Simple Linear Regression Q5

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 1 4 2 3 3 2 4 1 5 ​ The least squares estimate of the slope or b1 equals

-1

In a regression analysis, the regression equation is given by y = 12 - 6x. If SSE = 510 and SST = 1000, then the coefficient of determination is

.49

The following information regarding a dependent variable (y) and an independent variable (x) is provided. y x 4 2 3 1 4 4 6 3 8 5 ​ SSE = 6SST = 16 ​ The coefficient of determination is

.625

If a data set produces SSR = 400 and SSE = 100, then the coefficient of determination is

.80

In regression analysis, the error term ε is a random variable with a mean or expected value of

0.

The following information regarding a dependent variable (y) and an independent variable (x) is provided. y x 4 2 3 1 4 4 6 3 8 5 ​ SSE = 6SST = 16 ​ The least squares estimate of the slope is

1

4 / 4 pts The following information regarding a dependent variable (y) and an independent variable (x) is provided. y x 4 2 3 1 4 4 6 3 8 5 ​ SSE = 6SST = 16 ​ The MSE is

2

The following information regarding a dependent variable (y) and an independent variable (x) is provided. y x 4 2 3 1 4 4 6 3 8 5 ​ SSE = 6SST = 16 ​ The least squares estimate of the y-intercept is

2

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 1 4 2 3 3 2 4 1 5 ​ The point estimate of y when x = 2 is

4

You are given the following information about y and x. Dependent Variable (y) Independent Variable (x) 5 1 4 2 3 3 2 4 1 5 ​ The least squares estimate of the intercept or b0 equals

6

In a regression and correlation analysis, if r^2 = 1, then

SSE must be equal to zero.

​Which of the following is correct?

SST = SSR + SSE

In simple linear regression analysis, which of the following is not true?

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

In the following estimated regression equation y^= b0 + b1x,

b1 is the slope.

In regression analysis, if the independent variable is measured in pounds, the dependent variable

can be measured in any units.

A least squares regression line

can be used to predict a value of y if the corresponding x value is given.

The coefficient of determination

cannot be negative

In simple linear regression, r^2 is the

coefficient of determination.

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. ​ y^= 120 - 10x ​ Based on the above estimated regression equation, if price is increased by 2 units, then demand is expected to

decrease by 20 units.

A regression analysis between demand (y in 1000 units) and price (x in dollars) resulted in the following equation: ​ y^= 9 - 3x ​ The above equation implies that if the price is increased by $1, the demand is expected to

decrease by 3000 units.

The model developed from sample data that has the form of y^= b0 + b1x is known as the

estimated regression equation.

A regression analysis between sales (in $1000) and price (in dollars) resulted in the following equation: ​ y^= 60 - 8x ​ The above equation implies that an

increase of $1 in price is associated with a decrease of $8000 in sales.

SSE can never be

larger than SST.

Larger values of r2 imply that the observations are more closely grouped about the

least squares line.

It is possible for the coefficient of determination to be

less than 1.

In a simple linear regression analysis (where y is a dependent and x an independent variable), if the y-intercept is positive, then

the estimated regression line intercepts the positive y-axis.