Determine the best curvilinear trendline that maximizes R². IO A. OB. The best trendline is Power with an R2 value of. The equation is y= 00. (Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.) The best trendline is Logarithmic with an R² value of (Round the coefficient of the logarithm to one decimal The equation is y=( In (x) place as needed. Round all other values to three decimal places as needed.) O C. The best trendline is Exponential with an R² value of (Round the coefficient to one decimal place as needed. OD. The best trendline is Polynomial with an R² value of (Round to three decimal places as needed.) The equation is y= Round all other values to three decimal places as needed.) The equation is y=(x²-

Advanced Engineering Mathematics
10th Edition
ISBN:9780470458365
Author:Erwin Kreyszig
Publisher:Erwin Kreyszig
Chapter2: Second-order Linear Odes
Section: Chapter Questions
Problem 1RQ
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Question
The Helicopter Division of Aerospatiale is studying assembly costs at its Marseilles plant. Past data indicates the accompanying data of number of labor hours per helicopter. Reduction in labor hours
over time is often called a "learning curve" phenomenon. Using these data, apply simple linear regression and examine the residual plot. What do you conclude? Construct a scatter chart and use
the Excel Trendline feature to identify the best type of curvilinear trendline (but not going beyond a second-order polynomial) that maximizes R².
Click the icon to view the Helicopter Data.
The residuals plot has a nonlinear shape.
Determine the best curvilinear trendline that maximizes R².
Therefore, this data cannot be modeled with a linear model.
A.
The best trendline is Power with an R² value of The equation is y =
(Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.)
B.
The equation is y=
In (x) +
The best trendline is Logarithmic with an R² value of
(Round the coefficient of the logarithm to one decimal place as needed. Round all other values to three decimal places as needed.)
The best trendline is Exponential with an R² value of. The equation is y =
e
(Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.)
The equation is y = 0x²
D.
The best trendline is Polynomial with an R² value of
(Round to three decimal places as needed.)
+
X +
Transcribed Image Text:The Helicopter Division of Aerospatiale is studying assembly costs at its Marseilles plant. Past data indicates the accompanying data of number of labor hours per helicopter. Reduction in labor hours over time is often called a "learning curve" phenomenon. Using these data, apply simple linear regression and examine the residual plot. What do you conclude? Construct a scatter chart and use the Excel Trendline feature to identify the best type of curvilinear trendline (but not going beyond a second-order polynomial) that maximizes R². Click the icon to view the Helicopter Data. The residuals plot has a nonlinear shape. Determine the best curvilinear trendline that maximizes R². Therefore, this data cannot be modeled with a linear model. A. The best trendline is Power with an R² value of The equation is y = (Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.) B. The equation is y= In (x) + The best trendline is Logarithmic with an R² value of (Round the coefficient of the logarithm to one decimal place as needed. Round all other values to three decimal places as needed.) The best trendline is Exponential with an R² value of. The equation is y = e (Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.) The equation is y = 0x² D. The best trendline is Polynomial with an R² value of (Round to three decimal places as needed.) + X +
Helicopter Number
1
2
3
45678
8
Labor Hours
2000
1475
1239
1140
1072
1027
983
956
Transcribed Image Text:Helicopter Number 1 2 3 45678 8 Labor Hours 2000 1475 1239 1140 1072 1027 983 956
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