chap 5 stats
Help!
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| what are the two purposes of the pearson correlation and regression descriptive aspects: to study the relationship between __ and to predict scores on one variable from scores of __- how are scores __ | show 🗑
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| show | predictor (independent), criterion (dependent)
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| the pearson correlation is used to determine the extent to which .. | show 🗑
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| regression is used to identify the line that best describes this relationship as determined by a statistical criterion known as the | show 🗑
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| show | scatterplot
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| show | slope
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| the slope (B)= (state equation) | show 🗑
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| a relas X increases so does Y and as X decreases so does Y- this shows what relationship | show 🗑
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| inverse relationship between x and y varialbes is what relationship | show 🗑
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| show | intercept
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| show | a
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| show | equation, linear, intercepts, slopes
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| linear model equation | show 🗑
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| show | slope, intercept
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| show | y, x
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| using the linear equation we can substitute the scores on __ and get the scored predicted on __ | show 🗑
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| the pearson correlation coefficient can range from __ to __ and is represented as _ | show 🗑
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| the sign of the correlation coefficient indicates the __ of linear approximation (+) means __ and (-) means __ | show 🗑
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| correlation coefficient of 0 means that there is | show 🗑
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| the magnitude of the correlation coefficient is indexed by its | show 🗑
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| the magnitude indicates the __ to which a __ is approximated | show 🗑
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| show | higher the magnitude
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| we can use the __ of the __ of z scores as an index of the relationship between two variables | show 🗑
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| the sum of the products of z scores can be influences by __ and __ | show 🗑
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| how do you fix this problem? | show 🗑
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| show | N, scores fall between -1 and +1
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| what is SSx and SSy | show 🗑
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| show | fewer steps, presents fewer opportunities for rounding error
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| show | causes, causal relationship
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| show | varies
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| in behavioral science research, correlations of __, __ and __ __ are considered significant | show 🗑
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| show | coefficient of determination (r2)
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| the proportion of variability in the dependent variable and cant be explained by and is not associated with the dependent variable | show 🗑
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| when two variables are not perfectly correlated, the statistical technique of __ can be used to identify a line that fits the data points better than any other line | show 🗑
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| show | least squares criterion
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| the regression line describes the nature of the __ between the two variables | show 🗑
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| show | y=a +bx
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| show | b=SCP/SSx
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| intercept regression line equation | show 🗑
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| the regression line is the line that __ the __ at the value of the | show 🗑
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| and the slope of this line is scuh that when x increases by one unit, | show 🗑
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| the slope and the intercept are defined so as to minimize the ___ that the data points are from the regression line | show 🗑
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| show | least squares criterion
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| the least squares criterion concerns itself with the squares of the __ and formally defines the values of the slope and intercept so as to __ the __ | show 🗑
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| the amount of error for a given individual can be represented by the discrepancy between that persons __ and __ _ scores | show 🗑
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| least squares criterion is limited by the fact that the sum of the discrepancies between the actual y scores and y scores predicted from the regression equation will.. | show 🗑
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| index of predictive error | show 🗑
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| show | standard error of estimate
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| show | x and y are interval level measurements, same individuals are measured on both x and y, there is a linear trend between x and y predictions shouldnt be made with scores on x beyond the range measured
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| x and y in Syx are __ measurements | show 🗑
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| show | both x and y
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| show | linear
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| predictions shouldnt be made withs cores on __ beyond the range measured | show 🗑
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| show | meaningful
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| the standard error of estimate can be compared with the __ of Y which indicated what the average error in prediction would be if one were to predict a Y score equal to the mean of Y for each individual | show 🗑
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| show | not be sensitive to this
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| if they are related nonlinearly what are effective models | show 🗑
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| the regression equation for predicting variable x from varialbe y is not the same as the regression equation for predicting variable _ from variable x | show 🗑
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| example of this | show 🗑
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| from a statistical perspective, the designation of one variable as X and one varialbe as Y is | show 🗑
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| show | regression
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| if interest is merely in whether a given variable is linearly related to another __ can be applied | show 🗑
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| from a conceptual perspective the decision of which variable to designate as X and which to designate as Y has | show 🗑
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| depending on the particular circumstances, the __ of the correlation when a limited portion of this range is considered might be either less than or greater than if the range had not been so restricted | show 🗑
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| if two variables are linearly related, then restricting the range of one variable will __ the __ of the __ | show 🗑
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| show | range of x, regression
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| show | range of variables on which the correlation was based
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| show | range of variables, calculation
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| when using the regression equation for standard scores.. first __ the x and y scores and then apply the __ to calculate the __ for the regression line based on standard scores | show 🗑
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| in regression line eq. you dont have to calculate the intercept of the regression line when standard scores are analyzed in this manner because it will always | show 🗑
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| the slope of the regression line in this instance will always equal the | show 🗑
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| show | standard scores, equal
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| show | outliers
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| show | turn weak correlation into strong or strong correlation into weak
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| how strong a relationship is determined through | show 🗑
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| correlation coeff R (2) | show 🗑
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| show | slope of regression line should be 0 when no linear relationship
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| show | b or r
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| coef of determination indicates proportion of __ in _ explained or predicted by _ | show 🗑
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lilcollins92