Accounting
Anthropology
Archaeology
Art History
Banking
Biology & Life Science
Business
Business Communication
Business Development
Business Ethics
Business Law
Chemistry
Communication
Computer Science
Counseling
Criminal Law
Curriculum & Instruction
Design
Earth Science
Economic
Education
Engineering
Finance
History & Theory
Humanities
Human Resource
International Business
Investments & Securities
Journalism
Law
Management
Marketing
Medicine
Medicine & Health Science
Nursing
Philosophy
Physic
Psychology
Real Estate
Science
Social Science
Sociology
Special Education
Speech
Visual Arts
Finance
Q:
The ____________________ is the proportion of the total variation in the dependent variable explained by the regression model.
A. coefficient of determination
B. correlation coefficient
C. slope
D. standard error
Q:
If one of the assumptions of the regression model is violated, performing data transformations on the ____________ can remedy the situation.
A. independent variable
B. slope
C. predictor variable
D. response variable
Q:
The _____ distribution is used for testing the significance of the slope term.
A. t
B. z
C. r
D. r2
Q:
If there is significant autocorrelation present in a data set, the ________________ assumption is violated.A. normalityB. independence of error termsC. = 0D. constant variation
Q:
Any value of the error term in a regression model _____________ any other value of the error term.
A. increases with
B. is dependent on
C. is independent of
D. is exactly the same as
Q:
The _____________ is the range of the previously observed values of x.
A. population region
B. experimental region
C. slope
D. coefficient of determination
Q:
In a simple linear regression model, the slope term is the change in the mean value of y associated with _____________ in x.
A. a corresponding increase
B. a variable change
C. no change
D. a one-unit increase
Q:
In a simple linear regression model, the intercept term is the mean value of y when x equals _____.A. 1B. 0C. -1D. y
Q:
The least squares point estimates of the simple linear regression model minimize the ____________.
A. SS error
B. total variance
C. MS error
D. explained variance
Q:
The ___________ of the simple linear regression model is the value of y when the mean value of x is zero.
A. y-intercept
B. slope
C. independent variable
D. response variable
Q:
The range for r2 is between 0 and 1, and the range for r is between ____________.A. 0 and 1B. -1 and 1C. -1 and 0D. There is no limit for r.
Q:
The ____________ assumption requires that all variation around the regression line should be equal at all possible values (levels) of the ___________variable.
A. control variance, independent
B. control variance, dependent
C. constant variance, independent
D. constant variance, dependent
Q:
The coefficient of determination measures the _____________ explained by the simple linear regression model.
A. correlation
B. proportion of variation
C. standard error
D. mean square error
Q:
The simple linear regression (least squares method) minimizes
A. the explained variation.
B. SSyy.
C. total variation.
D. SSxx.
E. SSE.
Q:
After plotting the data points on a scatter diagram, we have observed an inverse relationship between the independent variable (X) and the dependent variable (Y). Therefore, we can expect both the sample ___________ and the sample _____________ to be negative values.
A. intercept, slope
B. slope, coefficient of determination
C. intercept, correlation coefficient
D. slope, correlation coefficient
E. slope, standard error of estimate
Q:
The strength of the relationship between two quantitative variables can be measured by
A. the slope of a simple linear regression equation.
B. the y-intercept of the simple linear regression equation.
C. the coefficient of correlation.
D. the coefficient of determination.
E. both the coefficient of correlation and the coefficient of determination.
Q:
For a given data set, value of X, and confidence level, if all the other factors are constant, the confidence interval for the mean value of Y will ___________ be wider than the corresponding prediction interval for the individual value of Y.
A. always
B. sometimes
C. never
Q:
For the same set of observations on a specified dependent variable, two different independent variables were used to develop two separate simple linear regression models. A portion of the results is presented below. Based on the results given above, we can conclude that
A. a prediction based on Model 1 is better than a prediction based on Model 2.
B. a prediction based on Model 2 is better than a prediction based on Model 1.
C. there is no difference in the predictive ability between Model 1 and Model 2.
D. there is not sufficient information to determine which of the two models is superior for prediction purposes.
Q:
Which of the following is a violation of the independence assumption?
A. negative autocorrelation
B. a pattern of cyclical error terms over time
C. positive autocorrelation
D. a pattern of alternating error terms over time
E. All of the other choices are correct.
Q:
When the constant variance assumption holds, a plot of the residual versus x
A. fans out.
B. funnels in.
C. fans out, but then funnels in.
D. forms a horizontal band pattern.
E. suggests an increasing error variance.
Q:
In a simple regression analysis for a given data set, if the null hypothesis = 0 is rejected, then the null hypothesis = 0 is also rejected. This statement is ___________ true.A. alwaysB. neverC. sometimes
Q:
In simple regression analysis, if the correlation coefficient is a positive value, thenA. the y-intercept must also be a positive value.B. the coefficient of determination can be either positive or negative, depending on the value of the slope.C. the least squares regression equation could have either a positive or a negative slope.D. the slope of the regression line must also be positive.E. the standard error of estimate can have either a positive or a negative value.
