3.11 Using Statistics To Make Inferences 3 Summary Review the normal distribution Z test Z test for...
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Transcript of 3.11 Using Statistics To Make Inferences 3 Summary Review the normal distribution Z test Z test for...
3.11
Using Statistics To Make Inferences 3
Summary
Review the normal distributionZ testZ test for the sample meant test for the sample mean
Tuesday 18 April 2023 08:41 PM
3.22
Goals To perform and interpret a Z test.To perform and interpret tests on the sample mean.To produce a confidence interval for the population mean.Know when to employ Z and when t.
Practical Perform a t test.
Perform a two sample t test, in preparation for next week.
3.33
Normal Distribution
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
-6 -4 -2 0 2 4 6
Series1
Series2
Series3
Series 1 2 3
μ 0 0 1
σ 1 ½ 1
Tables present results for the standard normal distribution (μ=0, σ=1).
3.44
Use of TablesProb(1≤z≤∞) = 68% of the observationsProb(-∞≤z≤-1) = lie within 1 standard deviation 0.16 of the mean Prob(1.96≤z≤∞) = 95% of the observations lieProb(-∞≤z≤-1.96) = within 1.96 standard
0.025 deviations of the mean
Prob(2.58≤z≤∞) = 99% of the observations lieProb(-∞≤z≤-2.58) = within 2.58 standard
0.005 deviations of the mean
3.55
Use of Tables
Z 0.00 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.08 -0.09
-1.0 0.159 0.156 0.154 0.152 0.149 0.147 0.145 0.142 0.140 0.138
-1.9 0.029 0.028 0.027 0.027 0.026 0.026 0.025 0.024 0.024 0.023
-2.5 0.006 0.006 0.006 0.006 0.006 0.005 0.005 0.005 0.005 0.005
Prob(1≤z≤∞) = Prob(-∞≤z≤-1) = 0.16
Prob(1.96≤z≤∞) = Prob(-∞≤z≤-1.96) = 0.025
Prob(2.58≤z≤∞) = Prob(-∞≤z≤-2.58) = 0.005
3.66
Testing Hypothesis
Null hypothe
sisH0
assumes that there is no real effect present
Alternate
hypothesis
H1
assumes that there is some
effect
3.77
Z Test
For a value x taken from a population with mean μ and standard deviation σ, the Z-score is
xz
3.88
The Central Limit Theorem When taking repeated samples of size n from the same population.
3. The distribution of the sample means approximates a Normal curve.
2. The spread of the distribution of the sample means is smaller than that of the original observations.
1. The distribution of the sample means is centred around the true population mean
3.99
Central Limit TheoremIf the standard deviation of the individualobservations is σ then the standard error of
the sample mean value is
For a sample mean, , with mean μ and
standard deviation the Z-score is
n
x
n
n
xz
3.1010
Example 1
mean score 100 standard deviation 16What is the probability a score is higher than 108?
5.0168
16100108
x
z
Prob(x≥108) = Prob(z≥0.5) = 0.309
Z 0.00 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.08 -0.09
-0.5 0.309 0.305 0.302 0.298 0.295 0.291 0.288 0.284 0.281 0.278
3.1111
Example 2
mean score 100 standard deviation 16 The sample mean of 25 individuals is found to be 110.
The null hypothesis, no real effect present, is that μ = 100. Wish to test if the mean significantly exceeds this value.
3.1212
Solution 2125.3
2.310
2516
100110 n
xz
Prob( ≥ 100) = Prob(z≥3.125) = 0.0009, beyond our basic table
x
Z 0.00 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.08 -0.09
-3.00 .001 .001 .001 .001 .001 .001 .001 .001 .001 .001
Since the p-value is less than 0.001 the result is highly significant, the null hypothesis is rejected. The sample average is significantly higher.
