Review- Chapter 8 Statistics

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22 Terms

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Statistical inference

  • drawing a conclusion about a population parameter based on a sample statistic

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Confidence Interval

  • used to estimate the value of a parameter with a range of plausible values

  • estimate population proportion(p) with a z test

  • " I am % confident that the true proportional of ALL is within the interval__to__)

  • Width of CI=1/√n

  • CI increase →width increase →z* increase

  • CI decrease →width decrease →ME and SE decrease

  • MORE narrow graph

    • decrease CI or Increase n

  • Higher CI

    • accept a wider interval OR increase n

*MUST compare to population, not only the sample

1 Proportion Z test

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Confidence Level

  • “if you took ____samples and constructed the resulting confidence interval, the true proportion of ALL___ will be in __%(as a number) of these intervals”

    increase

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Significance test

  • Assess the plausibility of a particular claim about a parameter

  • TEST claim about the population(p) with a z-interval

CONDITIONS

  1. Random sample(SRS)

  2. n1 p1 >=10, n1(1- p1 )>=10

  3. The sample size is <= 10% of the population size

  • condition of independence & not population >10n =okay!

  • the increase in variation due to the dependence almost offsets the decrease due to a relatively large sample

STEPS

Title

  1. Conditions

  2. Hypothesis, label

  3. Test statistic→pvalue→diagram(if need)

  4. Conclusion in context, doubts of validity

    1. Compare p-value with α, level of significance

    2. compare test stat to z*

  • “Since p value _α, there is/is not statistically significant evidence to reject the null hypothesis, that is there is/is not statistically significant evidence…’

<ul><li><p>Assess the <strong>plausibility</strong> of a particular <strong>claim</strong> about a parameter</p></li><li><p>TEST claim about the population(p) with a z-interval</p></li></ul><p>CONDITIONS</p><ol><li><p>Random sample(SRS)</p></li><li><p>n<sub>1 </sub>p<sub>1</sub> &gt;=10, n<sub>1(1- </sub>p<sub>1</sub> )&gt;=10</p></li><li><p>The sample size is &lt;= 10% of the population size</p></li></ol><ul><li><p>condition of independence &amp; <span style="color: red">not </span>population &gt;10n =<mark data-color="green">okay!</mark></p></li><li><p>the<span style="color: green"> increase</span> in <strong>variation</strong> due to the dependence almost <mark data-color="red">offsets</mark> the <span style="color: red">decrease</span> due to a relatively <span style="color: green">large</span> sample </p></li></ul><p>STEPS</p><p>Title</p><ol><li><p>Conditions</p></li><li><p>Hypothesis, label</p></li><li><p>Test statistic→pvalue→diagram(if need)</p></li><li><p>Conclusion in <strong>context</strong>, doubts of validity</p><ol><li><p>Compare p-value with α, level of significance</p></li><li><p>compare test stat to z*</p></li></ol></li></ol><ul><li><p>“Since p value _α, there is/is not statistically significant evidence to reject the null hypothesis, that is there is/is not statistically significant evidence…’</p></li></ul>
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Pooled estimate

p̂ =total success in BOTH sample size/total sample size

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means and standard error of sampling distribution formula

knowt flashcard image
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P value

  • probability of obtaining a sample statistics as EXTREME or MORE EXTREME than one obtained by the null hypotheses( Ho ) is assumed to be true

  • small(<0.05,0.1)→ sufficient evidence to reject Ho

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z*

  • Critical z

  • Ways to increase z*

    1. farther from p

    2. sample size decrease

    3. po farther from 0.5

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Margin of error

  • exactly ½ the width of CI

  • determined by…

  • small MOE→ large n

  1. how MUCH statistic typically varies from parameter σ

  2. how confident do we want our answer to be z*

MATH

z*√((p̂(1-p̂))/n

use 0.5 for p̂ if not given

<ul><li><p>exactly ½ the width of CI</p></li><li><p>determined by…</p></li><li><p><span style="color: red">small</span> MOE→ <span style="color: green">large</span> n</p></li></ul><ol><li><p>how MUCH statistic typically <strong>varies</strong> from parameter σ<sub>p̂</sub></p></li><li><p>how <strong>confident</strong> do we want our answer to be z*</p></li></ol><p>MATH</p><p>z*√((p̂(1-p̂))/n</p><p>use 0.5 for p̂ if not given</p>
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Hypothesis

Ho = null hypothesis, must use an = sign

Ha= alternate hypothesis, can use <, >, <=,>=, ≠

  • defines the parameter of interest → refers to the population

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sample proportion

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po

assumed proportion in the null hypotheses(Ho ), the given in text

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2 tail

2p(z____)

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1 tail

  • either < or >

  • in context, could be testing for strength, effectiveness…

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Errors

are inversely related

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Type I error (α)

  • rejecting the null hypotheses(Ho ) when it is true

  • increase if α increase

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Type II error(β)

  • failing to reject null hypotheses(Ho )when its false

  • MORE likely for small n

    *the only way to have power

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Power

(1-β)

  • probability that type II error does not occur → correctly rejecting null hypotheses(Ho )when its false

  • influenced by α

Power Increase by…

  1. n Increase

  2. α Increase

  3. SE decrease

  4. True parameter further from null

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Difference of 2 Proportions

CONDITIONS

  1. 2 samples are taken randomly or randomly assigned

  1. n11 , n1(1-p̂1), n22, n2(1-p̂2) >=5

  2. The sample size is <= 10% of the population size

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Confidence Interval of Difference of 2 Proportions

Captures 0

  • yes → __% confident that there is no difference between p1 and p2

    →means that 1 value is positive, 1 value is negative

  • no → __% confident that there is a difference between p1 and p2

    not plausible for there to be no difference

  • n can sometimes be split in half

  • “the true difference in proportion between___and___is”

  • 2 groups having the SAME “proportion”→ risk ratio =1, so for a positive/greater proportion than the other CI range is the decimal +1.

<p>Captures 0</p><ul><li><p><span style="color: green">yes </span>→ __% confident that there is <span style="color: red">no</span> difference between p1 and p2</p><p>→means that 1 value is <span style="color: green">positive</span>, 1 value is <span style="color: red">negative</span></p></li><li><p><span style="color: red">no </span>→ __% confident that there is a difference between p1 and p2</p><p>→<span style="color: red">not </span><strong>plausible</strong> for there to be <span style="color: red">no </span>difference</p></li><li><p>n can sometimes be<strong> split in half</strong></p></li><li><p>“the<span style="color: green"> true</span> difference in proportion between___and___is”</p></li><li><p>2 groups having the SAME “proportion”→ <mark data-color="yellow">risk ratio </mark>=1, so for a <span>positive/greater </span>proportion than the other CI range is the decimal +1.</p></li></ul>
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Test statistic of Difference of 2 Proportions

STEPS
name 2 proportion z test

result → C up, C Low

  1. Conditions

  2. Hypotheses

  3. Test statistic → p value → diagram(if need)

  4. Conclusion in context, doubts of validity

    1. compare p value and α

<p>STEPS<br>name<mark data-color="yellow"> 2 proportion z test</mark></p><p>result → C up, C Low </p><ol><li><p>Conditions </p></li><li><p>Hypotheses</p></li><li><p><strong>Test statistic</strong> → p value → diagram(if need) </p></li><li><p>Conclusion in context, doubts of validity </p><ol><li><p>compare p value and α</p></li></ol></li></ol>
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