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Q4). The following are advertised sale prices of color televisions at an electro

ID: 3376541 • Letter: Q

Question

Q4). The following are advertised sale prices of color televisions at an electronics store.

Part (a) Decide which variable should be the independent variable and which should be the dependent variable.

Independent variable: size; Dependent variable: sale price

Independent variable: sale price; Dependent variable: size     

Part (b) Make a scatter plot of the data.

Part (c) Does it appear from inspection that there is a relationship between the variables? Why or why not?

Yes, it appears that the television cost increases with television size.

No, there is no visible relationship between the variables.     

Part (d) Calculate the least squares line. Put the equation in the form of: ? = a + bx. (Round your answers to three decimal places.)

? = +   x

Part (e) Find the correlation coefficient r. (Round your answer to four decimal places.)

r = ( )

Is it significant?

Yes

No     

Part (f) Find the estimated sale price for a 34-inch television. (Use your equation from part (d). Round your answer to two decimal places.)

$  

Find the estimated sale price for a 51-inch television. (Use your equation from part (d). Round your answer to two decimal places.)
$

Part (g) Use the two points in part (f) to plot the least squares line.

Part (h)

Does it appear that a line is the best way to fit the data? Why or why not?

A line does appear to be the best way to fit the data because the data follow a weak positive trend.

A line is the best way to fit the data because the slope of the line is positive and the linear correlation is positive.     

A line does not appear to be the best way to fit the data because the data do not follow a linear trend.

A line does not appear to be the best way to fit the data because it does not touch all the data points.

Part (i) Are there any outliers in the above data?

Yes, (9, 157) is an outlier.

Yes, (40, 2177) is an outlier.     

Yes, (60, 2497) is an outlier.

No, there are no outliers.

Part (j) What is the slope of the least squares (best-fit) line? (Round your answer to three decimal places.)

( )
Interpret the slope.

As sale price of the television increases by $1, the size decreases by this many inches.

As the size of the television increases by one inch, the sale price increases by this many dollars.     

As sale price of the television increases by $1, the size increases by this many inches.

As the size of the television increases by one inch, the sale price decreases by this many dollars.

Size (inches) Sale Price ($) 9 157 20 197 27 267 31 447 35 1177 40 2177 60 2497 Sale Price 2500 2000 1500 1000 500F 10 20 30 40 50 L Size (in) 60

Explanation / Answer

Result:

Part (a) Decide which variable should be the independent variable and which should be the dependent variable.

Answer: Independent variable: size; Dependent variable: sale price

Independent variable: sale price; Dependent variable: size     

Part (b) Make a scatter plot of the data.

Top left Graph

Part (c) Does it appear from inspection that there is a relationship between the variables? Why or why not?

Answer: Yes, it appears that the television cost increases with television size.

No, there is no visible relationship between the variables.     

Part (d) Calculate the least squares line. Put the equation in the form of: ? = a + bx. (Round your answers to three decimal places.)

? = -746.362+ 54.701 x

Part (e) Find the correlation coefficient r. (Round your answer to four decimal places.)

r = (0.8915 )

Is it significant?

Answer: Yes

No     

Part (f) Find the estimated sale price for a 34-inch television. (Use your equation from part (d). Round your answer to two decimal places.)

Y = -746.3615+54.7006*34 =1113.4589

$  1113.46

Find the estimated sale price for a 51-inch television. (Use your equation from part (d). Round your answer to two decimal places.)

Y = -746.3615+54.7006*51 =2043.3691


$2043.37

Part (g) Use the two points in part (f) to plot the least squares line.

Part (h)

Does it appear that a line is the best way to fit the data? Why or why not?

A line does appear to be the best way to fit the data because the data follow a weak positive trend.

A line is the best way to fit the data because the slope of the line is positive and the linear correlation is positive.     

Answer: A line does not appear to be the best way to fit the data because the data do not follow a linear trend.

A line does not appear to be the best way to fit the data because it does not touch all the data points.

Part (i) Are there any outliers in the above data?

Yes, (9, 157) is an outlier.

Yes, (40, 2177) is an outlier.     

Yes, (60, 2497) is an outlier.

No, there are no outliers.

Part (j) What is the slope of the least squares (best-fit) line? (Round your answer to three decimal places.)

(54.701 )
Interpret the slope.

As sale price of the television increases by $1, the size decreases by this many inches.

Answer: As the size of the television increases by one inch, the sale price increases by this many dollars.     

As sale price of the television increases by $1, the size increases by this many inches.

As the size of the television increases by one inch, the sale price decreases by this many dollars.

  

Regression Analysis

0.7949

n

7

r

0.8915

k

1

Std. Error

490.144

Dep. Var.

Sale Price ($)

ANOVA table

Source

SS

df

MS

F

p-value

Regression

4,654,082.2977

1  

4,654,082.2977

19.37

.0070

Residual

1,201,203.4166

5  

240,240.6833

Total

5,855,285.7143

6  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=5)

p-value

95% lower

95% upper

Intercept

-746.3615

435.5093

-1.714

.1472

-1,865.8738

373.1508

Size (inches)

54.7006

12.4279

4.401

.0070

22.7536

86.6476

Regression Analysis

0.7949

n

7

r

0.8915

k

1

Std. Error

490.144

Dep. Var.

Sale Price ($)

ANOVA table

Source

SS

df

MS

F

p-value

Regression

4,654,082.2977

1  

4,654,082.2977

19.37

.0070

Residual

1,201,203.4166

5  

240,240.6833

Total

5,855,285.7143

6  

Regression output

confidence interval

variables

coefficients

std. error

   t (df=5)

p-value

95% lower

95% upper

Intercept

-746.3615

435.5093

-1.714

.1472

-1,865.8738

373.1508

Size (inches)

54.7006

12.4279

4.401

.0070

22.7536

86.6476