Picture of this:TRUE POPULATION
PROPORTION
AHOY!
We do not always want 95% confidence. Example, if a part on an airplane’s landing
gear needs to be a certain size to work, wouldn’t you want a little more confidence in the sample being within certain parameters?
Common Intervals are 90, 95 and 99% Denote as C=.90, C=.95, or C=.99
But 90 and 99% aren’t Empirically Cool
We need this z-score! It’s critical! So critical, it is called the critical value and denoted as z*
Area = 90%
p
Mas z*
Now check t distribution critical values chart (back of book or formula sheet)
Look at bottom. It gives you C and right above it is…..
Yeah!
Try it!
A poll asked who would you vote for if an election were held today between Sen. Barack Obama and Sen. John McCain. 115 of the 250 respondents chose Sen. McCain. Construct and interpret a 90% confidence interval for the proportion of voters choosing McCain.
Try It!
Conditions:
Mean, SE, z*
Calculate CI
Conclusion
Last thing
Finding sample size needed for a CI with a given level of confidence and a given margin of error
NBC News is doing a poll on who will be the next Governor of California. The want a 3% margin of error at a 95% confidence interval. What sample size should they use?
Sample size needed
ˆ ˆ(1 )ˆ *
p pp z
n
−±
Margin of Error
Sample size needed
ˆ ˆ(1 )* .03
0.5(1 0.5)1.960 .03
.251.960 .03
p pz
n
n
n
−=
−=
=( )2
.251.960 .03
1.960 0.5
.03
32.67
1067
n
n
n
n
=
=
=
=
g
Why 0.5? Gives us largest n value. Safety First!
OOPS! YOU SHOULD ALWAYS ROUND UP TO STAY WITHIN CONFIDENCE INTERVAL! SHOULD BE 1068.
APSTAT Chapter 20
One ProportionHypothesis Tests
Significance Tests
Example. AP Stat Exam 2005: National Proportion Who Passed = .58 Priory Students n = 32, p-hat=.78
Two Possibilities Higher WPS proportion just happened by
chance (natural variation of a sample) The likelihood of 78% of 32 students passing
is so remote we must conclude that Priory Students are likely better at APStat than national average.
Hypothesis Testing
Reflect our two possibilities from above: NOTHING IS STRANGE (difference could
have been by natural variation of sample) SOMETHIN’ IS GOIN’ ON (difference is so
improbable we must assume there is a difference)
Here is how we write them: H0: Null Hypothesis (Nothing Strange) Ha: Alternative Hypothesis (Somethin’ is
goin’ on)
In our WPS SAT Example
In practice, we describe the hypotheses in both symbols and words H0: p = .58, Priory students perform at the
same level as the National Average Ha: p > .58, Priory students perform better
than the National Average We will perform test(s) that give evidence
against the H0 (kinda like a trial)
What to do with the Hypothesis… After we conduct a test we will have
evidence based on our understanding of probability and sample variation. With this info we can: Reject H0 in favor of Ha
• if there is SIGNIFICANT evidence that the result did not likely happen by chance variation.
Fail to Reject H0
• if there is not enough evidence to reject it. The variation could likely have happened by chance
Be Carefull…
Notice we NEVER, NEVER, NEVERAccept either HypothesisSay one or the other is true or false
We only have evidence, we could still be wrong….
BUT….the stronger the evidence the more confident we can be!
Where do we get evidence?
One way, P-value from a z-score. What is the probability that this event happened given the population mean, standard deviation and # in our trial?
Our old friend, the z-score: We are using a sample here, so we throw in our sample standard deviation.
ˆ
(1 )
p pz
p pn
−=
−
Let’s Do It! WPS SAT Example Step 1 Define Parameter:
p the true passing proportion of WPS APstat test-takers
Step 2 Hypotheses H0: p= .58, Priory students perform at the
same level as the national proportion Ha: p> .78, Priory students perform better
than the national proportion
WPS SAT Example Continued Step 3 Assumptions:
SRS• No, but we will assume WPS Students
are a representative sample of the population of all AP Stat test-takers.
