Chapter 9 Quiz

. What is the constant?

The value of the dependent variable when the independent variable is zero.

The slope of the line that best describes the relationship between the independent and dependent variables.

A measure of the accuracy of the predictions made by the regression line

It measures how precisely the regression model estimates the coefficient's value for the population

. What symbol represents the unstandardized beta coefficient?

y_i

α

β

X

. What measures how precisely the regression model estimates a coefficient's value for the population?

standard error of the coefficient

confidence interval

standard error of the estimate

confidence range

. Regression analysis is used to:

Determine the degree of association between variables

Determine the median

Determine the amount variation within a variable

Determine the degree of bias within a variable

. Which symbol represents the constant?

x3

α

Yi

β1

. The notion of _______ helps assess the independent effect of each independent variable on the dependent variable while we make the assumption that the other independent variables are held constant:

Control

Standard Error

Regression

Omitted variable bias

. At the 95% confidence interval, a significance level of 0.02 would compel you to:

Reject the research hypothesis

Reject the null hypothesis

Accept the null hypothesis

Prove the null hypothesis

. What is the minimum number of observations you should have for each independent variable in regression analysis

1

10

100

1,000

. What is the coefficient in regression?

It is the expected change in the dependent variable for a one-unit change in the independent variable

It is the expected change in the independent variable for a one-unit change in the dependent variable

It is the significance level for the test

The confidence interval

. A constant is considered interpretable when:

When the variable's scale has a 0 associated with it

When the variable's scale has a 1 associated with it

When the variable does not have any variation

When the variable's size is large

. The difference between R² and adjusted R²is that

Adjusted R² always increases with each independent variable added while R²does not

Adjusted R² takes the number of independent variables into consideration

R²is better in explaining the amount of variation

There is really no difference between the two, especially when the number of independent variables is large

. Omitted variable bias is

Leaving out independent variables that explain variation in the dependent variable

Leaving out dependent variables that explain variation in the independent variable

Especially problematic when regression analysis includes multiple independent variables

Especially problematic when regression analysis includes multiple dependent variables

. One strength of multivariate regression is

an increase in the significance level of each independent variable

the confidence interval for each independent variable should be narrower when several independent variables are included in a regression equation

we save time from performing individual univariate regressions

we can tell which independent variable is the most significant in explaining variation in the dependent variable

. If in a linear regression model the dependent variable is "Test Score Percentage" and independent variable is "Hours Studied", a coefficient of 3 could be interpreted as

For every three hours of study, the expected test score increases by one point

For every hour studied, the expected test score percentage increases by three points

For every one unit increase in test score percentage, the expected number of hours studied is three

For every three hours of study, a test score is expected to increase by three points

. Multicollinearity occurs when

Two dependent variables in an equation are highly correlated

Two independent variables in an equation are highly correlated

The dependent variable and independent variable in an equation are highly correlated

When an independent variable with explanatory power is excluded

. Logistic regression is used for

Ordinal dependent variables

Interval dependent variables

Ratio dependent variables

Binary dependent variables

. In regression, when a confidence interval contains a possibility of 0 for an independent variable's coefficient

the coefficient is likely significant

the coefficient cannot be significant because there is the possibility of there being no correlation between the independent and dependent variables in the population

the coefficient has a small effect on the dependent variable

the sample is not a representative one

. The three S's (sign, size, significance) are relevant for the:

unstandardized beta coefficient for each independent variable

the constant

the p-value

the dependent variable

. Why is the sign of a coefficient important?

it tells us whether a coefficient is significant

a positive coefficient indicates a large relationship; a negative coefficient indicates a small relationship

it is used in the determination of the confidence interval

it tells us whether the direction of a research hypothesis is confirmed

. The standard error of the estimate:

tells us whether there was a lot of error in the estimation of the coefficient

tells us whether the results can be applied to the population from which a sample was derived

is the average of all the deviations between the actual and predicted values of the dependent variable for given values of the independent variable(s)

is used to estimate the confidence interval for the population

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