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Chapter 3 and 4 Stats Vocabulary
Terms in this set (25)
measures an outcome of a study; graphed on y axis
attempts to explain the observed outcomes of a study; graphed on x axis
The variable(s) being used to predict the dependent variable (x).
The variable being predicted (y).
Shows the relationship between two quantitative variables
Describes the direction of the points on the scatterplot.
The deviation between the observed value of the response variable, y, and the mean value of the response variable
Describes whether the points from a scatterplot are linear or curved.
Describes whether the points on the scatterplot are in either negative or positive direction.
a scatterplot shows an association that is this if there is little scatter around the underlying relationship
these form when there is a gap between the data
means that if one variable gets bigger, the other variable tends to get bigger.
means that if one variable gets bigger, the other variable tends to get smaller.
Where the points show a straight-line pattern.
Measures strength and direction from the scatterplot; e.g. if r= -.1, then it would be weak and negative.
Least Squares Regression Line
least square regression line; line that gives the best fit to the data set; minimizes the sum of the squares of the deviations from the line
A straight, best fit line for the data points that can be used to predict points and is a mathematical model
Slope of LSRL
For each unit increase in x, there is an approximate increase/decrease of b in y.
Intercept of LSRL
the estimated value of the response variable when the value of the explanatory variable is zero
Coefficient of Determination
explains how much percentage of the linear relationship between the explanatory and response variable there is
The vertical deviation between the observations & the LSRL (a.k.a. error: error=y-(y hat) or observed-predicted)
the error that occurs when the percents don't add up to 100%
Residual plots are graphs that show how much residual each value has. The mean of the residuals is usually 0, but sometimes it may not because of roundoff error
A point that lies far away in the x direction and seems to fit the pattern so that the correlation strengthens. If removed, the line direction would change.
The use of a regression line for prediction far outside the domain of values of the explanatory variable x that you used to obtain the line or curve. Such predictions are often not accurate.
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