Independence• Priory sample of 32 is less than 10%
of population of AP Stat test-takers
• .58(32) and .42(32) both > 10
WPS SAT Example Continued Step 4 Name Test and DO IT
One Sample Z-Test for a Proportion
ˆ
(1 )
p pz
p pn
−=
−
WPS SAT Example Continued Step 5 P-value and sketch of normal curve:
P(z> 2.31)= .01053
Step 6 Interpret P-value and Conclusion A P-Value of .01053 indicates that there is about a
1 in 100 chance that a result this distant from the p happened merely by chance. Therefore, reject H0 in favor of Ha. It is very likely that WPS students performed far better on average than the National Average on the 2005 APStat exam
.58 .78
PHAT-PI (MUCH LOVE TO AL YOUNG)
P - Parameters (What are we studying) H - Hypothesis (In words and symbols) A - Assumptions (depends on type of test) T - Test (Name it. Do it.) P - P-Value (Calculate it-Draw it) I - Interpret (Reject/Fail to Reject, Why, ATQ)
Alternative Hypotheses
Can be: Greater Than (Ha ------>blah)
Less Than (Ha --------<blah)
Not (Ha --------≠blah)Double your one-sided P-value
μ0
μ0
μ0
On TI-83
Still have to do all of Phat Pi, but helps with calculations.
Stat>Test>1-PropZTestp0 - Population proportionx - successes in sample n(p-hat)n - sample size
Do it for AP Stat Example
Defective Products
A company claims that just 3% of its products are defective. A simple random sample of 400 of their products yielded 14 defective items. Do these sample data suggest that the company’s claim is too low?
PHAT-PI
P
H
A
PHAT-PI
T
P
I
How Much Evidence?
GTang (and many texts) give a rule of thumb of 5%. If there is a 5% probability or less that the outcome would happen by chance, you can throw down the “enough evidence to reject H0…”
If it is 1% or less, you can throw down the “very strong evidence against H0. Reject H0 in favor…”
Significance level…
Sometimes a problem will specify a certain amount of evidence that is needed.
= Significance Level Usually = 0.05 or 0.01 Basically, your P-value must be below that
level to reject the null hypothesis. Example your p-value is .03 and = 0.05
Be careful with one and two-sided alternatives and significance levels Your p-value doubles in a 2-sided.
APSTAT Chapter 21
More Stuff About Hypothesis Tests
Great Chapter
Make sure you read it Important concepts:
What a Null Hypothesis is and isn’tWhat P-Value MeansSignificance () LevelCritical Value - One v. Two sidedConfidence Intervals and Tests of
Significance - Relationship Between
Great Chapter…But….
Goes further than you need in explaining:Types of Error
• Type I• Type II
Power
Errors - Can We Make Mistakes?
Sure, Rejecting a “Good” Shipment For Example, I need batteries that work 99% of
the time. My significance test of a sample from a battery shipment tells me to “reject” the shipment, but it is actually ok.
We Can also Fail to Reject a “Bad” Statement If I had accepted a shipment that was actually bad
because my sample proportion ended up close to the mean I was looking for.
Which of these is worse in real life?
