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Lecture 2

Lecture 2.docx

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Bruce Schneider

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Lecture 2: the reason why we want to know that (how the measures in a sample distributed…slide#1)is because we want to make inferences about the sample in the population make inference something about the population -eg. Physical fitness: measure HR nearest 10 to the beat per second Measurement: 95.3, 95.4, 95.5- Measurement precision one:- 1 decimal place to the right if you are measuring to the nearest units of 10 - measurement precision is -1 if the number is 110 so it will be 1.1 eg. 100= measurement precision 2 because 1. Etc so Measurement precision=-2. (2 of the digits are always 0) Slide #8: rule of thumb: ave number of categories (eg. 20 measures then you will have approx.. 5 measures per category thus u will have 4 category that would be possible) -next step: find range of scores: rank them from highest to smallest -the reason why we want ½ precision so no measurement falls under the boundary Eg. HR respond to stress: If wee can use HR change as a measure of response to the stressor -eg. Measurement precision 1 digit to the right= 1 Slide #11 lec 1: these are our measurements: you can see there is a range of HR involved…for some its decreased for some its increased -Measure the range of the HR -our measurement precision we know is 1 -our smallest measure is -4 our largest is 10 therefore range 10—4=14, and we decide we want to have 10 category and we have to define the category width…divide range 14 by the number of categories -14/1=1.4 -lowest category= is to compute 10 to – measurement precision divided by 2 eg. (10 to the minus 1)/2=0.05 -(10^.1)/2=0.05you subtract from the lowest measurement (which is -4) therefore you get Find range, 10 categories, 10 divided into range you get 1.4= which is our range or category find lowest category boundary: take 10 – measurement precision and subtract it from lowest measure in the dataset which will give u -4.05 Cumulative frequency includes all the measures and categories eg. 13 is fro,, 2 +5+6 from frequency = 1 MODULE We have 11 lower boundary: if we want to stick with 10 categories ( if we want to compare the date with previous data) then we have to make an adjustment with out category width to get precisely 10 categories: HOW DO WE DO THAT? - WE adjust our category with precisely a unit of measurement precision 10^-1=0.1 and we add 0.1 to our previous category widththe next precision above that we get is 1.5 -so we compute those category based on 1.5 We want to make sure our all measures fall into one category and we have to use real boundary take range divided by # of category if u use histogram u go with the numbers with that otherwise increase the category width Eg. Histogram Unions btw each category -distribut
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