ISDS 361 A - Extra Credit Quiz #3 (Regression)

In regression analysis, the variable that is being predicted is a. called the dependent variable b. called the independent variable c. called the intervening variable d. usually denoted as x

a. called the dependent variable

The differences between the data points and the estimated regression line are called the a. residuals b. parameters c. coefficients d. none of the above

a. residuals

The prediction intervals are usually a. wider than the confidence intervals b. narrower than the confidence intervals c. the same as the confidence intervals d. undetermined

a. wider than the confidence intervals

To test for a significant regression relationship, we can conduct a hypothesis test to determine whether: a. β(1) is not zero b. β(0) is not zero c. R^2 is not zero d. all of the above

a. β(1) is not zero

Point estimators and predictors do not provide any information about the precision associated with the estimate and/or prediction. For that we need to develop a. sample statistics b. confidence intervals and prediction intervals c. coefficients d. all of the above

b. confidence intervals and prediction intervals

A prediction interval is an interval estimate of the a. individual value of x for a given value of y b. individual value of y for a given value of x c. mean value of x for a given value of y d. mean value of y for a given value of x

b. individual value of y for a given value of x

In the regression model, β(0) and β(1) are called the a. estimates b. parameters of the model c. dependent variables d. independent variables

b. parameters of the model

Narrower prediction and confidence intervals a. provide a lower degree of precision b. provide a higher degree of precision c. are harder to estimate d. are easier to estimate

b. provide a higher degree of precision

The method used to find the estimated linear regression equation is called a. correlation b. the least squares method c. significance testing d. none of the above

b. the least squares method

b(1) is a. the y-intercept of the estimated regression equation b. the slope of the estimated regression equation c. always a positive number d. none of the above

b. the slope of the estimated regression equation

Regression analysis is a statistical procedure for developing a mathematical equation that describes how a. one independent and one or more dependent variables are related b. several independent and several dependent variables are related c. one dependent and one or more independent variables are related d. none of the above

c. one dependent and one or more independent variables are related

In order to initially observe the relationship between the two variables we can use a a. histogram b. bar graph c. scatter plot d. pie chart

c. scatter plot

If the coefficient of determination is 0.9, the percentage of variation in the dependent variable explained by the variation in the independent variable is Select one: a. 0.9% b. 90% c. 10% d. 0.1%

b. 90%

b(0) is a. the estimate for β0 b. the y-intercept of the estimated regression equation c. both a and b d. none of the above

c. both a and b

The equation y(hat) = b(0) + b(1)x is called the a. regression model b. population model c. estimated regression equation d. parameter

c. estimated regression equation

Larger values of R^2 imply that the observations are more closely grouped about the a. average value of the independent variables b. average value of the dependent variable c. least squares line d. origin

c. least squares line

The coefficient of determination R^2 a. measures the goodness of fit of the estimated regression equation b. takes on values between 0 and 1 c. is interpreted as the percentage of variability in the dependent variable that is explained by the model d. all of the above

d. all of the above

A confidence interval is an interval estimate of the a. individual value of x for a given value of y b. individual value of y for a given value of x c. mean value of x for a given value of y d. mean value of y for a given value of x

d. mean value of y for a given value of x

In regression analysis, the expression y = β(0) + β(1) + ε is called the a. regression coefficient b. correlation equation c. estimated regression equation d. regression model

d. regression model