1. A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do so, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using the candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:

Prics ($)| Sales|

1.30| 100|

1.60| 90|

1.80| 90|

2.00| 40|

2.40| 38|

2.90| 32|

| |

What is the estimated slope (b1 for this data set?

161.3855

0.784

-0.3810

-48.193

POINT VALUE: 1.0 points

2. A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do so, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using the candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:

Prics ($)| Sales|

1.30| 100|

1.60| 90|

1.80| 90|

2.00| 40|

2.40| 38|

2.90| 32|

| |

What is the percentage of the total variation in candy bar sales explained by the regression model? 100%

88.54%

78.39%

48.19%

POINT VALUE: 1.0 points

3. A candy bar manufacturer is interested in trying to estimate how sales are influenced by the price of their product. To do so, the company randomly chooses 6 small cities and offers the candy bar at different prices. Using the candy bar sales as the dependent variable, the company will conduct a simple linear regression on the data below:

Prics ($)| Sales|

1.30| 100|

1.60| 90|

1.80| 90|

2.00| 40|

2.40| 38|

2.90| 32|

| |

What is the coefficient of correlation for these data?

0.8854

0.7839

-0.7839

-0.8854

POINT VALUE: 1. points

4. The coefficient of determination (r2) tells us

that the coefficient of correlation (r) is larger than one. whether r has any significance.

that we should not partition the total variation.

the proportion of total variation that is explained.

POINT VALUE: 1.0 points

5. The slope (b1) represents

Predicted value of Y when X = 0.

The estimated average change in Y per unit change in X.

The predicted value of Y.

Variation around the line of regression.

POINT VALUE: 1.0 points

6. Assuming a linear relationship between X and Y, if the coefficient of correlation (r) equals – 0.30, There is no correlation.

the slope (b 1) is negative.

Variable X is larger than variable Y.

The variance of X is negative.

POINT VALUE: 1. points

7. The Y-intercept (b0) represents the

Predicted value of Y when X = 0.

Change in estimated average Y per unit change in X.

Predicted value of Y.

Variation around the sample regression line

POINT VALUE: 1. points

8. The strength of the linear relationship between two numerical variables may be measured by the Scatter diagram

Slope

Coefficient of correlation

Y-intercept

POINT VALUE: 1. points

9. In a simple linear regression problem, r (correlation coefficient) and b1 May have opposite signs.

Must have the same sign.

Must have opposite signs.

Are equal

POINT VALUE: 1. points

10. The managers of a brokerage firm are interested in finding out if the number of clients a broker brings into the firm affects the sales generated by the broker. They sampled 12 brokers and determined the number of new clients they have enrolled in their lat year and their sales amount in thousands of dollars.

Clients (X)| Sales (Y)|

27| 52|

11| 37|

42| 64|

33| 55|

15| 29|

15| 34|

25| 58|

36| 59|

28| 44|

30| 48|

17| 31|

22| 38|

| |

The estimated equation of the line is

Y(hat)=17.7+1.12X

Y(hat)=-17.7+1.12X

Y(hat)=17.7-1.12X

Y(hat)=-17.7-1.12X

POINT VALUE: 1. points

11. The managers of a brokerage firm are interested in finding out if the number of clients a broker brings into the...