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Midterm

midterm cheat.docx


Department
Psychology
Course Code
PSYC 3000
Professor
Bruce Hutcheon
Study Guide
Midterm

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Run-series plots: horizontal axis=time/sequence, vertical= value of each observation (detect
sudden changes, gradual change/trend, patterns, problems, changes in variance, plotted same
sequence as gathered, shows all data)
Scatter plots: Show all data points, 2 variables on 2 axes, reveals unusual relationships btw
variables, detect changes in experimental conditions
Histograms: not show all data points, just summary. Atleast interval data on x-axis, y axis starts
at 0. Distributions of values. Identify non-normal distributions.
Numeric tools: Symmetric and no severe outliers= mean and sd. Not symmetric= median and
semi-interquartile range (resistant to outliers and can be used for ordinal).
Mean= average, interval/ratio, easy but affected by outliers,
Median=ordinal but not for ANOVA,
mode=nominal, most frequent, affected by sampling fluctuations.
Variance= Avg. squared deviation from the mean,
Standard Deviation=square root of variance (affected by extreme scores but easy for
ANOVA),
Interquartile Range= Q3-Q1/2 (resistant to outliers but not sampling fluctuations).
Nominal data= probability and odds.
Boxplots=summary of data, 50% data in box, 99% inside fences, dispersion.symmetry/outliers,
compare distributions/ medians. Central part in box, outer part=whiskers.
Boxplots over histograms= uniform description, outliers, compare many distributions but
don’t show modality like histograms or normality.
Error bars= can be associated with any measure of dispersion (SD,SE, quantiles, confidence
intervals), allow comparison of btw. and within variation, comparing different distributions.
Conduct visual t-test.
Hypothesis 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 distribution.
Skew: 0=symmetric, positive= pos. skew, neg.= neg. skew. Skew from symmetric pop doesn’t
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
distribution.
In normal distribution, 5% of values more than 1.96 SD from the mean (mean should be 0
if normal). If sample stat in outer 5% of samp.dist. then reject, too rare to occur by accident
(significant). Therefore conclude population can’t 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 skew/SD of samp. Dist. gives number we compare to 1.96 SD.
Standard Error= SD of a sampling distribution.
Hypothesis tests of the mean: Observed mean (sample) expected mean (hypothesis)/ SE
(z score). If samp. Dist normal then +/- 1.96 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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