Chapter 3 AP Vocabulary Flashcards

7137369968	The coefficient of determination, r²	A statistic: It is the proportion of the variation in the y-variable that is accounted for by the linear regression line.	0
7137388872	Correlation, r	A statistic: It measures the direction and strength of a linear relationship between two quantitative variables.	1
7140517363	Description of a scatterplot	It addresses the direction, form and strength of an association between two quantitative variables.	2
7140530392	Explanatory variable	It is the variable that is used to predict or explain changes in the response variable.	3
7140532124	Extrapolation	It is a prediction for the response variable, based on the regression line and an explanatory variable value that is outside the observed range of explanatory variable values.	4
7140543940	Influential observation	It is an observation that has a strong effect on the regression line, especially on the slope of the regression line.	5
7140549825	Least-squares regression line	It is the line that makes the sum of the squared residuals (vertical distances from the data to the line) as small as possible.	6
7140554408	Negative association	It occurs when above-average values of one variable tend to accompany below-average values of the other, and vice versa.	7
7140559764	Outlier in regression	It is an observation that lies outside the overall pattern of the other observations in a scatterplot. They may or may not have large regression residuals, and they may or may not be influential.	8
7140567113	Positive association	It occurs when above-average values of one variable tend to accompany or occur together with above-average values of the other.	9
7140572135	Predicted value	It is the response value (y-value) that is computed by substituting an explanatory value (x-value) into the regression equation. Referred to as yhat.	10
7140582631	Residual	It is the difference between an actual y-value and the y-value predicted by the regression line. It can be positive or negative.	11
7140592316	Residual plot	It is a scatterplot of the regression residuals against the explanatory variable values. It helps analyze the form of a relationship, and whether a linear model is appropriate.	12
7140597777	Scatterplot	It is a plot of two variables, each one measured on an x- or y-axis. Each data point represents an (x,y) pair of numbers.	13
7140608598	Slope	It is the amount by which the response variable changes for a one unit increase in the explanatory variable along a regression line.	14
7140617170	Standard deviation of the residuals	It is the approximate size of a typical prediction error. It is the estimate of the typical vertical distance between the regression line and the actual data points.	15
7140623159	y-intercept	It is the predicted y-value when x=0 is substituted into a linear regression model.	16