3.1313
Estimating The Population Mean
Confidence interval for the population mean
nzx
Sample mean
n Sample size
σ Population standard deviation (known)
Tabulated value of the z-score that achieves a significance level of α in a two tail test
z
x
Don’t forget to multiply or divide before you add or subtractThis test is not available in SPSS
3.1414
Estimating The Population Mean
Confidence interval for the population mean
nzx
Sample mean
n Sample size
σ Population standard deviation (known)
Tabulated value of the z-score that achieves a significance level of α in a two tail test
z
x
nzx,
nzx
We can be 100(1-2α)% certain the population mean lies in the interval
3.1515
Normal Values
Conf. level
Prob. α
One Tail
Zα
90% 0.05 1.645
95% 0.025 1.960
99% 0.005 2.576Z 0.00 -0.01 -0.02 -0.03 -0.04 -0.05 -0.06 -0.07 -0.08 -0.09
-1.6 0.055 0.054 0.053 0.052 0.051 0.049 0.048 0.047 0.046 0.046
-1.9 0.029 0.028 0.027 0.027 0.026 0.026 0.025 0.024 0.024 0.023
-2.5 0.006 0.006 0.006 0.006 0.006 0.005 0.005 0.005 0.005 0.005
Notation commonly used to denote Z values for confidence interval is Zα where 100(1 - 2α) is the desired confidence level in percent.
3.1616
Example 3
standard deviation 16mean of a sample of 25 individuals is found to be 110
Require 95% confidence interval for the population mean
110x 25n 16 96.1z
3.1717
Solution 3
110x 25n 16 96.1z
]272.116 ,728.103[25
1696.1110 n
zx
95% sure the population mean lies in the interval [103.7,116.3]
3.1919
If The Population Standard Deviation Is Not Available?
t values
ns
tx )( with ν = n – 1 degrees of freedom(ν the Greek letter nu)
Sample mean
n Sample size
ν Degrees of freedom, n-1 in this case
s Sample standard deviation
αProportion of occasions that the true mean lies outside the range
tν Critical value of t from tables
Don’t forget to multiply or divide before you add or subtract
xNote in this module, typically, the sample variance is required. Divide by n-1
3.2020
If The Population Standard Deviation Is Not Available?
t values
Sample mean
n Sample size
ν Degrees of freedom, n-1 in this case
s Sample standard deviation
αProportion of occasions that the true mean lies outside the range
tν Critical value of t from tables
x
n
s)(tx,
n
s)(tx nn 11
3.2121
Two Tail t
To obtain confidence limits a two tail probability is employed since it refers to the proportion of values of the population mean, both above and below the sample mean.
3.2222
Example 4
An experiment results in the following estimates.
Obtain a 90% confidence interval for the population mean.
4.71x20n 344.7s
3.2323
Example 4
Given4.71x 20n 344.7s 729.1)05.0(19 t
ν p=0.05 p=0.025 p=0.005 p=0.0025 p=0.0025
19 1.729 2.093 2.861 3.174 3.174
]239.74,561.68[20344.7
729.14.71 ns
tx )(
We can be 90% (α=0.05) sure that the population mean lies in this interval [68.6,74.2].
3.2525
Interpreting t-values
If tcalc<tν(α) then we cannot reject the null hypothesis that μ=m.
If tcalc>tν(α) the null hypothesis is rejected, the true mean μ differs significantly at the 2α level from m.
3.2626
Example 5
Claimed mean is 75 seconds, the times taken for 20 volunteers are
72 64 69 82 7670 58 64 81 7571 76 60 78 6465 73 69 84 77
H0: there is no effect so μ = 75H1: μ ≠ 75 (two tail test)
3.2727
Solution 5
n = 20 Σx = 1428 Σx2 = 102984
72 64 69 82 7670 58 64 81 7571 76 60 78 6465 73 69 84 77
n = 20 Σx = 72 + 64 + … + 84 + 77 = 1428Σx2 = 722 + 642 + … + 842 + 772 = 102984
3.2929
Solution 5
40.7120
1428... 121
n
x
n
xxxx
n
ii
n
n = 20 Σx = 1428 Σx2 = 102984
s = 7.34
9368.53120
142820
1102984
1
1
var
2
2
11
2
n
xn
x
x
n
ii
n
ii
Note in this module, typically, the sample variance is required. Divide by n-1
3.3030
Solution 520n 40.71x 34.7s
193.2
2034.7
7540.71 n
sx
t
861.2)005.0(19 t 093.2)025.0(19 t
ν p=0.05 p=0.025 p=0.005 p=0.0025
p=0.0010
19 1.729 2.093 2.861 3.174 3.579
In an attempt to “estimate” p.
