The image contains a question and two scatter plots used for analyzing the relationship between line speed and the number of defective parts.### Scatter Plots DescriptionThe scatter plots both display data points representing line speed in feet per minute on the x-axis and the number of defective parts on the y-axis, ranging from -10 to 25.1. **Scatter Plot (a):** - The x-axis is labeled "Line Speed (feet per minute)," ranging from -10 to 60. - The y-axis is labeled "Defective Parts," ranging from -10 to 25. - The plot displays several data points suggesting an upward trend as line speed increases.2. **Scatter Plot (D):** - Similar to the first plot, the x-axis is "Line Speed (feet per minute)" and the y-axis is "Number of Defective Parts." - This plot shows the same set of data points, emphasizing the analysis requirements.### Question and Instructionsb. **Question:** What does the scatter diagram developed in part (a) indicate about the relationship between the two variables? - A dropdown menu is present to select an answer regarding the relationship.c. **Instruction:** Use the least squares method to develop the estimated regression equation (to one decimal). - Regression equation template: \(\hat{y} = \_\_\_ + \_\_\_x\), where users must fill in the blanks.d. **Instruction:** Predict the number of defective parts found for a line speed of 25 feet per minute.The exercise aims to assist students in understanding scatter plots, the concept of correlation, and applying regression analysis to predict outcomes based on the given data points. Brawdy Plastics, Inc., produces plastic seat belt retainers for General Motors at their plant in Buffalo, New York. After final assembly and painting, the parts are placed on a conveyor belt that moves the parts past a final inspection station. How fast the parts move past the final inspection station depends upon the line speed of the conveyor belt (feet per minute). Although faster line speeds are desirable, management is concerned that increasing the line speed too much may not provide enough time for inspectors to identify which parts are actually defective. To test this theory, Brawdy Plastics conducted an experiment in which the same batch of parts, with a known number of defective parts, was inspected using a variety of line speeds. The following data were collected:Excel file: [data14-05.xlsx](#)| Line Speed | Number of Defective Parts Found ||------------|----------------------------------|| 20 | 23 || 20 | 21 || 30 | 19 || 30 | 16 || 40 | 15 || 40 | 17 || 50 | 14 || 50 | 11 |

(b) Determine the type of relationship exists between the two variables. From the scatter plot…

Answered: Select your answer - v b. What does the…

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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6) Suppose you have the following data on the price of orange and the quantity sold: Price per Pound (in Quantity Sold (in Dollars) Pounds) 0.50 0.75 1.00 1.25 1.50 10 7 699 5 2 Assume that the quantity sold (Y) is a linear function of the price (X), i.e. Y₁ =B₁ + B₂X₁ + ε₁ Estimate the population regression coefficients. (Do not use Computer)
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please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly
When running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?
You estimated a regression with the following output. Source | SS df MS Number of obs = 335 -------------+---------------------------------- F(1, 333) = 69555.83 Model | 211169628 1 211169628 Prob > F = 0.0000 Residual | 1010979.01 333 3035.97301 R-squared = 0.9952 -------------+---------------------------------- Adj R-squared = 0.9952 Total | 212180607 334 635271.28 Root MSE = 55.1 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 44.15183 .1674102 263.73 0.000 43.82251 44.48114 _cons | 31.63715 16.49849 1.92 0.056 -.8172452 64.09155…
You estimated a regression with the following output. Source | SS df MS Number of obs = 268 -------------+---------------------------------- F(1, 266) = 23.48 Model | 668419.175 1 668419.175 Prob > F = 0.0000 Residual | 7572666.51 266 28468.6711 R-squared = 0.0811 -------------+---------------------------------- Adj R-squared = 0.0777 Total | 8241085.68 267 30865.4895 Root MSE = 168.73 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 1.014128 .2092916 4.85 0.000 .6020489 1.426207 _cons | 9.173163 21.13463 0.43 0.665 -32.43929 50.78561…
You estimated a regression with the following output. Source | SS df MS Number of obs = 223 -------------+---------------------------------- F(1, 221) = 17592.99 Model | 182392130 1 182392130 Prob > F = 0.0000 Residual | 2291176.96 221 10367.3166 R-squared = 0.9876 -------------+---------------------------------- Adj R-squared = 0.9875 Total | 184683307 222 831906.786 Root MSE = 101.82 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 11.97037 .0902481 132.64 0.000 11.79252 12.14823 _cons | 74.40159 10.96696 6.78 0.000 52.78839 96.01479…
1. Consider a linear regression model y = XB + € with E(e) = 0. The bias of the ridge estimator of 3 obtained by minimizing Q(B) = (y — Xß)¹ (y — Xß) + r(BTB), for some r > 0, is ——(X²X + r1)-¹8 1 (X¹X +rI)-¹3 r -r(XTX+rI) ¹8 r(X¹X+r1) ¹3
You estimated a regression with the following output. Source | SS df MS Number of obs = 472-------------+---------------------------------- F(1, 470) > 99999.00 Model | 2.2728e+09 1 2.2728e+09 Prob > F = 0.0000 Residual | 4246681.85 470 9035.4933 R-squared = 0.9981-------------+---------------------------------- Adj R-squared = 0.9981 Total | 2.2771e+09 471 4834590.83 Root MSE = 95.055------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval]-------------+---------------------------------------------------------------- X | 29.84419 .0595046 501.54 0.000 29.72726 29.96112 _cons | 88.27799 7.592427 11.63 0.000 73.35868 103.1973------------------------------------------------------------------------------…
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