Q:
The following results were obtained as part of a simple regression analysis.r2 = .9162F statistic from the F table = 3.59Calculated value of F from the ANOVA table = 81.87 = .05p-value = .000The null hypothesis of no linear relationship between the dependent variable and the independent variableA. is rejected.B. cannot be tested with the given information.C. is not rejected.D. is not an appropriate null hypothesis for this situation.
Q:
The correlation coefficient may assume any value betweenA. 0 and 1.B. - and .C. 0 and 8.D. -1 and 1.E. -1 and 0.
Q:
A simple regression analysis with 20 observations would yield ________ degrees of freedom error and _________degrees of freedom total.
A. 1, 20
B. 18, 19
C. 19, 20
D. 1, 19
E. 18, 20
Q:
In simple regression analysis, the quantity is called the __________ sum of squares.
A. total
B. explained
C. unexplained
D. error
Q:
In simple regression analysis, the quantity that gives the amount by which Y (dependent variable) changes for a unit change in X (independent variable) is called theA. coefficient of determination.B. slope of the regression line.C. y-intercept of the regression line.D. correlation coefficient.E. standard error.
Q:
In simple regression analysis, the standard error is ___________ greater than the standard deviation of y values.
A. always
B. sometimes
C. never
Q:
The ___________ the r2 and the __________ the s (standard error), the stronger the relationship between the dependent variable and the independent variable.
A. higher, lower
B. lower, higher
C. lower, lower
D. higher, higher
Q:
For the same value of X (independent variable), the confidence interval for the average value of Y (dependent variable) is __________________ the prediction interval for the individual value of Y.
A. larger than
B. smaller than
C. the same as
D. sometimes larger than, sometimes smaller than
Q:
Which of the following is a violation of one of the major assumptions of the simple regression model?
A. The error terms are independent of each other.
B. A histogram of the residuals forms a bell-shaped, symmetrical curve.
C. The error terms show no pattern.
D. As the value of x increases, the value of the error term also increases.
Q:
The least squares regression line minimizes the sum of the
A. differences between actual and predicted Y values.
B. absolute deviations between actual and predicted Y values.
C. absolute deviations between actual and predicted X values.
D. squared differences between actual and predicted Y values.
E. squared differences between actual and predicted X values.
Q:
The _____________ measures the strength of the linear relationship between the dependent variable and the independent variable.
A. correlation coefficient
B. distance value
C. Y-intercept
D. residual
Q:
All of the following are assumptions of the error terms in the simple linear regression model except
A. errors are normally distributed.
B. error terms have a mean of zero.
C. error terms have a constant variance.
D. error terms are dependent on each other.
Q:
If successive values of the residuals are close together, then there is a ___________ autocorrelation and the value of the Durbin-Watson statistic is _________.
A. negative, large
B. positive, small
C. negative, small
D. positive, large
Q:
When the assumption of __________ residuals (error terms) is violated, the Durbin-Watson statistic is used to test to determine if there is significant _____________ among the residuals.
A. normality, probability
B. independent, probability
C. independent, autocorrelation
D. normality, autocorrelation
Q:
In a simple linear regression analysis, the correlation coefficient (a) and the slope (b) ___________ have the same sign.
A. always
B. sometimes
C. never
Q:
The point estimate of the variance in a regression model is
A. SSE.
B. b0.
C. MSE.
D. b1.
Q:
If the Durbin-Watson statistic is greater than (4 -dL), then we conclude thatA. there is significant positive autocorrelation.B. there is significant negative autocorrelation.C. there is significant autocorrelation, but we cannot identify whether it is positive or negative.D. the test result is inconclusive.
Q:
If the Durbin-Watson statistic is less than dL, then we conclude that
A. there is significant positive autocorrelation.
B. there is significant negative autocorrelation.
C. there is significant autocorrelation, but we cannot identify whether it is positive or negative.
D. the test results are inconclusive.
Q:
The Durbin-Watson test statistic ranges fromA. -4 to 4.B. 0 to 4.C. 0 to 3.D. -1 to 1.E. 0 to 1.
Q:
What value of the Durbin-Watson statistic indicates that there is no autocorrelation present in time-ordered data?A. 1B. -1C. 2D. -2E. 0
Q:
When using simple linear regression, we would like to use confidence intervals for the ___________ and prediction intervals for the ___________ at a given value of x.
