Chapter 4 Regression Models

1) In regression, an independent variable
is sometimes called a response variable.
2) One purpose of regression is to
understand the relationship between variables.
3) One purpose of regression is to predict
the value of one variable based on the other variable.
4) The variable to be predicted is the
dependent variable.
5) The dependent variable is also called
the response variable.

6) A scatter diagram is a graphical
depiction of the relationship between the dependent and independent variables.

7) In a scatter diagram, the dependent
variable is typically plotted on the horizontal axis.

8) There is no relationship between
variables unless the data points lie in a straight line.
9) In any regression model, there is an
implicit assumption that a relationship exists between the variables.
10) In regression, there is random error
that can be predicted.
11) Estimates of the slope, intercept, and
error of a regression model are found from sample data.

12) Error is the difference in the actual
value and the predicted value.

13) The regression line minimizes the sum
of the squared errors.
14) In regression, a dependent variable is
sometimes called a predictor variable.

15) Summing the error values in a
regression model is misleading because negative errors cancel out positive
errors.
16) The SST measures the total variability
in the dependent variable about the regression line.
MODEL

17) The SSE measures the total variability
in the independent variable about the regression line.

18) The SSR indicates how much of the
total variability in the dependent variable is explained by the regression
model.
19) The coefficient of determination takes
on values between -1 and + 1.
20) The coefficient of determination gives
the proportion of the variability in the dependent variable that is explained
by the regression equation.

21) The correlation coefficient has values
between ?1 and +1.
22) Errors are also called residuals.

23) The regression model assumes the error
terms are dependent.

24) The regression model assumes the
errors are normally distributed.
25) The errors in a regression model are assumed
to have an increasing mean.
26) The errors in a regression model are
assumed to have zero variance.

27) If the assumptions of regression have
been met, errors plotted against the independent variable will typically show
patterns.

28) Often, a plot of the residuals will
highlight any glaring violations of the assumptions.

29) The error standard deviation is
estimated by MSE.
30) The standard error of the estimate is
also called the variance of the regression.