lecture 06 - raw.githubusercontent.com
TRANSCRIPT
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Lecture 0610.12.21
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Refresher QuizThe heights of men in a certain population follow a normal distribution with mean 69.7 inches and standard deviation 2.8 inches.a) If a man is chosen at random from the population, find the probability that he will be more than 72 inches tall.
b) If two men were chosen at random from the population, find the probability that (i) both of them will be more than 72 inches tall; (ii) their mean height will be more than 72 inches.
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The heights of men in a certain population follow a normal distribution with mean 69.7 inches and standard deviation 2.8 inches.a) If a man is chosen at random from the population, find the probability that he will be more than 72 inches tall.
b) If two men were chosen at random from the population, find the probability that (i) both of them will be more than 72 inches tall; (ii) their mean height will be more than 72 inches.
> pnorm(72, 69.7, 2.8, lower.tail = F) = 20.5%
P(both > 72 in tall) = P(> 72) * P(> 72) = 0.205 * 0.205 = 4.20%
> pnorm(72, 69.7, 2.8/sqrt(2), lower.tail = F) = 12.26%
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The normal distribution
N(μ, σ2)
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SamplesPopulation
Summary: sampling distribution
μ, σy, s
y, s
y, s
y, s
μY = μσY = σ/ n
Y(Distribution of )
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Statistical estimation
• We view our data as a random sample from a population and use the information about our data to infer facts about the population
• Goals:
• (1) Determine estimate of some feature of the population (i.e. mean)
• (2) Assess the precision of the estimate
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Statistical estimation
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Statistical estimation
Population
μσ
Sample
y s
14x
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Statistical estimation
Population
μσ
Sample 1
y1 s1
14x
Sample 2
y2 s2
14x
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μY
σY
Standard error of the mean is estimated from the sampling distribution
σY = σn
Standard deviation of sampling distribution
SEY = sn
Standard error of the mean
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Standard error of the mean is estimated from the sampling distribution
SEY = sn
Standard error of the mean
• Standard error of the mean (SE) is a measure of reliability or precision of the sample mean as an estimate of the population mean
• SE incorporates the two factors that influence reliability:
• Variability of observations (s)
• Sample size (n)
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Standard error of the mean is estimated from the sampling distribution
n = 14 y = 32.81cm2
s = 2.48cm2
SEY = sn
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Standard error of the mean is estimated from the sampling distribution
n = 14 y = 32.81cm2
s = 2.48cm2
SEY = 2.4814
= 0.66cm2What does the SE mean?
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Standard error of the mean is estimated from the sampling distribution
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Standard error of the mean is estimated from the sampling distribution
sSE
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Standard error (SE) versus standard deviation (SD)
SD = dispersion of data SE = unreliability in the estimate of the population mean
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When would we choose to plot SE v. SD?
SD = dispersion of data SE = unreliability in the estimate of the population mean
Do you want to compare means or summarize data variability?
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Standard error (SE) versus standard deviation (SD)
32.69 32.12 31.97
1.68 2.47 2.52
0.451 0.209 0.067
ys
SE
n = 14 n = 140 n = 1400 n → ∞y → μs → σ
SE → 0
y = 32.81cm2 s = 2.48cm2
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Standard error (SE) versus standard deviation (SD)
32.69 32.12 31.97
1.68 2.47 2.52
0.451 0.209 0.067
ys
SE
n = 14 n = 140 n = 1400 n → ∞y → μs → σ
SE → 0
y = 32.81cm2 s = 2.48cm2
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Standard error (SE) versus standard deviation (SD)
32.69 32.12 31.97
1.68 2.47 2.52
0.451 0.209 0.067
ys
SE
n = 14 n = 140 n = 1400 n → ∞y → μs → σ
SE → 0
y = 32.81cm2 s = 2.48cm2
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A pharmacologist measured the concentration of dopamine in the brains of eight rats. The mean concentration was 1,269 ng/gm and the standard
deviation was 145 ng/gm. What was the standard error of the mean?
