Scatterplots
A ______ shows the relationship between two quantitative variables measured on the same cases.
Association
You look for _________ by checking direction, form, and strength.
Outlier
A point that does not fit the overall pattern seen in the scatterplot
Response Variable
The variable on the y-axis that you hope to predict or explain
Explanatory Variable
The variable on the x-axis that accounts for, explains, predicts, or is otherwise responsible for the y-variable.
Correlation Coeffient
The ______ _______ is a numerical meausure of the direction and strength of a linear regression.
Lurking variable
A variable other than x and y that simutaneously affects both variables, accounting for the correlation between the two.
Model
An equation or formula that simplifies and represents reality
Linear Model
A _____ _____ is an equation of a line. To interpret it, we need to know the variables (along with their W's) and their units.
Predicted Value
The value of y found for a given x-value in the data.
Residuals
The differences between data values and the corresponding values predicted by the regression model- or, more generally, values predicted by any model
Least Squares
The _____ ______ criterion specifies the unique line that minimizes the variance of the residuals or, equivalently, the sum of the squared residuals.
Regression to the Mean
Beacuse the correlation is always less than 1.0 in magnitude, each predicted y tends to be fewer standard deviations from its mean than its corresponding x was from the mean.
Regression Line/ Line of Best Fit
The particular linear equation that satisfies the least squared criterion
y=b0+b1x
Slope
The ____ b1, gives a value in "y-units per x-unit." Changes of one unit in x are associated with changes of b1 units in predicted values of y.
rs(small y below s)
b1 = -------------------------------
s (small x below s)
Intercept
The ______ b0, gives a starting value in y-units. It's the y value when x is 0.
Extrapolation
Although linear models provide an easy way to predict values of y for a given value of the x, it is unsafe to predict for values of x far from the ones used to find the linear model equation. Such _______ may pretend to see into the future, but the predic
Outlier
In regression, they can be extraordinary in two ways: by having a large residual or by having high levarage.
Leverage
Data points whose x-values are far from the mean of x are said to exert ______ on a linear model. High ones pull the line close to them, and so they have a large effect on the line.
Influential Point
If omitting a point from the data results in a very different regression model, then it is a ______ _______.
Lurking Variable
Not always apart of the model, but affects the way the variables in the model appear.
Re-expression
We _______ data by taking the logarithm, the square root, the reciprocal, or some other mathematical operation on all values of the variable
Ladders of Powers
The _______ __ ________ places in order the effects that many re-expressions have on the data.
Scatterplots
A ______ shows the relationship between two quantitative variables measured on the same cases.
Association
You look for _________ by checking direction, form, and strength.
Outlier
A point that does not fit the overall pattern seen in the scatterplot
Response Variable
The variable on the y-axis that you hope to predict or explain
Explanatory Variable
The variable on the x-axis that accounts for, explains, predicts, or is otherwise responsible for the y-variable.
Correlation Coeffient
The ______ _______ is a numerical meausure of the direction and strength of a linear regression.
Lurking variable
A variable other than x and y that simutaneously affects both variables, accounting for the correlation between the two.
Model
An equation or formula that simplifies and represents reality
Linear Model
A _____ _____ is an equation of a line. To interpret it, we need to know the variables (along with their W's) and their units.
Predicted Value
The value of y found for a given x-value in the data.
Residuals
The differences between data values and the corresponding values predicted by the regression model- or, more generally, values predicted by any model
Least Squares
The _____ ______ criterion specifies the unique line that minimizes the variance of the residuals or, equivalently, the sum of the squared residuals.
Regression to the Mean
Beacuse the correlation is always less than 1.0 in magnitude, each predicted y tends to be fewer standard deviations from its mean than its corresponding x was from the mean.
Regression Line/ Line of Best Fit
The particular linear equation that satisfies the least squared criterion
y=b0+b1x
Slope
The ____ b1, gives a value in "y-units per x-unit." Changes of one unit in x are associated with changes of b1 units in predicted values of y.
rs(small y below s)
b1 = -------------------------------
s (small x below s)
Intercept
The ______ b0, gives a starting value in y-units. It's the y value when x is 0.
Extrapolation
Although linear models provide an easy way to predict values of y for a given value of the x, it is unsafe to predict for values of x far from the ones used to find the linear model equation. Such _______ may pretend to see into the future, but the predic
Outlier
In regression, they can be extraordinary in two ways: by having a large residual or by having high levarage.
Leverage
Data points whose x-values are far from the mean of x are said to exert ______ on a linear model. High ones pull the line close to them, and so they have a large effect on the line.
Influential Point
If omitting a point from the data results in a very different regression model, then it is a ______ _______.
Lurking Variable
Not always apart of the model, but affects the way the variables in the model appear.
Re-expression
We _______ data by taking the logarithm, the square root, the reciprocal, or some other mathematical operation on all values of the variable
Ladders of Powers
The _______ __ ________ places in order the effects that many re-expressions have on the data.