Chapter 13 Flashcards

1
Q

A group of techniques to measure the relationship between two variables

A

Correlation analysis

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2
Q

The variable that is being predicted or estimated

A

dependent variable

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3
Q

The dependent variable is measured on which axis

A

Y-axis

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4
Q

A measure of strength of the linear relationship between two variables of interval or ratio scale

A

Correlation coefficient

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5
Q

6 characteristics of a correlation coefficient

A
  1. Designated with letter r
  2. Between -1 and 1
  3. Shows direction and strength of linear relationship between two variables
  4. Closer to 0 means less of a correlation
  5. Near 1 shows strong correlation
  6. near -1 shows strong correlation
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6
Q

an equation that expresses the linear relationship between two variables

A

Regression equation

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7
Q

A mathematical procedure that uses the data to position a line with the objective of minimizing the sum of the squares of the vertical distances between the actual y-values and the predicted values of y.

A

Least squares principle

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8
Q

What is the symbol y hat

A

The estimated y value for a given x

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9
Q

What is the y hat formula for linear regression equation

A

y hat = a+bx

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10
Q

The difference between the actual y values and the predicted y values, known as the error values

A

Residuals

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11
Q

A measure of dispersion, or scatter, of the observed values around the line of regression for a given value of x

A

Standard error of estimate

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12
Q

What does a small standard error mean

A

The data is close to the regression line
Predicted Y will have small error

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13
Q

The proportion of the total variation in the dependent variable Y that is explained by the variation in x

A

The coefficient of determination

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14
Q

Assumptions for linear regression

A
  1. Corresponding y values are normally distributed
  2. The means of the distributions lie on the regression line
  3. The standard deviations are the same.
  4. Y values are statistically independent
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15
Q

If the standard error of estimate is large, what would you expect for the coefficient of determination

A

It would be small

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16
Q

Is used when the regression equation is used to predict the mean value of Y for a given value of X

A

Confidence interval

17
Q

Is used when the regression equation is used to predict an individual y for a given value of x

A

Prediction interval