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# Untitleddocument NTR 108A

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This 8 page One Day of Notes was uploaded by Suzanna Van Roijen on Thursday January 29, 2015. The One Day of Notes belongs to NTR 108A at University at Buffalo taught by Chabot in Fall. Since its upload, it has received 144 views. For similar materials see Human Nutrition in Health Sciences at University at Buffalo.

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Date Created: 01/29/15

Statistics set of tools building a tool box describing and originally descriptive numerical observations tools used to organize population and samples Population set of the individuals of interest eg depressed humans sample subset of a population individuals in a research study past humans and yet to he formed people inferences predicts behavior Population and samples parameters and statistics numerical description of characteristic of a population average of hours of sleep for entire population of depressed individual statistics numerical descriptions of a sample average of hours sleep in a study statistics fer to a whole set of tools statistics specific tool applied to a sample parameters statistics describes samples parameters describes population descriptive tables graphs surveys graphs inferential statistics tool to make inferences from observations tools for generalized beyond the available observation population gt select a sample subset sample to refer to population Sampling error discrepancies between sample and the population not a perfect picture of a population The discrepancy between the sample statistic and the population parameter in sampling error ef average hours of sleep for sample 12 for population Data collection of numerical observations sinlge datum is a raw score qualitative data a single observation represents a class or categories marital status religion sometimes categorical data look at a single observation quantitative a single amount or count reaction time cognitive psychology quantitative or qualitative political party qualitative blood pressure amount quantitative gender qualitative residence qualitative coordinates quantitative Discrete data VS continuous data discrete data consists of countany data integers continuous infinite number of possible values heights body weight both can be infinite discrete countable with integers continuous higher degree of infinity qualitative are always discrete quantitative data are not always continuous quantitative data are not always continuous number of cars in parking lot is discrete and qualitative variable and constant variable characteristics of property of organizm constant does not change 5 x loo 50 mostly with population independent and dependent variables independent variable IV manipulated by a n investigate in an experiment measured new antidepressant drug we have two groups if depressed participants anti depressed participants other group placebo independent manipulated antidepressant placebo dependent unchange symptoms of depression experimental and correlational studies experimental and correlational studies experiment investigator aipultable IV and measure the DV correlation national study investigator measures two DV and looks for relationship relationship vocabulary size and age scales of measurement what number did you wear in a race 10 what did you finish 10 place how many minutes did it take 10 using different scales of measurements assigning to objects nominal ordinal interval ratio nominal name an objects used to label ordinal refers to date score that can be arranged some info about order rank in class Interval scale 12915 refers to data that have meaningful differences between scores numbers are used to identify an object or event and tell we the rank or order however a there is a O lacking or may be arbitrary Interval scales example celsius and fahrenheit temperature scale are both interval scales zero degree in both is arbitrary that is zero does not interval measurement scale example difference can be determined between 25 and 50 degrees Fahrenheit but 50 degree is not twice as hot as 35 what about kelvin scale there is an absolute zero ratio measurement refers to data on a scale with a true zero point has all properties of nominal ordinal interval scale example height in principle weight in principle can be zero the time it takes to finish the race 0 think about absolute zero in principle other key terms and concepts from gw quasi independent variable sampling error descriptive statistics tables and graphs tables are used to describe data define frequency distribution frequency distribution societal collection of observations showing the number of three or frequency each observation occurs two kinds of dataungrouped and grouped example the size of science classes private elementary schools we surveyed 20 private schools ungrouped frequency distribution 52930251516171551118202016201913211414 20 25 18 17 11 pick out the lowest value and the highest value of students frequency 30 1 29 1 28 0 27 0 26 0 25 1 24 0 23 0 22 0 21 1 20 3 19 1 18 1 17 16 15 14 13 12 11 10 9 NCOOOO LONN LNN 8 7 6 5 If you have a large number of value that cover a wide range the ungrouped frequency distribution is not appropriate for example 1000 observation that range from 0 to 500 grouped frequency distribution of classes frequency 3034 1 Zltsigma sum Zf 2529 2 2024 4 flt is frequency 1519 5 1014 5 Zsum of the frequency is 20 59 2 Zf class width5 relative frequency distribution a frequency distribution consists converting a proportion 22010 most classes are between 1014 and 1519 students Outliers extreme values in a frequency distribution eg if yiu have 100 observations and 99 of these observations of 32000 would be an outlier you can exclude the value from your table but you must make sure that it is clear that the value has been excluded graphs built directly on frequency distribution you should be able to construct and identify histograms frequency polygons bar graphs identify histograms a paper a frequency distribution in what a rectangular bar over each value the plotting quantitative if bars are against each other frequency polygons wline graph of a frequency distribution in which classes are plotted on that axis and frequencies Run RanE Results Number lullquot Runners 5 If 15 El 25 3e 35 411 45 l t lllEE HullBE frequency polygons help you see the shape typical shapes of histograms and frequency polygons nominal distribution familiar bell shape curve symmetrical describes many naturally occurring phenomena nominal distribution perfectly known typical shapes phenomena normal distribution perfectly known typical shapes of a histogram and frequency polygon 2 bimodal distribution distribution with two modes or humps iEimdal Histgram skewed distribution what are they numbers of extreme values positively skewed extremes values are large positively skewed distribution positive direction Negatively skewed distortion 1 4 4 HdHu bar graph type of histograms to graph qualitative bar graph 14D E u aw wMMEEEE ELI WI ELI MS MILE L5 1IJLE BEL Elh It Enalle

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