BIOL101A: Ex. 2 Graphs and Measurements

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13 Terms

1
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measurement

description of a phenomenon in precise terms which then can be handled objectively

2
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systolic pressure

equal to the maximum pressure recorded in an artery

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diastolic pressure

the minimum pressure recorded in an artery 

4
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waist-to-hip ratio

estimates belly fat

5
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normal distribution

the definite pattern with most of the values clustered toward the middle of the range and very few values at the extreme edges of the range

  • described the variation in a large number of phenomena, biological and non-biological

<p>the definite pattern with most of the values clustered toward the middle of the range and very few values at the extreme edges of the range </p><ul><li><p>described the variation in a large number of phenomena, biological and non-biological</p></li></ul><p></p>
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central tendency

the idea that there is a tendency for values to group around a central value

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distribution

a certain degree of variability or spread away from this central value

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mean

the average value obtained by dividing the sum of a sample of quantities by the total number of quantities added 

<p>the average value obtained by dividing the sum of a sample of quantities by the total number of quantities added&nbsp;</p>
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median

another measure of central tendency that is the middle-most value of a list of values comprising the data set

  • 4,9,10,10,13,24,22,34

    • 13 —> middle most

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mode

the value in a data set that occurs most frequently

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standard deviation

measure of variability relative to the mean 

  • Xi - avg = the difference of each value from the mean 

  • N = total number of individual measurements (same size) 

  • can be used to predict the number of measurements which should fall within any given interval centered on the mean 

<p>measure of variability relative to the mean&nbsp;</p><ul><li><p>Xi - avg = the difference of each value from the mean&nbsp;</p></li><li><p>N = total number of individual measurements (same size)&nbsp;</p></li><li><p>can be used to predict the number of measurements which should fall within any given interval centered on the mean&nbsp;</p></li></ul><p></p>
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causality

demonstrated by altering one variable (IV) in the system experimentally and then observe whether a corresponding change takes place in the other variable (DV) 

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statistical correlation

measures the strength and direction of a linear relationship between two variables