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 R. E Click the icon to view the Hellicopter Data. The residuals plot has a nonlinear shape. Therefore, this data cannot modeled with a linear model. Determine the best curvilinear trendline that maximizes R Data table for number of hours per helicopter OA. The best trendline is Power with an R value ofO The equation is y = Ox. (Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.) Helicopter Number Labor Hours 2000 1500 O B. The best trendline is Polynomial with an R? value of. The equation is y = (D.*+ (Round to three decimal places as needed.) 1238 1144 OC. The best trendline is Exponential with an R value of. The equation is y= (De (Round the coefficient to one decimal place as needed. Round all other values to three decimal places as needed.) 1074 1029 980 950 OD. The best trendline is Logarithmic with an R? value of The equation is y= () In (x) (Round the coefficient of the logarithm to one decimal place as needed. Round all other values to three decimal places as needed.) Print Done

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The Helicopter Division of Aerospatiale is analyzing assembly costs of its Marseilles plant. The provided data table showcases the number of labor hours per helicopter. The trend indicates a "learning curve" phenomenon where labor hours decrease over time.

**Data Table for Number of Hours per Helicopter:**
- Helicopter 1: 2000 hours
- Helicopter 2: 1500 hours
- Helicopter 3: 1328 hours
- Helicopter 4: 1114 hours
- Helicopter 5: 1074 hours
- Helicopter 6: 1029 hours
- Helicopter 7: 950 hours
- Helicopter 8: 950 hours

The scatter plot of this data reveals a nonlinear shape, indicating that it may not be adequately modeled with a linear model. Therefore, it is crucial to determine the best curvilinear trendline to maximize the R² value. The options for trendline models include:

A. **Power Trendline:** y = [Coefficient] * x^[Exponent]
   - R² value needed
   - Round coefficient to one decimal place

B. **Polynomial Trendline (2nd Order):** y = [Coefficient1] * x² + [Coefficient2] * x + [Constant]
   - R² value needed
   - Round to three decimal places

C. **Exponential Trendline:** y = [Coefficient] * e^([Rate] * x)
   - R² value needed
   - Round coefficient to one decimal place

D. **Logarithmic Trendline:** y = ([Coefficient]) + [Rate] * ln(x)
   - R² value needed
   - Round the coefficient of logarithm to one decimal place

Select the appropriate model and calculate the coefficients as needed to determine which trendline provides the best fit for the data.
Transcribed Image Text:The Helicopter Division of Aerospatiale is analyzing assembly costs of its Marseilles plant. The provided data table showcases the number of labor hours per helicopter. The trend indicates a "learning curve" phenomenon where labor hours decrease over time. **Data Table for Number of Hours per Helicopter:** - Helicopter 1: 2000 hours - Helicopter 2: 1500 hours - Helicopter 3: 1328 hours - Helicopter 4: 1114 hours - Helicopter 5: 1074 hours - Helicopter 6: 1029 hours - Helicopter 7: 950 hours - Helicopter 8: 950 hours The scatter plot of this data reveals a nonlinear shape, indicating that it may not be adequately modeled with a linear model. Therefore, it is crucial to determine the best curvilinear trendline to maximize the R² value. The options for trendline models include: A. **Power Trendline:** y = [Coefficient] * x^[Exponent] - R² value needed - Round coefficient to one decimal place B. **Polynomial Trendline (2nd Order):** y = [Coefficient1] * x² + [Coefficient2] * x + [Constant] - R² value needed - Round to three decimal places C. **Exponential Trendline:** y = [Coefficient] * e^([Rate] * x) - R² value needed - Round coefficient to one decimal place D. **Logarithmic Trendline:** y = ([Coefficient]) + [Rate] * ln(x) - R² value needed - Round the coefficient of logarithm to one decimal place Select the appropriate model and calculate the coefficients as needed to determine which trendline provides the best fit for the data.
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