Experiment (10):Graphic Plots (Linear Regression and Curve fitting)1.The following data is given:2581315y781011121415Use linear least-squares regression to determine the coefficients m and b in thefunction y = mx +b that best fits the data.Make a plot that shows the function and the data points.%3D2. Find the straight line, quadratic and cubic equations that fit to the following data1.21.51.82.63.14.34.95.3y4.55.15.86.77.07.37.67.45.76.47.17.68.69.29.8y7.26.96.65.14.53.42.73.Find the least-squares parabola for the data shown in Table below.2345y34344.The boiling temperature of water T, at various altitudes h is given in the fol-lowing table. Determine a linear equation in the form T = mh +b that bestfits the data. Use the equation for calculating the boiling temperature at5,000 m. Make a plot of the points and the equation.h (m)60015002300300061007900T( C)10098.895.192.29081.275.623

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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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Report the equation of the regression line and interpret it in the context of the problem
I need the right answer to d, e, and f ASAP, please.
Consider the following. x 1 2 3 4 y 4 6 9 13 (a) Find an equation of the least-squares line for the data. (Give each answer correct to 3 decimal places.)y = x +(b) Draw a scatter diagram for the data and graph the least-squares line.
Fill in the blanks. a. Multicollinearity is considered to be severe if the VIF for one or more predictor variables is ______. or greater. b. If the coefficient of multiple determination for the regression of the predictor variable x11 on all the other predictor variables in a regression equation is 0.6, then the VIF for x1 is ______. c. The effect of multicollinearity in a polynomial regression analysis can be reduced by ______. the predictor variable.
Fit the following points, (17,8), (18,10), (23,6), (24,5), (27,5), and (32,2). Predict the f(28). A. second-order polynomial Regression B. Simple Linear Regression
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 following data is given: x -2 -1 0 1 2 y 1.5 3.2 4.5 3.4 2 Determine the coefficients a and b in the function y=a/(x2+b) that best fit the data. (Write the function in a linear form, and use linear least-squares regression to determine the value of the coefficients.) Once the coefficients are determined make a plot that shows the function and the data points.
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In the graph shown below consider the horizontal line to represent the average y value for the data set and the slant line to represent the linear regression equation. use only the graph of the data to do the following:

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1. The following data is given: 2 5 8 9 13 15 y 7 8 10 11 12 14 15 Use linear least-squares regression to determine the coefficients m and b in the function y Make a plot that shows the function and the data points. mx +b that best fits the data. %3D