a review of key statistical concepts. an overview of the review populations and parameters samples...
TRANSCRIPT
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A review of key statistical concepts
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An overview of the review
• Populations and parameters
• Samples and statistics
• Confidence intervals
• Hypothesis testing
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Populations and Parameters
… and Samples and Statistics
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Populations and Parameters
• A population is any large collection of objects or individuals, such as Americans, students, or trees about which information is desired.
• A parameter is any summary number, like an average or percentage, that describes the entire population.
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Parameters
• Examples:– population mean µ = average temperature– population proportion p = proportion approving
of president’s job performance
• 99.999999999999….% of the time, we don’t (...or can’t) know the real value of a population parameter.
• Best we can do is estimate the parameter!
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Samples and Statistics
• A sample is a representative group drawn from the population.
• A statistic is any summary number, like an average or percentage, that describes the sample.
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Statistics
• Examples– sample mean (“x-bar”)– sample proportion (“p-hat”)
• Because samples are manageable in size, we can determine the value of statistics.
• We use the known statistic to learn about the unknown parameter.
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Example: Smoking at PSU?
Population of 42,000 PSU students
What proportion smoke regularly?
Sample of 987 PSU students
43% reported smoking regularly
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Example: Grade inflation?
Population of 5 million college
studentsIs the average GPA 2.7?
Sample of 100 college students
How likely is it that 100 students would have an average GPA as large as 2.9 if the population average was 2.7?
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Example: A linear relationship?
424140393837363534
3500
3000
2500
Gestation (weeks)
Birt
h w
eig
ht (
gra
ms)
S = 167.327 R-Sq = 77.5 % R-Sq(adj) = 76.8 %
Weight = -2037.00 + 130.817 Gestation
Regression Plot
Y-hat = a + b X
E(Y) = A + B X
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Two ways to learn about a population parameter
• Confidence intervals estimate parameters.– We can be 95% confident that the proportion of
Penn State students who have a tattoo is between 5.1% and 15.3%.
• Hypothesis tests test the value of parameters.– There is enough statistical evidence to conclude
that the mean normal body temperature of adults is lower than 98.6 degrees F.
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Confidence intervals
A review of concepts
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The situation
• Want to estimate the actual population mean .
• But can only get “x-bar,” the sample mean.• Use “x-bar” to find a range of values,
L<<U, that we can be really confident contains .
• The range of values is called a “confidence interval.”
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Confidence intervals for proportions in newspapers
• “Sample estimate”: 69% of 1,027 U.S. adults think using a hand-held cell phone while driving a car should be illegal.
• The “margin of error” is 3%.• The “confidence interval” is 69% ± 3%.• We can be really confident that between 66% and
72% of all U.S. adults think using a hand-held cell phone while driving a car should be illegal.
Source: ABC News Poll, May 16-20, 2001
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General form of most confidence intervals
• Sample estimate ± margin of error
• Lower limit L = estimate - margin of error
• Upper limit U = estimate + margin of error
• Then, we’re confident that the value of the population parameter is somewhere between L and U.
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(1-α)100% t-interval for population mean
nsx 1,
21 nt
Formula in notation:
Formula in words:
Sample mean ± (t-multiplier × standard error)
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Determining the t-multiplier
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1
2
2
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Typical t-multipliers
Conf. coefficient Conf. level
0.90 90% 0.95
0.95 95% 0.975
0.99 99% 0.995
21
%1001 1
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t-interval for mean in Minitab
One-Sample T: FVC
Variable N Mean StDev SE Mean 95.0% CI FVC 8 3.5875 0.1458 0.0515 (3.4655,3.7095)
We can be 95% confident that the mean forced vital capacity of all female college students is between 3.5 and 3.7 liters.
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Length of confidence interval
• Want confidence interval to be as narrow as possible.
• Length = Upper Limit - Lower Limit
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How length of CI is affected?
• As sample mean increases…
• As the standard deviation decreases…
• As we decrease the confidence level…
• As we increase sample size …
nsx t
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Hypothesis testing
A review of concepts
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General idea of hypothesis testing
• Make an initial assumption.
• Collect evidence (data).
• Based on the available evidence (data), decide whether to reject or not reject the initial assumption.
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Example: Normal body temperature
Population of many, many adults
Is average adult body temperature 98.6 degrees? Or is it lower?
Sample of 130 adults
Average body temperature of 130 sampled adults is 98.25 degrees.
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Making the decision
• It is either likely or unlikely that we would collect the evidence we did given the initial assumption.
• If it is likely, then we “do not reject” our initial assumption. There is not enough evidence to do otherwise.
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Making the decision (cont’d)
• If it is unlikely, then:– either our initial assumption is correct and we
experienced a very unusual event– or our initial assumption is incorrect
• In statistics, if it is unlikely, we “reject” our initial assumption.
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Again, idea of hypothesis testing: criminal trial analogy
• First, state 2 hypotheses, the null hypothesis (“H0”) and the alternative hypothesis (“HA”)
– H0: Defendant is not guilty (innocent).
– HA: Defendant is guilty.
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Criminal trial analogy (continued)
• Then, collect evidence, such as finger prints, blood spots, hair samples, carpet fibers, shoe prints, ransom notes, handwriting samples, etc.
• In statistics, the data are the evidence.
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Criminal trial analogy(continued)
• Then, make initial assumption.– Our criminal justice system is based on
“defendant is innocent until proven guilty.”– So, assume defendant is innocent.
• In statistics, we always assume the null hypothesis is true.
