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Midterm

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Carleton University

Psychology

PSYC 3000

Bruce Hutcheon

Fall

Description

Runseries plots horizontal axistimesequence vertical value of each observation detect sudden changes gradual changetrend patterns problems changes in variance plotted same sequence as gathered shows all dataScatter plots Show all data points 2 variables on 2 axes reveals unusual relationships btw variablesdetect changes in experimental conditionsHistograms not show all data points just summary Atleast interval data on xaxis y axis starts at 0 Distributions of values Identify nonnormal distributions Numeric tools Symmetric and no severe outliers mean and sd Not symmetric median and semiinterquartile range resistant to outliers and can be used for ordinalMean average intervalratio easy but affected by outliersMedianordinal but not for ANOVAmodenominal most frequent affected by sampling fluctuationsVariance Avg squared deviation from the meanStandard Deviationsquare root of variance affected by extreme scores but easy for ANOVAInterquartile Range Q3Q12 resistant to outliers but not sampling fluctuationsNominal data probability and oddsBoxplotssummary of data 50 data in box 99 inside fences dispersionsymmetryoutliers compare distributions medians Central part in box outer partwhiskersBoxplots over histograms uniform description outliers compare many distributions but dont show modality like histograms or normalityError bars can be associated with any measure of dispersion SDSE quantiles confidence intervals allow comparison of btw and within variation comparing different distributions Conduct visual ttestHypothesis testing Pick statistic take sample and measure said stat in sample make a hypothesis find out how stat should vary across sample if hyp true construct sampling distribution compare sample stat to sampling distribution reject if sample stat in tails of sampling distributionSkew 0symmetric positive pos skew neg neg skew Skew from symmetric pop doesnt have to be zero Find out how much it could jump around if pop symmetric Sampling distribution Distribution of values if draw bunch of random samples from specified distributionIn normal distribution 5 of values more than 196 SD from the mean mean should be 0 if normal If sample stat in outer 5 of sampdist then reject too rare to occur by accident significant Therefore conclude population cant be symmetric if sample stat significantly skewed If a population is symmetric then sampling distribution for the skew is normal with mean of 0 Sample skewSD of samp Dist gives number we compare to 196 SDStandard Error SD of a sampling distributionHypothesis tests of the mean Observed mean sampleexpected mean hypothesis SE z score If samp Dist normal then196 SD gives outer 5 Often samp Dist of mean is normal but not always If a population is normal then so is the corresponding sampling distribution of the mean so can use hypothesized properties of pop to find mean and SE of sampling distribution Mean of samp dist for a stat is called expected value M SE of samp

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