Example
SE =s
n
SE =145
8= 51.2
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This quantity is a measure of the accuracy of the sample mean as an estimate of the population mean
Practice
This quantity tends to stay the same as the sample size goes up
This quantity tends to go down as the sample size goes up
Standard Error (SE) Standard Deviation (SD)
Standard Error (SE) Standard Deviation (SD)
Standard Error (SE) Standard Deviation (SD)
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This quantity is a measure of the accuracy of the sample mean as an estimate of the population mean
Practice
This quantity tends to stay the same as the sample size goes up
This quantity tends to go down as the sample size goes up
Standard Error (SE) Standard Deviation (SD)
Standard Error (SE) Standard Deviation (SD)
Standard Error (SE) Standard Deviation (SD)
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The confidence intervalThe woman’s position
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The confidence interval
The woman’s position
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The confidence interval
The woman’s position
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The confidence interval
Not likely, but possible
The woman’s position
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The confidence intervalThe woman’s position
μ y2(SE)
95% confidence interval of woman’s position = position of the dog +/- 2 x SE
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The confidence interval
95%
Sampling distribution of Y - random sample from normal distribution
y 2SE2SE
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Y ± 1.96 σn
Will contain for 95% of all samples
μ
The confidence interval
95%
Pr[−1.96 < Z < 1.96] = 0.95
Y − μσ/ n
But… we don’t know σ
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Student’s t distribution for confidence intervals
95%
Pr[−1.96 < Z < 1.96 = 0.95
Y − μσ/ n
Y ± 1.96 σn
Will contain mu for 95% of all samples
μ
But… we don’t know muσ
s
t0.025y
“Critical value”
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Student’s t distribution for confidence intervals
Standard normal
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Student’s t distribution for confidence intervals
Standard normaldf = 3
df = 10
Shape of distribution depends on degrees of freedom (df = n - 1)
Student’s t distribution is similar to normal distribution
but has a larger SD
y ± t0.025sn
“Critical value”
“Two-tailed 5% critical
value”
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Critical value and Student’s t distribution
95%
“Two-tailed 5% critical value” = Combined area above t and below -t = 5%“Two-tailed 5% critical value” = Area between two tails = 95%
“Two-tailed 5% critical
value”
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N-1: degrees of freedom explained
100-10 20 30
0-10 = -10 20-10 = 10
30-10 = 20-10-10 = -20
(-20) + (-10) + (10) + (20) = 0Sum of deviations is always zero!
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N-1: degrees of freedom explained
100-10 20 30
0-10 = -10 20-10 = 10
30-10 = 20-10-10 = -20
(-20) + (-10) + (10) + X = 0Sum of deviations is always zero!
Has to be +20 from mean…
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Calculating the confidence interval: butterflies
n = 14 y = 32.81cm2
s = 2.48cm2df = 13
y ± t0.025sn
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qt(p, df)
Critical value
Calculating the confidence interval: butterflies
n = 14 y = 32.81cm2
s = 2.48cm2df = 13
y ± t0.025sn
32.81 ± 2.16 2.4814
32.81 ± 1.43qt(0.975, 13)
(31.4,34.2)
95%
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Note: why use Student’s t distribution?
n = 14 y = 32.81cm2
s = 2.48cm2df = 13
y ± z0.025sn
32.81 ± 1.96 2.4814
32.81 ± 1.29 (31.51,34.11)
95%
Normal
Student’s t
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sSE
Calculating the confidence interval: butterflies
32.81 ± 1.43
CI
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Critical value and Student’s t distribution
2.5% 2.5%95%
−t0.025 t0.0250
y ± t0.025sn
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Critical value and Student’s t distribution
5% 5%90%
−t0.05 t0.050
y ± t0.05sn
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Calculating the confidence interval: butterflies
n = 14 y = 32.81cm2
s = 2.48cm2df = 13
y ± t0.05sn
90%
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Calculating the confidence interval: butterflies
n = 14 y = 32.81cm2
s = 2.48cm2df = 13
y ± t0.05sn
32.81 ± 1.77 2.4814
32.81 ± 1.17 (31.6,34.0)
The higher the confidence level, the wider the confidence interval
90%
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A pharmacologist measured the concentration of dopamine in the brains of eight rats. The mean concentration was 1,269 ng/gm and
the standard deviation was 145 ng/gm. Construct a 95% confidence interval for the population mean.
Example
SE =s
n
SE =145
8= 51.2
y ± t0.025sn
1269 ± (t0.025)(51.2)
(df = n - 1 = 7)
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qt(p = 0.025, df = 7, lower.tail = F)
qt(p = 0.975, df = 7, lower.tail = T)
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A pharmacologist measured the concentration of dopamine in the brains of eight rats. The mean concentration was 1,269 ng/gm and
the standard deviation was 145 ng/gm. Construct a 95% confidence interval for the population mean.
Example
SE =s
n
SE =145
8= 51.2
y ± t0.025sn
1269 ± (2.365)(51.2)1269 ± 121.1
(df = n - 1 = 7)
(1147.9,1390.1)
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One-sided confidence intervals
5%95%
t0.050
y + t0.05sn
“one-tailed 5% critical
value”
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y + t0.05sn
(df = n - 1 = 7)
One-sided confidence intervalsA pharmacologist measured the concentration of dopamine in the brains of eight rats after a treatment aimed to reduce dopamine.
The mean concentration was 1,269 ng/gm and the standard deviation was 145 ng/gm. Construct a 95% confidence interval for
the population mean after treatment.
