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 hoursover 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 usethe 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 best trendline is Logarithmic with an R² value ofThe 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.)C.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=([ X +X +O D. The best trendline is Polynomial with an R² value of(Round to three decimal places as needed.)Helicopter Number12345678Labor Hours200014751239114010721027983956

Answered: The Helicopter Division of Aerospatiale…

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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Consider the following image of the data. Is the linear trend line a reasonable fit to the data points? Is it linear? +H R²=0.9467 No. The dashed line is well outside many of the error bars. No. The R^2 value is less than 1. Yes. The R^2 value is approximately 1. Yes. The data points have error bars and the line goes through them.
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a) Give a practical interpretation of the y-intercept of the regression line. b) What is the best-predicted value for the median hourly wage gain for the fifteenth year of schooling? c) The actual wage gain for the fifteenth year of schooling was 14%. How close was the predicted wage gain present to the actual value, i.e. what is the residual?
A rural state wants to encourage high school graduates to continue their education and attend college. The state collected information on a random sample of high school seniors from across the state 7 years ago and is now observing how many years of education they completed. They believe students decide to achieve more education when they are more capable, have easier access to college education, and the opportunity cost of attending are lower. To explore the factors that affect the years of education completed they have used multiple regression to estimate the years of completed education as a function of: Unemployment rate - the unemployment rate in the county (3.9 – 16.8) County Hr Wage - average starting hourly manufacturing wage in the county Test - student score on college admission test (0 to 100 scale) Dist to college - Distance to near college (measured in 100’s of miles) Tuition - Tuition charged at nearest state university (measured in $1000s)…
please answer the last question on the screenshot. "what is the interpretation of the intercept?"
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Forensic scientists can learn about events at a crime scene by collecting data. Ex: Properties of glass shards at a crime scene such as chemical composition can indicate what type of glass was broken at the scene. Possible types include building glass (building windows or doors), vehicle glass (car windows or doors), or household glass (lightbulbs, baking dishes). The fitted logistic regression model for predicting whether a glass shard is building glass based on sodium is: = 20.02+(-1.42) (sodium) 1+e20.02+(-1.42) (sodium) Calculate the log-odds that a glass shard with sodium = 13.08 is building glass. Ex: 1.23 C Calculate the probability that a glass shard with sodium = 13.08 is building glass.
We wants to assess the relationship between overall GPA (4.0 scale) and amount of time spent weekly on school work of 50 college students. Simple linear regression model is used and some results for the model is given: a) Is it plausible that the true intercept for the model is 2.0? Why or why not? (hint: using interval for intercept) b) If 2.0 is the real intercept for the model, does it make contextual sense? Explain why it is or why it is not. c) Using the model, calculate the number of hours needed to achieve a GPA of 4.0. Then explain why this estimate number of hours is biased. (hint: think about Y).
The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y=41.57 – 0.49.x. This line is shown in the scatter plot in Figure 1. Used selling price, Mileage, x (in thousands) (in thousands of dollars) 25.9 26.1 28.1 26.2 40- 21.1 31.4 24.0 27.5 35 27.2 30.9 38.7 21.4 30. 34.6 25.5 37.2 23.5 15.6 34.0 25- 23.8 28.0 20.9 30.9 20. 23.1 32.7 28.0 30.3 40 29.2 28.1 Figure 1 24.0 29.6 23.0 31.5 Send data to Excel
Connie Harris, who is in charge of office supplies at First Capital Mortgage Corp., would like to predict the quantity of paper used in the office photocopying machines per month. She believes that the number of loans originated in a month influence the volume of photocopying performed. She has compiled the following recent monthly data: Develop an estimated regression equation for this data set. Place output in cell G1 At .05 level of significance, is the equation significant? Why? Does the equation seem to have a good “fit”? Why? Forecast the amount of paper used in a month when 42 loan originations are expected. Loans Originated in Month Paper Used (000's) 45 22 25 13 50 24 60 25 40 21 25 16 35 18 40 25
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 Helicopter Data. The residuals plot has a nonlinear shape. Therefore, this data cannot be modeled with a linear model. Determine the best curvilinear trendline that maximizes R2. Data table for number of hours per helicopter A. The best trendline is Logarithmic with an R value of The equation is y = () In (x) Helicopter Number Labor Hours 2000 (Round the coefficient of the logarithm to one decimal place as needed. Round all other…

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