Compute b 1 and b 0 (to 1 decimal). b 1 b 0 Complete the estimated regression eq

Question

Compute b1 and b0 (to 1 decimal).
b1  
b0  

Complete the estimated regression equation (to 1 decimal).
=  +  x

What is the variable cost per unit produced (to 1 decimal)?
$

Compute the coefficient of determination (to 3 decimals). Note: report r2 between 0 and 1.
r2 =  

What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)?
%

The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)?
$

Production Volume (units) Total Cost ($) 400 3,900 450 4,900 550 5,300 600 5,800 700 6,300 750 6,900

Explanation / Answer

Answer:

Compute b1 and b0 (to 1 decimal).
b1=  7.6
b0= 1146.7

Complete the estimated regression equation (to 1 decimal).
y = 1146.7 + 7.6 x

What is the variable cost per unit produced (to 1 decimal)?
$ 7.6

Compute the coefficient of determination (to 3 decimals). Note: report r2 between 0 and 1.
r2 =  0.959

What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)?
95.9 %

The company's production schedule shows 500 units must be produced next month. What is the estimated total cost for this operation (to the nearest whole number)?

4947

Regression Analysis

r²

0.959

n

6

r

0.979

k

1

Std. Error

241.523

Dep. Var.

y

ANOVA table

Source

SS

df

MS

F

p-value

Regression

5,415,000.0000

1  

5,415,000.0000

92.83

.0006

Residual

233,333.3333

4  

58,333.3333

Total

5,648,333.3333

5  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=4)

p-value

95% lower

95% upper

Intercept

1,146.6667

464.1599

2.470

.0689

-142.0479

2,435.3812

x

7.6000

0.7888

9.635

.0006

5.4099

9.7901

Predicted values for: y

95% Confidence Interval

95% Prediction Interval

x

Predicted

lower

upper

lower

upper

Leverage

500

4,946.667

4,627.409

5,265.924

4,203.971

5,689.362

0.227

Regression Analysis

r²

0.959

n

6

r

0.979

k

1

Std. Error

241.523

Dep. Var.

y

ANOVA table

Source

SS

df

MS

F

p-value

Regression

5,415,000.0000

1  

5,415,000.0000

92.83

.0006

Residual

233,333.3333

4  

58,333.3333

Total

5,648,333.3333

5  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=4)

p-value

95% lower

95% upper

Intercept

1,146.6667

464.1599

2.470

.0689

-142.0479

2,435.3812

x

7.6000

0.7888

9.635

.0006

5.4099

9.7901

Predicted values for: y

95% Confidence Interval

95% Prediction Interval

x

Predicted

lower

upper

lower

upper

Leverage

500

4,946.667

4,627.409

5,265.924

4,203.971

5,689.362

0.227

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