3.3131
Conclusion 5
Since 2.093<2.193<2.8610.01<p-value<0.05
(note 2α since two tail)
861.2)005.0(19 t 093.2)025.0(19 tt = 2.193
There is sufficient evidence to reject H0 at the 5% level. The experiment is not consistent with a mean of 75.In fact the 95% confidence interval is [68.0,74.8] which, as expected, excludes 75.
The precise p value may be found from software.
3.3333
SPSS 5Basic descriptive statistics for a manual test
One-Sample Statistics
20 71.40 7.344 1.642V1N Mean Std. Deviation
Std. ErrorMean
3.3434
SPSS 5As predicted 0.01 < p-value < 0.05
One-Sample Test
-2.192 19 .041 -3.600 -7.04 -.16V1t df Sig. (2-tailed)
MeanDifference Lower Upper
95% ConfidenceInterval of the
Difference
Test Value = 75
The confidence interval is 75-7.04 to 75-0.16 that is [67.96, 74.84].
3.3737
Example 6
Experimental data 0.235 0.252 0.312 0.2640.323 0.241 0.284 0.3060.248 0.284 0.298 0.320
Test whether these data are consistent with a population mean of 0.250.
H0 is that μ = 0.250
3.3838
Solution 612n 2806.0x 0318.0s
333.3
120318.0
250.02806.0 n
sx
t
ν p=0.05 p=0.025 p=0.005 p=0.0025
p=0.0010
11
1.796 2.201 3.106 3.497 4.025
t11(0.005)=3.106 t11(0.0025)=3.497
In an attempt to “estimate” p.
3.3939
Conclusion 6106.3)005.0(11 t t = 3.333
Since 3.106 < 3.333 < 3.4970.005 < p-value < 0.01
There is sufficient evidence to reject H0 at the 1% level.
The experimental mean would not appear to be consistent with 0.250
497.3)0025.0(11 t
3.4040
SPSS 6
One-Sample Test
3.333 11 .007 .030583 .01039 .05078V1t df Sig. (2-tailed)
MeanDifference Lower Upper
95% ConfidenceInterval of the
Difference
Test Value = 0.250
As predicted p-value < 0.01
The confidence interval is 0.250+0.010 to 0.250+0.050 that is [0.26, 0.30].
3.4141
Read
Read Howitt and Cramer pages 40-50
Read Russo (e-text) pages 134-145
Read Davis and Smith pages 133-134, 139-143, 200-205, 237-264
3.4242
Practical 3
This material is available from the module web page.
http://www.staff.ncl.ac.uk/mike.cox
Module Web Page
3.4343
Practical 3
This material for the practical is available.
Instructions for the practical
Practical 3
Material for the practicalPractical 3
3.4444
Whoops!From testimony by Michael Gove, British Secretary of State for Education, before their Education Committee:
"Q98 Chair: [I]f 'good' requires pupil performance to exceed the national average, and if all schools must be good, how is this mathematically possible?
"Michael Gove: By getting better all the time.
"Q99 Chair: So it is possible, is it?
"Michael Gove: It is possible to get better all the time.
"Q100 Chair: Were you better at literacy than numeracy, Secretary of State?
"Michael Gove: I cannot remember."
Oral Evidence, British House of Commons, January 31, 2012, p. 28