A. Individual y-value, mean y-value
B. mean y-value, individual y-value
C. slope, mean slope
D. y-intercept, mean y-intercept
Q:
The estimated simple linear regression equation minimizes the sum of the squared deviations between each value of Y and the line.
Q:
The standard error of the estimate (standard error) is the estimated standard deviation of the distribution of the independent variable (X) for all values of the dependent variable (Y).
Q:
A significant positive correlation between X and Y implies that changes in X cause Y to change.
Q:
The notation refers to the average value of the dependent variable Y.
Q:
The least squares simple linear regression line minimizes the sum of the vertical deviations between the line and the data points.
Q:
The slope of the simple linear regression equation represents the average change in the value of the dependent variable per unit change in the independent variable (X).
Q:
In simple linear regression analysis, we assume that the variance of the independent variable (X) is equal to the variance of the dependent variable (Y).
Q:
The correlation coefficient is the ratio of explained variation to total variation.
Q:
If r = -1, then we can conclude that there is a perfect relationship between X and Y.
Q:
In simple linear regression analysis, if the error terms exhibit a positive or negative autocorrelation over time, then the assumption of constant variance is violated.
Q:
In a simple linear regression model, the coefficient of determination not only indicates the strength of the relationship between the independent and dependent variables, but also shows whether the relationship is positive or negative.
Q:
In simple regression analysis, r2 is a percentage measure and measures the proportion of the variation explained by the simple linear regression model.
Q:
When there is positive autocorrelation, over time, negative error terms are followed by positive error terms and positive error terms are followed by negative error terms.
Q:
When using simple regression analysis, if there is a strong correlation between the independent and dependent variables, then we can conclude that an increase in the value of the independent variable causes an increase in the value of the dependent variable.
Q:
The simple coefficient of determination is the proportion of total variation explained by the regression line.
Q:
The experimental region is the range of the previously observed values of the dependent variable.
Q:
The residual is the difference between the observed value of the dependent variable and the predicted value of the dependent variable.
Q:
A simple linear regression model is an equation that describes the straight-line relationship between a dependent variable and an independent variable.
Q:
The error term is the difference between an individual value of the dependent variable and the corresponding mean value of the dependent variable.
Q:
The dependent variable is the variable that is being described, predicted, or controlled.
Q:
A regression model was applied to a data set with 8 time-ordered observations. The residuals for these observations are given below. Calculate the Durbin-Watson statistic (d).
Q:
Based on 30 time-ordered observations from a simple regression, we have determined the Durbin-Watson statistic, d = 2.71. At = .05, test to determine if there is any evidence of negative autocorrelation. State your conclusions.
Q:
Based on 25 time-ordered observations from a simple regression model, we have determined the Durbin-Watson statistic, d = 1.39. At = .05, test to determine if there is any evidence of positive autocorrelation. State your conclusions.
Q:
The following time-sequenced observations of actual and predicted values of the dependent variable (demand) are obtained from a simple regression model. Determine the Durbin-Watson statistic (d).
Q:
Consider the following partial computer output from a simple linear regression analysis.Test to determine if there is a significant correlation between x and y. Use H0: = 0 versus Ha: 0 with = .01. Show the test statistic used in the decision.
Q:
Consider the following partial computer output from a simple linear regression analysis.Determine the 95 percent prediction interval for the mean value of y when x = 9.00. Givens: -x = 129.03, -x2 = 1178.547
Q:
Consider the following partial computer output from a simple linear regression analysis.Determine the 95 percent confidence interval for the mean value of y when x = 9.00. Givens: -x = 129.03 and -x2 = 1178.547
Q:
Consider the following partial computer output from a simple linear regression analysis.What is the correlation coefficient?
Q:
Consider the following partial computer output from a simple linear regression analysis.What is the coefficient of determination?
Q:
Consider the following partial computer output from a simple linear regression analysis.What is the explained variance?
Q:
Consider the following partial computer output from a simple linear regression analysis.What is the unexplained variance?
Q:
Consider the following partial computer output from a simple linear regression analysis.Calculate the SSE.
Q:
Consider the following partial computer output from a simple linear regression analysis. Calculate the MSE.
Q:
Consider the following partial computer output from a simple linear regression analysis.What is the predicted value of y when x = 9.00?
Q:
Consider the following partial computer output from a simple linear regression analysis.Calculate the t statistic used to test H0: 1 = 0 versus Ha: 1 0 at = .001.
Q:
Consider the following partial computer output from a simple linear regression analysis.Test H0: 1 = 0 versus Ha: 1 0 by setting = .001. What do you conclude about the relationship between y and x?
Q:
Consider the following partial computer output from a simple linear regression analysis. Write the equation of the least squares line.