Errors
Type I – Reject H0 when it is actually true Usually not so bad Rejecting a “good” shipment Probability is equal to
Type II – Failing to Reject H0 when it is actually false Usually bad Accepting a “bad” shipment Probability () is a bear to calculate
• Check book to see how! Ooooo, fun!• Be happy you will NEVER be asked to do it
Errors - #2
Decrease both Type I and II errors by:Increasing n
Decrease Type II Errors by:Increasing
• You end up rejecting more/failing to reject less
• Causes an increase in Type I errors
POWER
Basically, how sure we are that we will not get a Type II error
Power = 1 – P(Type II) OR Power = 1 - P() Never will you be asked to compute (unless
the probability of a type II error is given) Increase Power by:
Increasing n (Sample size) Increase (say from .01 to .05)
Power and Error Wrap
What you have to know:Explain Power, Type I, and Type II
errors in context of the problem.Calculate P(Type I error) given How to Decrease:
• Type I Error• Type II Error
How to increase Power
APSTAT Chapter 22
Two Proportion Hypothesis Tests
Let’s Hop Right In…
A recent report found that men wash their hands 75% of the time after using the restroom and women 85% of the time. If SRS’s of 1200 men and 1100 women were surveyed, can we statistically say there is a significant difference between hand washing habits of men and women?
Handwashing
Parameter (group 1=female, 2=male)1-p2: Difference between female and male
hand washing proportions Hypotheses
H0: p1-p2=0 No difference in hand washing
Ha: p1-p2≠0 Is a “ “ “ “
Handwashing
AssumptionsSRS’s YepIndependent samples Safe to Assumen1p1>5 and n1(1-p1)>5
n2p2>5 and n2(1-p2)>5 YepPopulation 10X Sample
Yep
Handwashing
Test – Two Sample Proportion Z-Test
1 2
1 2
ˆ ˆ( ) .85 .755.965
1 11 1 .7978(1 .7978)ˆ ˆ(1 )1100 1200
p pz
p pn n
− −= = =
⎛ ⎞ ⎛ ⎞− +− + ⎜ ⎟⎜ ⎟ ⎝ ⎠⎝ ⎠
POOLED 1 2
1 2
935 900ˆ .7978
1100 1200
x xp
n n
+ += = =
+ +
Pool if variances are equal (since our null theorizes that the populations – and thus the variances - are equal)
Handwashing
P-Value2*P(Z>5.965)=Really Really Really Small
InterpretationP Value is so small, there is VERY
significant evidence against the assumption that males and females wash hands at the same proportion. Reject Null Hypothesis in favor of the Alternative. Males and females almost assuredly have different hand washing proportions.
Pooled vs. Non-Pooled
( )1 2 1 2
1 1 2 2
1 2
ˆ ˆ( )
ˆ ˆ ˆ ˆ(1 ) (1 )
p p p pz
p p p pn n
− − −=
− −+
•Use Pooled when you hypothesize populations have the same variance (in proportions, the same p = same variance)
• Use Non-pooled when populations are likely to have separate variances. (If your null shows a non-zero difference)
Confidence Interval
( ) 1 1 2 21 2
1 2
ˆ ˆ ˆ ˆ(1 ) (1 )ˆ ˆ *
p p p pp p z
n n
− −− ± +
Use Non-Pooled because there is no null to test for.
So, To Review…
PhatPi is on, but with these changes: P Parameter of interest is now the
difference between ___ and ___ H H0: p1=p2 (or p1-p2=0)
Ha: p1>p2 (or p1-p2>0)
or Ha: p1<p2 (or p1-p2<0)
or Ha: p1≠p2 (or p1-p2 ≠ 0)
Plus, you have to choose Pooled v. Non-Pooled (Pooled if Null is p1=p2)
Using TI 83
Stat>Test>2-PropZTest Can Also Do Interval:
Stat>Test>2-PropZInt• Put in C-Level (usually .9, .95, or .99)
Let’s do one!
Some scientist suggest that sickle-cell traits protect against malaria. A study in Africa tested 543 for sickle-cell trait and also for malaria. In all, 136 of the children had sickle-cell trait and 36 of these had malaria. The other 407 children lacked the sickle-cell trait and 157 of them had malaria. Is there evidence that malaria infection is lower among children with the sickle-cell trait.
Malaria v. Sickle Cell
P
H
A
Malaria v. Sickle Cell
T
P
I
Do Using a 95% C-Interval
Assumptions:
Interval Calculation
Interpretation
That is it!
Just one section left to go!
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