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1

Names for y-variable

Response Variable, Output Variable, Dependent Variable

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2

Names for x-variable

Predictor, Explanatory Variable, Input Variable, Independent Variable

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3

What type of graph should be used for depicting the relationship between two quantitative, continuous variables?

Scatterplot

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4

What must be captured in describing a scatterplot?

Direction, Form, Strength, Unusual Features

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5

Correlation

Measure of linear dependence between two continuous quantitative variables.

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6

What does correlation measure?

Strength and Direction for a linear relationship.

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7

5 Properties for the linear correlation coefficient *r*

r is between -1 and 1.

r measures the strength of a linear relationship.

r is very sensitive to outliers.

r is unitless, so a different scale for a variable does not change r.

r is not affected by the choice of x or y. x and y are interchangeable.

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8

Regression

Statistical method of estimating the value of a variable from another variable or variables.

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9

Regression Equation

Equation of best fit line for a scatterplot using regression

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10

Simple Linear Regression

Regression when done with one independent variable and the regression equation is linear.

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11

How many y values are there per x value on a scatterplot?

A distribution of values, so no specific number.

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12

Least-Squares Regression

Method that estimates slope and y-intercept to yield the best fit line for a scatterplot.

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13

What range of data is safe to use with a regression

Only the given range of x-values. The farther you go from this range, the less accurate the prediction will be.

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14

Residual

The difference between the observed y value and the predicted y value.

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15

Residual less than 0 means that

the predicted value is an overestimate

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16

Residual greater than 0 means that

the predicted value is an underestimate.

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17

How do you verify that a linear regression model is appropriate for a data set?

Check if

The scatterplot suggests a positive or negative linear relationship

The distribution of residuals is approximately normal with a mean of 0

The residual plot contains no obvious pattern

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18

Smaller residuals mean what about the model?

The model is a better fit.

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19

rÂ˛

The amount of total variation explained by the regression line.

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