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The swinging pendulum Refer to Exercise 33. Here is Minitab output from separate regression analyses of the two sets of transformed pendulum data:

Do each of the following for both transformations.

(a) Give the equation of the least-squares regression line. Define any variables you use.

(b) Use the model from part (a) to predict the period of a pendulum with length of 80 centimeters. Show your work.

(c) Interpret the value of s in context

Short Answer

Expert verified

a). Transformation 1:P^eriod=-0.08594+0.209999length

Transformation 2:P^eriod2=-0.15465+0.042836×length

b). Transformation 1:1.7924

Transformation 2:1.8089

c). The expected error from the prediction of the square of the period is 0.105469.

Step by step solution

01

Part (a) Step 1: Given Information

02

Part (a) Step 2: Given Information

In the question there are two transformations given and the computer output for each is also given. Thus, we have,

For transformation 1:

Thus, the general equation of the regression equation is as:

localid="1650609318117" P^eriod=a+blength

Thus, the value of the slope and the constant is given in the computer output as:

a=-0.08594

b=0.209999

Thus, the regression equation is as follows:

localid="1650609326733" P^eriod=a+blength

localid="1650609346683" P^eriod=-0.08594+0.209999length

03

Part (a) Step 3: Explanation

For transformation 2 :

Thus, the general equation of the regression equation is as:

P^eriod2=a+b×length

Thus, the value of the slope and the constant is given in the computer output as:

a=-0.15465

b=0.042836

Thus, the regression equation is as follows:

localid="1650609373265" P^eriod2=a+b×length

localid="1650609386019" ⇒P^eriod2=-0.15465+0.042836×length

04

Part (b) Step 4: Given Information

05

Part (b) Step 5: Explanation

We need to find out the period of a pendulum with the length 80centimeters using the part (a) as:

For transformation 1 :

Thus, the regression equation is as follows:localid="1650609408367" P^eriod=a+blength

⇒P^eriod=-0.08594+0.209999length

Then by evaluating we have,

localid="1650609425383" P^eriod=a+blength

localid="1650609440792" ⇒P^eriod=-0.08594+0.209999length

=-0.08594+0.20999980

=1.7924

06

Part (b): Step 6: Explanation

For transformation 2 :

Thus, the regression equation is as follows:

Period=2a+b×length

⇒Period2=-0.15465+0.042836×length

Then by evaluating we have,

localid="1650609465411" Period=a+b×length

⇒Period2=-0.15465+0.042836×80

⇒Period2=3.27223

⇒Period=3.27223

⇒Period=1.8089

07

Part (c) Step 7: Given Information

08

Part (c) Step 8: Explanation

As we need to interpret the value of s in this context. Thus, we have,

For transformation 1:

Thus, the regression equation is as follows:

P^eriod=a+blength

⇒P^eriod=-0.08594+0.209999length

And the value of sis given as,

s=0.0464223

This means that the expected error from the prediction of the period is 0.0464223.

09

Part (c) Step 9: Explanation

For transformation 2:

Thus, the regression equation is as follows:

Period2=a+b×length

localid="1650609508643" ⇒Period2=-0.15465+0.042836×length

And the value of sis given as,

s=0.105469

This means that the expected error from the prediction of the square of the period is 0.105469.

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Most popular questions from this chapter

A 95%confidence interval for the population slope βis

(a) 1.0466±149.5706.

(b) 1.0466±0.2415.

(c) 1.0466±0.2387.

(d) 1.0466±0.1983.

(e) 1.0466±0.1126.

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(c) the average increase in selling price in a population of units when the appraised value increases by \( 1000.

(d) the average selling price in a population of units when a unit's appraised value is 0.

(e) the average increase in appraised value in a sample of 16 units when the selling price increases by \) 1000.

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(c) [ln(time), ln(distance)]

(d) [ln(distance), time]

(e) [distance, ln(time)]

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(a) The probability that the new integrated circuit has the same life span as the current model is 0.035.

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