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Criminal trial analogy(continued)
• Then, make a decision based on the available evidence.– If there is sufficient evidence (“beyond a
reasonable doubt”), reject the null hypothesis. (Behave as if defendant is guilty.)
– If there is insufficient evidence, do not reject the null hypothesis. (Behave as if defendant is innocent.)
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Very important point
• If we reject the null hypothesis, we do not prove the alternative hypothesis is true.
• If we do not reject the null hypothesis, we do not prove the null hypothesis is true.
• We merely state there is enough evidence to behave one way or the other.
• Always true in statistics! Whatever the decision, there is always a chance we made an error.
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Errors in criminal trials
Truth
JuryDecision
Not guilty Guilty
Not guilty OK ERROR
Guilty ERROR OK
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Errors in hypothesis testing
Truth
DecisionNull
hypothesisAlternativehypothesis
Do notreject null
OKTYPE IIERROR
Reject nullTYPE IERROR
OK
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Definitions: Types of errors
• Type I error: The null hypothesis is rejected when it is true.
• Type II error: The null hypothesis is not rejected when it is false.
• There is always a chance of making one of these errors. But, a good scientific study will minimize the chance of doing so!
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Making the decision
• “It is either likely or unlikely that we would collect the evidence we did given the initial assumption.”
• Two ways to determine likely or unlikely:– Critical value approach (many textbooks)– P-value approach (science, journals, software)
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Possible hypotheses about mean µ
Type Null Alternative
Right-tailed
Left-tailed
Two-tailed
3:0 H
3:0 H
3:0 H
3:0 H
3:0 H
3:0 H
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Critical value approach
• Using sample data and assuming null hypothesis is true, calculate the value of the test statistic.
• Set the significance level, α, the probability of making a Type I error to be small (0.05 or 0.01).
• Compare the value of the test statistic to the known distribution of the test statistic.
• If the test statistic is more extreme than expected, allowing for an α chance of error, reject the null hypothesis. Otherwise, don’t reject the null.
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Right-tailed critical value
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1.7613
0.95
0.05
Reject null hypothesis if test statistic is greater than 1.7613.
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Left-tailed critical value
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t(14)
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nsity
-1.7613
0.05
0.95
Reject null hypothesis if test statistic is less than -1.7613.
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Two-tailed critical value
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t(14)
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0.025 0.025
-2.1448 2.1448
Reject null hypothesis if test statistic is less than -2.1448 or greater than 2.1448.
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P-value approach
• Using sample data and assuming null hypothesis is true, calculate the value of the test statistic.
• Using known distribution of the test statistic, calculate the P-value = “If the null hypothesis is true, what is the probability that we’d observe a more extreme test statistic than we did?”
• Set the significance level, α, the probability of making a Type I error to be small (0.05 or 0.01).
• If the probability is small, i.e., smaller than α, reject the null hypothesis. Otherwise, don’t reject the null.
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Right-tailed P-value
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0.9873
0.0127
If it’s unlikely to observe such a large test statistic, i.e., if the P-value (0.0127) is smaller than α, reject the null hypothesis.
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Left-tailed P-value
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t* = -2.5
0.0127
0.9873
If it’s unlikely to observe such a small test statistic, i.e., if the P-value (0.0127) is smaller than α, reject the null hypothesis.
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Two-tailed P-value
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t* = 2.5t* = -2.5
0.0127 0.0127
0.9746
If it’s unlikely to observe such an extreme test statistic, i.e., if the P-value (0.0254) is smaller than α, reject the null hypothesis.
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Example: Right-tailed test
Brinell hardness measurement of ductile iron subcritically annealed:170 167 174 179 179156 163 156 187 156183 179 174 179 170156 187 179 183 174187 167 159 170 179
170:
170:0
AH
H
One-Sample T: BrinellTest of mu = 170 vs mu > 170
Variable N Mean StDev SE Mean T PBrinell 25 172.52 10.31 2.06 1.22 0.117
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Example: Right-tailed critical value
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0.1
0.0
t(24)
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0.05
1.7109
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Example: Right-tailed P-value
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0.1
0.0
t(24)
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t* = 1.22
0.883
0.117
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Example: Left-tailed testHeight of sunflower seedlings.11.5 11.8 15.7 16.1 14.1 10.515.2 19.0 12.8 12.4 19.2 13.516.5 13.5 14.4 16.7 10.9 13.015.1 17.1 13.3 12.4 8.5 14.312.9 11.1 15.0 13.3 15.8 13.5 9.3 12.2 10.3
7.15:
7.15:0
AH
H
Test of mu = 15.7 vs mu < 15.7
Variable N Mean StDev SE Mean T PSunflower 33 13.664 2.544 0.443 -4.60 0.000
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Example: Left-tailed critical value
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0.4
0.3
0.2
0.1
0.0
t(32)
de
nsity
0.95
0.05
-1.6939
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Example: Left-tailed P-value
50-5
0.4
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0.2
0.1
0.0
t(32)
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-4.60
<0.0001
>0.9999
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Example: Two-tailed test
Thickness of spearmint gum.7.65 7.60 7.65 7.70 7.557.55 7.40 7.40 7.50 7.50 5.7:
5.7:0
AH
H
Test of mu = 7.5 vs mu not = 7.5
Variable N Mean StDev SE Mean T PGum 10 7.5500 0.1027 0.0325 1.54 0.158
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Example: Two-tailed critical value
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0.1
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t(9)
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0.95
0.025 0.025
2.2622-2.2622
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Example: Two-tailed P-value
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t(9)
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1.54-1.54
0.0790.079
0.842