# 400a - Statistical Analysis Midterm

 The flashcards below were created by user athorne on FreezingBlue Flashcards. ANOVA design IV manipulatedDV measured Regression design IV selectedDV measured Correlation design IV measuredDV measured ANCOVA design IV and DV are a mix of manipulated, selected, and measured ANOVA analysis "analysis of variance"categorical IVcontinuous DV Regression analysis "regression analysis"continuous IVcontinuous DV ANCOVA analysis "analysis of covariance"combination of continuous and categorical DV and IV simple model predicts the same value for everyoneunconditionally complex model includes more than the constant in the modelpredicts a different value for everyoneconditional bivariate regression analysis DV and IV are continuousonly a single predictor multiple regression analysis DV and IV are are continuousmore than one predictor factorial ANOVA analysis IV is categorical, DV is continuousa categorical variable is linked to another categorical variable multivariate ANOVA analysis IV is categorical, DV is continuousthere is more than one DV measurement error error associated with unreliable and invalid measures design error error associated with a poor design and therefore the data is inaccurate and non-representative sampling error: error associated with non-representative samplingalways expected to exist because a sample will never truly represent a populationproblematic for analysis if it results from design error commission inclusion of variables to a model that should not be there omission exclusion of variables to a model that should be thereusually realized post hoc PRE - proportional reduction of error [ERROR(C) - ERROR(A)]/ERROR(C)estimate of ada squaredeffect size SAE - sum of absolute errors ∑|Y-ˆY|use the median to minimize SSE - sum of squared errors ∑(Y-ˆY)²use the mean to minimize MAE - mean of absolute errors mdn|y-mdn| MSE - mean of squared errors ∑(y-ˆy)²/[N-(p+1)]dividing by the number of parameters that can still be reducedb₀ cannot be reduced more standard error of estimate square root of the MSE SD - standard deviation square root of the variance (MSE in simple models) variables get scores from a sample in a certain area (X) parameters how much of the variable predicts y unbiased frequency sampling distribution the mean value of the sampling distribution is close to β₀ efficient frequency sampling distribution the sampling distribution is skinnymore likely to be close to β₀ consistent frequency sampling distribution efficiency of sampling distribution increases as sample size increases assumptions regarding error normal distributionunbiasedindependenthomeoscedasticity homeoscedasticity error distributions of y are the same across different values of x standard error of the mean the size of the difference between the population mean and expected mean central limit theorem the distribution of errors is normal sampling distribution distribution taken from a population where the null hypothesis, MODEL©, is true sample distribution distribution based on the data SSR - sum of squares reduced SSE(C) - SSE(A) SSR (remaining) regression analysis: SS(regression)ANOVA analysis: SS(between) SSE(A) regression analysis: SS(residual)ANOVA analysis: SS(within) SSE(C) regression analysis: SS(total)ANOVA analysis: SS(total) PA number of parameters in model A PC number of parameters in model C F transformation of PRE to standardized formobtained proportion of reduction divided by the amount of known parameter changes, all divided by the remaining unexplained error divided by the number of unknown parametersratio of the amount of explained error to unexplained errortypically sig if greater than 4 or 5equal to t² η² ada squaredestimate of PREexpected to be 0 when null hypothesis is true ἣ² unbiased ada squaredaccounts for ada squared always being positivecritical value which cuts off the most extreme 5% Type 1 Error wrong conclusion that the null hypothesis is trueequal to alpha (usually 5%) Type 2 Error wrong conclusion the null hypothesis is correct, but is actually false in realityβ is the probability of this type of error Power the probability of actually detecting an effect that is theredepends of the effect size (PRE - smaller means harder to detect), the level of alpha (but only ever made more stringent and decrease power), and sample size (N) β₁ how much changer there is on the y-axis for every one unit of change on the x-axisslope r(xy) relationship of x and y s(y) scaling factor standard error of estimate square root of the MSE(A)also known as the standard error of prediction variance of y MSE(C) Authorathorne ID241818 Card Set400a - Statistical Analysis Midterm Descriptiontheoretical introduction to the general linear model, model comparison approach, and regression analysis Updated2013-10-21T08:21:56Z Show Answers