Qnt/351 week 4

The production department of Celltronics International wants to explore the relationship between the number of employees who assemble a subassembly and the number produced. As an experiment, 3 employees were assigned to assemble the subassemblies. They produced 16 during a one-hour period. Then 5 employees assembled them. They produced 27 during a one-hour period. The complete set of paired observations follows.

Number of
Assemblers

One-Hour
Production (units)

3

16

5

27

2

11

6

50

4

36

The dependent variable is production; that is, it is assumed that different levels of production result from a different number of employees.

b.

A scatter diagram is provided below. Based on it, does there appear to be any relationship between the number of assemblers and production?

   , as the number of assemblers , so does the production.

c.

Compute the correlation coefficient. (Negative amounts should be indicated by a minus sign. Round s_x, s_y and r to 3 decimal places.)

X

Y

( )²

( )²

( )( )

3  

16  

 is the dependent variable.

c.

Determine the correlation coefficient. (Round your answer to 2 decimal places.)

  Coefficient of correlation

 correlation between the variables.

The owner of Maumee Ford-Mercury-Volvo wants to study the relationship between the age of a car and its selling price. Listed below is a random sample of 12 used cars sold at the dealership during the last year.

Car

Age (years)

Selling Price ($000)

Car

Age (years)

Selling Price ($000)

1

9

8.1

7

8

7.6

2

7

6.0

8

11

8.0

3

11

3.6

9

10

8.0

4

12

4.0

10

12

6.0

5

8

5.0

11

6

8.6

6

7

10.0

12

6

8.0

a.

If we want to estimate selling price on the basis of the age of the car, which variable is the dependent variable and which is the independent variable?

   is the independent variable and  is the dependent variable.

b-1.

Determine the correlation coefficient. (Negative amounts should be indicated by a minus sign. Round your answers to 3 decimal places.)

X

Y

( )²

( )²

( )( )

9.0  

8.1  

 correlation between age of car and selling price. So,  H₀: ρ ≤ 0