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... residuals; outliers and influential points should make you cautious in interpreting results. 2) The standard deviation of y about the true line () is the same for all values of x. Check the residual plot – are the residuals evenly scattered about the regression line? 3) For any fixed value of x, th ...
... residuals; outliers and influential points should make you cautious in interpreting results. 2) The standard deviation of y about the true line () is the same for all values of x. Check the residual plot – are the residuals evenly scattered about the regression line? 3) For any fixed value of x, th ...
Stats Boot Camp
... random sample {Y1, Y2, . . . Y3} from a distribution with mean µ and variance σ2, then you can act as if you drew from a normal distribution with mean µ and variance σ2. More precisely, we can say that Y −µ Z= has an asymptotic standard (i.e. mean 0, variance 1) normal distribution and so does the σ ...
... random sample {Y1, Y2, . . . Y3} from a distribution with mean µ and variance σ2, then you can act as if you drew from a normal distribution with mean µ and variance σ2. More precisely, we can say that Y −µ Z= has an asymptotic standard (i.e. mean 0, variance 1) normal distribution and so does the σ ...
Introduction to Biostatistics
... If p > .05, the null hypothesis is usually accepted (the scientific hypothesis is rejected), and any measured difference is thought to be a chance event. This is an arbitrary cutoff point. If p = .05 there is still a 1 in 20 chance that the null hypothesis is actually true, but that the measured d ...
... If p > .05, the null hypothesis is usually accepted (the scientific hypothesis is rejected), and any measured difference is thought to be a chance event. This is an arbitrary cutoff point. If p = .05 there is still a 1 in 20 chance that the null hypothesis is actually true, but that the measured d ...
t Test for a Single Sample
... • Compares mean difference score across pairs of scores against a difference of 0 under the null hypothesis. • In other respects, t test for dependent means is just like a single sample t test with a population mean of 0 Aron, Aron, & Coups, Statistics for the Behavioral and Social Sciences: A Brief ...
... • Compares mean difference score across pairs of scores against a difference of 0 under the null hypothesis. • In other respects, t test for dependent means is just like a single sample t test with a population mean of 0 Aron, Aron, & Coups, Statistics for the Behavioral and Social Sciences: A Brief ...
data prep and descriptive stats
... Coding • The process of systematically and consistently assigning each response a numerical score. • The key to a good coding system is for the coding categories to be mutually exclusive and the entire system to be collectively exhaustive. • To be mutually exclusive, every response must fit into on ...
... Coding • The process of systematically and consistently assigning each response a numerical score. • The key to a good coding system is for the coding categories to be mutually exclusive and the entire system to be collectively exhaustive. • To be mutually exclusive, every response must fit into on ...
Sharpening the jackknife - Trevor Sharot: Red Research
... On the basis of the Monte Carlo studies, it would appear that the desired gain in precision o£J(p*) over $m is often achieved. Alternative estimators designed for a particular application may, not surprisingly, do better still. The infinitesimal jackknife is seen to yield just such an estimator in m ...
... On the basis of the Monte Carlo studies, it would appear that the desired gain in precision o£J(p*) over $m is often achieved. Alternative estimators designed for a particular application may, not surprisingly, do better still. The infinitesimal jackknife is seen to yield just such an estimator in m ...
The t-test - University of South Florida
... The t distribution is a short, fat relative of the normal. The shape of t depends on its df. As N becomes infinitely large, t becomes normal. ...
... The t distribution is a short, fat relative of the normal. The shape of t depends on its df. As N becomes infinitely large, t becomes normal. ...
