is310 ch14

a. dependent variable

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

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A least squares regression line a. may be used to predict a value of y if the corresponding x value is given b. implies a cause-effect relationship between x and y c. can only be determined if a good linear relationship exists between x and y d. None of these alternatives is correct.

d. increase of $1 in advertising is associated with an increase of $4,000 in sales

A regression analysis between sales (Y in $1000) and advertising (X in dollars) resulted in the following equation = 30,000 + 4 X The above equation implies that an a. increase of $4 in advertising is associated with an increase of $4,000 in sales b. increase of $1 in advertising is associated with an increase of $4 in sales c. increase of $1 in advertising is associated with an increase of $34,000 in sales d. increase of $1 in advertising is associated with an increase of $4,000 in sales

a. narrower

Compared to the confidence interval estimate for a particular value of y (in a linear regression model), the interval estimate for an average value of y will be a. narrower b. wider c. the same d. None of these alternatives is correct.

b. the strength of the relationship between the dependent and the independent variables

Correlation analysis is used to determine a. the equation of the regression line b. the strength of the relationship between the dependent and the independent variables c. a specific value of the dependent variable for a given value of the independent variable d. None of these alternatives is correct.

c. standard error

If MSE is known, you can compute the a. r square b. coefficient of determination c. standard error d. all of these alternatives are correct

b. 1

If all the points of a scatter diagram lie on the least squares regression line, then the coefficient of determination for these variables based on this data is a. 0 b. 1 c. either 1 or -1, depending upon whether the relationship is positive or negative d. could be any value between -1 and 1

d. 64%

If the coefficient of correlation is 0.8, the percentage of variation in the dependent variable explained by the variation in the independent variable is a. 0.80% b. 80% c. 0.64% d. 64%

d. must be positive

If the coefficient of correlation is a negative value, then the coefficient of determination a. must also be negative b. must be zero c. can be either negative or positive d. must be positive

d. the slope of the line must be positive

If the coefficient of correlation is a positive value, then a. the intercept must also be positive b. the coefficient of determination can be either negative or positive, depending on the value of the slope c. the regression equation could have either a positive or a negative slope d. the slope of the line must be positive

a. must also be positive

If the coefficient of correlation is a positive value, then the slope of the regression line a. must also be positive b. can be either negative or positive c. can be zero d. cannot be zero

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If the coefficient of determination is 0.81, the coefficient of correlation a. is 0.6561 b. could be either + 0.9 or - 0.9 c. must be positive d. must be negative

c. can be either negative or positive

If the coefficient of determination is a positive value, then the coefficient of correlation a. must also be positive b. must be zero c. can be either negative or positive d. must be larger than 1

b. can be either -1 or +1

If the coefficient of determination is equal to 1, then the coefficient of correlation a. must also be equal to 1 b. can be either -1 or +1 c. can be any value between -1 to +1 d. must be -1

d. None of these alternatives is correct.

If there is a very weak correlation between two variables then the coefficient of correlation must be a. much larger than 1, if the correlation is positive b. much smaller than 1, if the correlation is negative c. any value larger than 1 d. None of these alternatives is correct.

a. there may or may not be any causal relationship between x and y

If two variables, x and y, have a good linear relationship, then a. there may or may not be any causal relationship between x and y b. x causes y to happen c. y causes x to happen d. None of these alternatives is correct.

a. zero

In a regression analysis, the error term is a random variable with a mean or expected value of a. zero b. one c. any positive value d. any value

c. is the dependent variable

In a regression analysis, the variable that is being predicted a. must have the same units as the variable doing the predicting b. is the independent variable c. is the dependent variable d. usually is denoted by x

d. SSR = SST

In a regression and correlation analysis if r2 = 1, then a. SSE = SST b. SSE = 1 c. SSR = SSE d. SSR = SST

b. SSE must be equal to zero

In a regression and correlation analysis if r2 = 1, then a. SSE must also be equal to one b. SSE must be equal to zero c. SSE can be any positive value d. SSE must be negative

d. None of these alternatives is correct.

In a simple regression analysis (where Y is a dependent and X an independent variable), if the Y intercept is positive, then a. there is a positive correlation between X and Y b. if X is increased, Y must also increase c. if Y is increased, X must also increase d. None of these alternatives is correct.

c. can be any units

In regression analysis if the dependent variable is measured in dollars, the independent variable a. must also be in dollars b. must be in some units of currency c. can be any units d. cannot be in dollars

b. used to predict the dependent variable

In regression analysis, the independent variable is a. used to predict other independent variables b. used to predict the dependent variable c. called the intervening variable d. the variable that is being predicted

d. regression model

In regression analysis, the model in the form y=Bo+B1x+z is called a. regression equation b. correlation equation c. estimated regression equation d. regression model

c. mean square error

In regression analysis, the unbiased estimate of the variance is a. coefficient of correlation b. coefficient of determination c. mean square error d. slope of the regression equation

a. The expected value of the error term is one.

In regression analysis, which of the following is not a required assumption about the error term ? a. The expected value of the error term is one. b. The variance of the error term is the same for all values of X. c. The values of the error term are independent. d. The error term is normally distributed.

b. The F test and the t test may or may not yield the same conclusion.

In simple linear regression analysis, which of the following is not true? a. The F test and the t test yield the same conclusion. b. The F test and the t test may or may not yield the same conclusion. c. The relationship between X and Y is represented by means of a straight line. d. The value of F = t2.

b. less than one

It is possible for the coefficient of determination to be a. larger than 1 b. less than one c. less than -1 d. None of these alternatives is correct.

c. least squares line

Larger values of r2 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. one dependent and one or more independent variables are related

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 these alternatives is correct.

d. decease by 20 units

Regression analysis was applied between demand for a product (Y) and the price of the product (X), and the following estimated regression equation was obtained. = 120 - 10 X Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to a. increase by 120 units b. increase by 100 units c. increase by 20 units d. decease by 20 units

b. is the square root of the coefficient of determination

The coefficient of correlation a. is the square of the coefficient of determination b. is the square root of the coefficient of determination c. is the same as r-square d. can never be negative

a. cannot be negative

The coefficient of determination a. cannot be negative b. is the square root of the coefficient of correlation c. is the same as the coefficient of correlation d. can be negative or positive

b. the regression model

The equation that describes how the dependent variable (y) is related to the independent variable (x) is called a. the correlation model b. the regression model c. correlation analysis d. None of these alternatives is correct.

a. prediction interval estimate

The interval estimate of an individual value of y for a given value of x is a. prediction interval estimate b. confidence interval estimate c. average regression d. x versus y correlation interval

b. confidence interval estimate

The interval estimate of the mean value of y for a given value of x is a. prediction interval estimate b. confidence interval estimate c. average regression d. x versus y correlation interval

a. regression equation

The mathematical equation relating the independent variable to the expected value of the dependent variable; that is, E(y) = 0 + 1x, is known as a. regression equation b. correlation equation c. estimated regression equation d. regression model

c. estimated regression equation

The model developed from sample data that has the form of is known as a. regression equation b. correlation equation c. estimated regression equation d. regression model

d. square root of MSE

The standard error is the a. t-statistic squared b. square root of SSE c. square root of SST d. square root of MSE