We don’t expect dopamine to increase, so we can use a one-sided CI here with the upper
boundary fixed
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qt(p = 0.05, df = 7, lower.tail = F)
qt(p = 0.95, df = 7, lower.tail = T)
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y + t0.05sn
(df = n - 1 = 7)
1269 + (1.895)(51.2)
(∞,1366.0)
One-sided confidence intervalsA pharmacologist measured the concentration of dopamine in the brains of eight rats after a treatment aimed to reduce dopamine.
The mean concentration was 1,269 ng/gm and the standard deviation was 145 ng/gm. Construct a 95% confidence interval for
the population mean after treatment.
We don’t expect dopamine to increase, so we can use a one-sided CI here with the upper
boundary fixed
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One-sided confidence intervals
95%, two-sided
(1147.9,1390.1)(−∞,1366.0)
95%, one-sided
5%2.5%2.5%
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Confidence intervals and randomness
Population
μ = 25.4σ = 0.08
25.40 25.45 25.5025.3525.30
y = 25.419s = 0.085
y = 25.32s = 0.056
y = 25.39s = 0.091
y = 25.451s = 0.064
95% of the sample confidence intervals will
contain the true population mean
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Confidence intervals and randomness
Population
μ = 25.4σ = 0.08
y = 25.419s = 0.085
y = 25.32s = 0.056
y = 25.39s = 0.091
y = 25.451s = 0.064
25.40 25.45 25.5025.3525.30
95% of the sample confidence intervals will
contain the true population mean
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Confidence intervals and randomness• Larger samples produce narrower confidence intervals
• Because SE is smaller (divide by square root of n)
• A confidence interval can be interpreted as a probability… with caution!
• Pr{a sample will give us a CI that contains the true mean} = 0.95
• Pr{the true mean is within our CI} = 0.95
• An individual statement can be TRUE or FALSE, but if you create numerous statements, one statement will be TRUE 95% of the time
• “We are 95% confident that the true mean is between X and X”
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Confidence intervals and randomness
Pr[Y = 2] =16
Pr[5 = 2] ≠16
Y = 5
Pr{a sample will give us a CI that contains the true mean} = 0.95
Pr{the true mean is within our CI} = 0.95
Pr{31 < < 34} 0.95μ ≠
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Confidence intervals and randomness• Larger samples produce narrower confidence intervals
• Because SE is smaller (divide by square root of n)
• A confidence interval can be interpreted as a probability… with caution!
• Pr{a sample will give us a CI that contains the true mean} = 0.95
• Pr{the true mean is within our CI} = 0.95
• An individual statement can be TRUE or FALSE, but if you create numerous statements, one statement will be TRUE 95% of the time
• “We are 95% confident that the true mean is between X and X”
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A pharmacologist measured the concentration of dopamine in the brains of eight rats. The mean concentration was 1,269 ng/gm and
the standard deviation was 145 ng/gm. Construct a 95% confidence interval for the population mean.
SE =s
n
SE =145
8= 51.2
y ± t0.025sn
1269 ± (2.365)(51.2)1269 ± 121.1
(df = n - 1 = 7)
(1147.9,1390.1)
We are 95% confident that the mean concentration of dopamine of all rats is between 1,147.9 and 1,390.1 ng/gm
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Assumptions for estimating confidence intervals
• Conditions on the design of the study:
• (1) Data is a random sample from a large population
• (2) Observations in the sample must be independent of each other
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What does it mean for samples to be independent of each other?
#1 #2 #3
n = 3? n = 9?
Still a good experimental
design! Means > individual sample
“Hierarchical data structures”
“Biological replicates”
“Technical replicates”
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What does it mean for samples to be independent of each other?
A B C
“Hierarchical data structures”
How can we know for SURE the difference between A, B, and C is due to treatment and not due to difference between flasks?
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Assumptions for estimating confidence intervals
• Conditions on the design of the study:
• (1) Data is a random sample from a large population
• (2) Observations in the sample must be independent of each other
• Conditions on the form of the population distribution
• (3) If n is small, the population distribution must be ~normal
• (4) If n is large, the population distribution doesn’t have to be normal
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Why does it matter if our population is normally distributed?
Is the mean even a meaningful measure for
this population?
If n is large enough, the sample distribution will be normal regardless
of the shape of the population
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How can we tell if a population is normally distributed?
• Plot distribution (i.e. histogram)
• Every analysis should begin with an inspection of the data and the points that lie far from the center
• Quantile plot
• Shapiro-wilks test for non-normality
• If not normal distribution, try a data transformation
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Announcements
• Extra practice problems from the textbook posted to GitHub and Canvas
• Note: no solutions, but use your classmates/TAs for help!
• If you need help keeping track of the stats R functions we have been using, check out the stats_R_cheatsheet.md on GitHub!