Question 1

Linear regression is a statistical method requiring the following variable types:&#10;A) Two variables at the ordinal level.&#10;B) One variable at the ordinal level and one at the scale level.&#10;C) Two variables at the scale level.&#10;D) None of the above.

Accepted Answer

Linear regression requires two variables at the scale level because it is based on the assumption of a linear relationship between two continuous variables. A variable at the ordinal level would not be appropriate for linear regression because it lacks equal intervals between its categories.

Question 2

SPSS Statistics calculates the following outputs for a linear regression analysis:&#10;A) Model Summary&#10;B) ANOVA&#10;C) Coefficients&#10;D) All of the above

Accepted Answer

A linear regression analysis in SPSS Statistics generates three main sections of output: Model Summary, ANOVA, and Coefficients. The Model Summary provides an overview of the model's fit, including R-squared, Adjusted R-squared, and standard error of the estimate. The ANOVA table tests the overall significance of the regression model and individual significance of the predictor variables. The Coefficients table provides estimates of the regression coefficients (i.e., slopes) and associated standard errors, t-values, and p-values. Therefore, all three outputs (Model Summary, ANOVA, and Coefficients) are important and should be examined together.

Question 3

Use the following statistics to determine the statistical significance of the regression: A) ANOVA Sig. B) R² Linear value C) Unstandardized Coefficient D) Standard Error of the Estimate

Accepted Answer

The ANOVA Sig. value (also known as the p-value) indicates the statistical significance of the regression. A value less than 0.05 is typically considered statistically significant, meaning that there is less than a 5% chance that the results were obtained by chance.

Question 4

Use the following SPSS Statistics tool to edit graphs:&#10;A) Chart Builder&#10;B) Descriptive Statistics&#10;C) Graph Editor&#10;D) Chart Editor

Accepted Answer

The correct answer is D: Chart Editor. The Chart Editor is a tool in SPSS Statistics that allows users to edit and customize graphs.

Question 5

The following statistics can be displayed on the scattergram: A) Regression equation B) R² Linear value C) Data points D) All of the above

Accepted Answer

All of the above options, including the regression equation, linear value, and data points, can be displayed on the scattergram to provide a comprehensive visual representation of the relationship between the variables being analyzed.

Question 6

Linear regression requires two variables at the ordinal level.

Accepted Answer

Linear regression requires at least one variable to be at the interval or ratio level. It does not necessarily require variables at the ordinal level.

Question 7

The SPSS Statistics tool labels the variables used in linear regression as independent and dependent.

Accepted Answer

This statement is true. In linear regression, one variable is designated as the dependent variable and one or more variables are designated as independent variables. The SPSS tool helps to identify and label these variables.

Question 8

A significance value of less than .05 indicates a statistically significant finding for an alpha-level of 5%.

Accepted Answer

A significance value (p-value) less than .05 indicates that the observed result is less likely to occur by chance, and is considered statistically significant at an alpha-level of 5%.

Question 9

The ANOVA output box contains the coefficient of determination.

Accepted Answer

The ANOVA output box contains the F-statistic, the p-value, and the degrees of freedom, but not the coefficient of determination (R-squared). R-squared can be obtained from regression output.

Question 10

Linear regression involves predicting the outcome of one variable (dependent) based on the input of another variable (independent).

Accepted Answer

This is the basic principle of linear regression.

Deck 11: Simple Linear Regression