5. Use least-squares regression to fit a straight line to data given in table.Show data and line in the same graph. Give equation of the fitted line.03915175691112Xy57V(knots) 679P (kW) 170 270 400 5601286. Fit a straight line, a parabola and a cubic equation to the speed power dataof a ship. Compare your results. (use matlab, excel etc. to obtain graph andrelation)10770191411 12 13 14 151030 1360 1800 2350 31007. Estimate the first derivative the function f(x) = e* cosx, at x=2.0employing step sizes of h= 0.25. Use centered finite divided differenceformulas with 2 and 4 point. Compare your results with true value.Estimate error, &t.

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Answered: 5. Use least-squares regression to fit…

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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What are the interpretations of the Y intercept and the slopes in a multiple regression model?
Two variables are defined, a regression equation is given, and one data point is given. Weight Training = = Weight 92 +11.9(Training) = maximum weight capable of bench pressing (pounds) number of hours spent lifting weights a week The data point is an individual who trains 5 hours a week and can bench 150 pounds. Residual (a) Find the predicted value for the data point and compute the residual. Enter the exact answers. Predicted value = M. lbs lbs
Use least squares regression to fit a line to the table data below.
Lean body mass (weight leaving out fat ) helps predict metabolic rate (how many calories of energy are burned in a hour ) The relationship is roughly a straight line the least squares regression line for predicting metabolic rate (y in calories) from lean body mass (x in kilograms) is y =113.2+26.9x The slope of regression line is ?
The table shows the numbers of new-vehicle sales (in thousands) in the United States for Company A and Company B for 10 years. The equation of the regression line is y = 0.991x + 1,222.81. Complete parts (a) and (b) below. D New-vehicle sales (Company A), x New-vehicle sales (Company B), y 4,149 3,923 3,566 3,400 3,266 3,076 2,868 2,485 1,952 2,066 4,912 4,871 4,827 4,721 4,672 4,474 4,684 3,822 2,956 2,754 (a) Find the coefficient of determination and interpret the result. 12²=0 (Round to three decimal places as needed.) 1
Use the given data to find the scatter plot, equation of the regression line, and prediction. 4) Managers rate employees according to job performance and attitude. The results for several randomly selected employees are given below. Performance 59 63 65 69 58 77 76 | 69 70 64 Attitude 72 67 78 82 75 87 92 83 87 78 What function do you use to see the scatterplot on the calculator. Scatterplot from the calculator:
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
The table shows the lengths and weights of seven muskies captured by the Department of Natural Resources in Catfish Lake in Eagle River, Wisconsin. Use the linear regression feature on a graphing calculator to determine an equation of the line that best fits the data. Round to the hundredths. Musky 1 2 3 4 5 6 7 Length (in.) 26 27 29 33 35 36 38 Weight (lb) 5 8 9 12 14 14 19
Explain how to find se
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). The equation CITY - 3.17 +0.823HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2700 lb, it has an engine displacement of 1.6 L, and its highway fuel consumption is 35 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.). Regression Table I R² Adjusted R2 WT/DISP WT/HWY Predictor (x) Variables P-Value WT/DISP/HWY 0.000 0.942 0.000 0.748 0.000 0.942 0.000…
Do not show any work on this question. For these ordered pairs: (0, 0.1), (1, 1), (2,2.4),(4,3.7), and (5, 5.7): Find (f ,y) and plot it as well as the five given points. Find the equation ofthe least squares regression line. Round off the x coefficient and the constant to the nearest onethousandth. Graph the equation together with the points above. If needed do not use a Pvalue to answer any problem. No credit will be given if a P value is used. Use only criticalvalues.
The sales of a company (in million dollars) for each year shown in the table below (25)X(year) 2005 2006 2007 2008 2009Y (sales) 12 19 39 37 45a. Find the regression line of the data. Draw the inference about the dataset.b. Use the regression line as a model to estimate the sales of the company in 2012.

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5. Use least-squares regression to fit a straight line to data given in table. Show data and line in the same graph. Give equation of the fitted line. 0 3 5 9 15 17 5 6 7 9 11 12 X y 12 8 19 14