chap 5 stats
Quiz yourself by thinking what should be in
each of the black spaces below before clicking
on it to display the answer.
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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| use X for the __ (__) variable and Y for the __ (__) variable | show 🗑
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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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| the relationship between certain variables X and Y, represents the values of X on the abscissa and the values of Y on the ordinate, and the scors for each individual on the body of the graph | show 🗑
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| show | slope
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| show | y1-y2/ x1-x2
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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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| show | negative
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| point at which a line intersects the y axis when x=0 | show 🗑
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| show | a
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| a linear model is a __ that states the __ relationships and how they can differ in the values of their __ and values of their __ | show 🗑
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| linear model equation | show 🗑
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| b= the __ and a= the __ | show 🗑
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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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| show | -1.00 to +1.00, r
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| show | direction, direct relationship, inverse relationship
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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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| the further r is in either a positive or negative direction from 0, the ... | show 🗑
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| show | sum, products
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| show | size of correlation and sample size
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| show | divide by N
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| show | N, scores fall between -1 and +1
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| show | sum of squares for variable X and variable Y
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| sum of crossproducts is more precise and efficient because it requires __ and presents __ | show 🗑
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| show | causes, causal relationship
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| show | varies
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| show | +/- .20 +/- .30
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| the proportion of variability in the dependent variable that can be explained by or that is associated with the independent variable | show 🗑
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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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| the slope of the regression line equation: | show 🗑
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| show | a=y-bX
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| show | intersects the yaxis at the value of the intercept
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| show | y increases by the b
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| show | squared vertical distances
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| the criterion for deriving the values of the slope and intercept | show 🗑
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| show | discrepancy scores, minimize the sum of these squared errors
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| show | actual and predicted y scores
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| show | always equal zerio
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| index of predictive error | show 🗑
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| typical error made when predicting y from x | show 🗑
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| conditions of standard error of estimate (4) | show 🗑
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| show | interval
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| show | both x and y
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| show | linear
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| show | x
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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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| show | curvilinear or polynomial regression
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| show | y, x
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| show | conversion rates
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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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| show | magnitude
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| show | reduce the magnitude of the correlation coefficient
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| show | range of x, regression
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| we must not extend our interpretation of correlational results outside the range of the original data set- the conclusions drawn from a correlational analysis apply only to the | show 🗑
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| the pearson correlation coefficient reps the extent to which two variables approximate a linear relationship for the __ of __ included in its __ | show 🗑
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| show | standardize x and y scores, apply formula to calculate slope
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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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| show | correlation ccoefficient
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| a correlation coefficient conveys the number of __ that one variable is predicted to change given a change of one standard score in the other variable, other things being __ | show 🗑
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| the magnitude and sign of a correlation coefficient can be influenced by __ | show 🗑
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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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| show | nature of relationship, strength of relationship
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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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| show | variability, y, x,
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Created by:
lilcollins92