Representative data read from a plot on runoff sediment concentration for plots with varying amounts of grazing damage,measured by the percentage of bare ground in the plot, are given for steeply sloped plots.Steeply Sloped PlotsBare ground (%) 5 5 10 15 20 25 20 30 35 40 35Concentration 90 225 270 540 450 450 810 720 990 1080 900(a) Using the data for steeply sloped plots, find the equation of the least-squares line for predicting y runoff sedimentconcentration using x= percentage of bare ground. (Give the answer to two decimal places.)ŷ 54.05 - 24.71x=eBookX(b) What would you predict runoff sediment concentration to be for a steeply sloped plot with 19% bare ground?(Round your answer to the nearest whole number.)524Additional Materials

The given data is as follows: Bare ground…

Answered: Representative data read from a plot on…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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In baseball, two statistics, the ERA (Earned Run Average) and the WHIP (Walks and Hits per Inning Pitched), are used to measure the quality of pitchers. For both measures, smaller values indicate higher quality. The following computer output gives the results from predicting ERA by using WHIP in a least-squares regression for the 2017 baseball season. Variable DF Estimate SE T Intercept 1 -5.0 0.26 - 19.3 WHIP 1 6.8 0.14 47.4 Which of the following statements is the best interpretation of the value 6.8 shown in the output? ERA is predicted to increase by 6.8 units for each 1 unit increase of WHIP. WHIP is predicted to increase by 6.8 units for each 1 unit increase of ERA. For a pitcher with 0 units of WHIP, the ERA is predicted to be approximately 6.8 units. For a pitcher with 0 units of ERA, the WHIP is predicted to be approximately 6.8 units. Approximately 6.8% of the variability in ERA is due to its linear relationship with WHIP.
Square Footage, X Asking Price ($000s), y 1148 154 1096 159.9 1142 169 1288 169.9 1322 170 1466 179.9 1344 180 1544 189 1494 189.9 Determine the least squares regression line.
An important application of regression analysis is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, one can estimate the cost associated with a particular manufacturing volume. Consider the following sample of monthly production volumes and total costs data for a manufacturing operation for the year 2018. Month (2018) Volume of Production (Units) Total Costs ($) Jan 500 6000 Feb 350 4000 Mar 450 5000 Apr 550 5400 May 600 5900 Jun 400 4000 Jul 400 4200 Aug 350 3900 Sept 400 4300 Oct 600 6000 Nov 700 6400 Dec 750 7000 1) Which of the following is NOT necessarily true about the interpretation of the value of b in the simple linear regression equation y = a + bx for this problem? 2) Which of the following statements are true about the dependent (or response) and independent (or predictor) variables for the simple linear model in…
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 3,800 450 4,800 550 5,200 600 5,700 700 6,200 750 6,800 a. Compute b1 and bo (to 1 decimal). b1 bo Complete the estimated regression equation (to 1 decimal). + b. What is the variable cost per unit produced (to 1 decimal)? c. Compute the coefficient of determination (to 3 decimals). Note: report r between 0 and 1. What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? d. The company's production schedule shows 500 units must be produced next month. What is the estimated total…
Report the equation of the regression line and interpret it in the context of the problem
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes O and total cost data for a manufacturing operation. O Production Volume (units) Total Cost ($) 400 4100 450 4600 500 5600 600 6400 700 7400 750 8400 N The data on the production volume and total cost y for particular manufacturing operation were used to develop the estimated regression equation ŷ = -524.58+11.66z. a. The company's production schedule shows that 550 units must be produced next month. Predict the total cost for next month. ŷ* = (to 2 decimals) b. Develop a 98% prediction interval for the total cost for next month. X (to 2 decimals) 8 t-value (to 3 decimals) 8pred (to 2 decimals) Prediction Interval for…
Complete parts (a) through (h) for the data below. 30 40 50 60 70 y 90 87 82 74 Question Help v 62 (c) Graph the line found in part (b) on the scatter diagram. Choose the correct graph below. O A. OB. Ay 80- OC. Ay 80- OD. Av 20- 20 20 80 100 (d) By hand, determine the least-squares regression line. /Dound to throo donimal lncon an nondod Click to select your answer(s). M acer 9 esc # 2$ 6. 4 3 e W k h
The residual plot shows the residuals for the least- squares line relating price of a used car (y) to the number of miles driven (x). Residuals Residual Plot for Miles Driven vs Price 10,000 5,000 -5,000 50,000 100,000 150,000 200,000 Number of Miles Driven Based on the residual plot, is the linear model appropriate for the relationship between miles and price? No, because there are clear outliers in the residual plot. No, because there is a clear curved pattern in the residual plot. No, because there does not appear to be a pattern to the residuals. Yes, because there is a clear curved pattern in the residual plot. Yes, because there does not appear to be a pattern to the residuals.
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,300 450 5,300 550 5,700 600 6,200 700 6,700 750 7,300 Compute b1 and b0 (to 1 decimal). Complete the estimated regression equation (to 1 decimal) y^ = ____ + ____x What is the variable cost per unit produced (to 1 decimal)?
An important application of regression analysis in accounting is in the estimation of cost. By collecting data on volume and cost and using the least squares method to develop an estimated regression equation relating volume and cost, an accountant can estimate the cost associated with a particular manufacturing volume. Consider the following sample of production volumes and total cost data for a manufacturing operation. Production Volume (units) Total Cost ($) 400 4,700 450 5,700 550 6,100 600 6,600 700 7,100 750 7,700 A) Compute b1 and b0 (to 1 decimal)b1 b0 Complete the estimated regression equation (to 1 decimal). = + x B) What is the variable cost per unit produced (to 1 decimal)?$ C) Compute the coefficient of determination (to 3 decimals). Note: report r2 between 0 and 1.r2 = What percentage of the variation in total cost can be explained by the production volume (to 1 decimal)? % D) The company's production schedule shows 500 units must be produced next…
Representative data read from a plot on runoff sediment concentration for plots with varying amounts of grazing damage, measured by the percentage of bare ground in the plot, are given for steeply sloped plots. Steeply Sloped Plots Bare ground (%) 5 5 10 15 20 25 20 30 35 40 35 Concentration 90 225 270 540 450 450 810 720 990 1080 900 (a) Using the data for steeply sloped plots, find the equation of the least-squares line for predicting y runoff sediment concentration using x = percentage of bare ground. (Give the answer to two decimal places.) ŷ = (b) What would you predict runoff sediment concentration to be for a steeply sloped plot with 19% bare ground? (Round your answer to the nearest whole number.)
Representative data read from a plot on runoff sediment concentration for plots with varying amounts of grazing damage, measured by the percentage of bare ground in the plot, are given for steeply sloped plots. Steeply Sloped Plots Bare ground (%) 7 7 14 21 28 35 28 42 49 56 49 Concentration 80 200 240 480 400 400 720 640 880 960 800 (a) Using the data for steeply sloped plots, find the equation of the least-squares line for predicting y runoff sediment concentration using x = percentage of bare ground. (Give the answer to two decimal places.) y hat = (b) What would you predict runoff sediment concentration to be for a steeply sloped plot with 18% bare ground? (Round your answer to the nearest whole number.)

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