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Lecture 17

STAT 2230 Lecture 17: Day 17: Paired design + Midterm notes [Feb 15]
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by Bronwyn , Winter 2017
3 Pages
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Department
Statistics
Course Code
STAT 2230
Professor
Andrew Mc Adam
Lecture
17

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Day 17: Paired design [Feb 15]
- assumptions of independent samples t-test:
- observations are independent
- observations are normally distributed
- dispersion is equal to the two groups (‘equality of variances’)
- ////CHART + a few other pages
- paired design: comparing two groups when groups are paired
- in a paired design, each observational or experimental unit in group 1 is
paired or matched with an observational or experimental unit in group 2
- the members of a pair have more in common with each other than with
members of another pair
- paired designs make it easier to detect differences between groups when
they exist
- only concerned with differences between groups within a pair
- differences among pairs are ignored
- example:
- researchers are interested in comparing fish species directly in
lakes before and after heavy metal contamination
- comparing patient weight before and after hospitalization
- testing effects of sunscreen applied to one arm of each subject,
compared with a placebo applied to the other arm
- testing effects of smoking in a sample of patients, each of which is
compared with a non-smoker closely matched by age, weight, and
ethnic background
- paired t-test
- d = difference between each pair of measurements
- degrees of freedom (df) = number of pairs - 1
- ////CHART
- midterm review notes:
- multiple choice and short answer
- 29 questions
- know when you would use pt vs. 1-pt vs. 2xpt, etc.
- answers to the sample midterm will be posted after class!
- example:
- 1, 2, 3, 4, || 5, 6, 7, 8
- median is 4.5
- 1, 2, | 3, 4, | 5, 6, | 7, 8
- interquartile ranges
- Q1 = 1, 2
- Q2 = 3, 4
- Q3 = 5, 6
- Q4 = 7, 8

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Description
Day 17: Paired design [Feb 15] - assumptions of independent samples t-test: - observations are independent - observations are normally distributed - dispersion is equal to the two groups (‘equality of variances’) - ////CHART + a few other pages - paired design: comparing two groups when groups are paired - in a paired design, each observational or experimental unit in group 1 is paired or matched with an observational or experimental unit in group 2 - the members of a pair have more in common with each other than with members of another pair - paired designs make it easier to detect differences between groups when they exist - only concerned with differences between groups within a pair - differences among pairs are ignored - example: - researchers are interested in comparing fish species directly in lakes before and after heavy metal contamination - comparing patient weight before and after hospitalization - testing effects of sunscreen applied to one arm of each subject, compared with a placebo applied to the other arm - testing effects of smoking in a sample of patients, each of which is compared with a non-smoker closely matched by age, weight, and ethnic background - paired t-test - d = difference between each pair of measurements - degrees of freedom (df) = number of pairs - 1 - ////CHART - midterm review notes: - multiple choice and short answer - 29 questions - know when you would use pt vs. 1-pt vs. 2xpt, etc. - answers to the sample midterm will be posted after class! - example: - 1, 2, 3, 4, || 5, 6, 7, 8 - median is 4.5 - 1, 2, | 3, 4, | 5, 6, | 7, 8 - interquartile ranges - Q1 = 1, 2 - Q2 = 3, 4 - Q3 = 5, 6 - Q4 = 7, 8 - what happens when you have an odd number of data? - 1, 2, 3, 4, |5|, 6, 7, 8, 9 - median is 5 - 1, 2, |3|, 4, |5|, 6, |7|, 8, 9 - IQR is trickier - include the same # in both the upper and lower quartiles - Q1 = 1, 2, 3
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