2.The data below obtained from the experiment:1.51.82.42.73.23.6f(x) | 0.7555 | 0.6206 | 0.5052 0.4812 0.4498 | 0.4337This data can be fitted to the curve y =where C and D areC + Dxconstants.a. Show that the curve above can be written asD+-.b. Hence, by using the least squares linearization process, determinethe value of C and D. Start your calculation withS= ΣΥ- Y(X)>i=0where S is the total squares error.c. Estimate the value of f (2.5) based on the results obtained in part(b).

To fit a linear equation y=a+bx by the method of least squares, solve the following normal equations…

Answered: The data below obtained from the…

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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You determined there was a positive correlation between the number of study hours a week a student spends studying and their overall grade at the end of the semester. You determined the regression equation: = 1.806x+40, where x is the number of hours a student spends studying each week. What would be the predicted overall test score for a student who studies 14 hours a week? Round your answer to 3 decimal places if needed. 9 Σ Th
# 1 Given the data as shown in the table below X Y 3.8 | 9.8 2.5 8.8 4.1 9.7 0.9 7.5 2.4 9.6 The correct equation of the "Best-Fit"/Regression line associated with the given data above would be Oŷ = 0.6742z + 7.2326 Oy = 0.6742z + 7.2326 Oy = 0.67427 + 7.2326 Oŷ = 0.67427 + 7.2326 Oŷ = 7.23262 + 0.6742 Note: Round all coefficients to four decimal places.
Given below are the ages and heart rates of 50 patents aho participated in a dinical tral for a new ant-hypertersive drug A a Graphicaly, what can you conclude about the relationship between age and heart rate? B. b. Test the hypothesis that p= 0 against the altemative that p0 at the 0.05 level of significance| Heart Rate (y) 100 76 Patient No. Age Patient No. Age Heart Rate 1 48 26 39 72 2 45 27 56 70 3 67 70 28 51 76 4 44 72 29 41 68 57 62 64 30 31 32 49 76 70 79 92 72 70 76 53 58 60 62 33 67 76 47 34 54 68 37 76 86 10 84 35 49 11 52 90 36 54 12 66 90 37 44 81 13 33 80 38 69 72 14 30 68 39 45 80 15 41 72 40 39 80 16 52 74 41 37 72 17 40 30 76 42 45 52 86 18 88 43 72 19 35 84 44 44 76 20 49 70 45 28 39 76 21 62 51 72 68 46 74 68 22 47 49 72 23 41 76 48 80 24 55 70 49 52 80 25 63 68 s0 27 82
You estimated a regression with the following output. Source | SS df MS Number of obs = 246 -------------+---------------------------------- F(1, 244) = 16642.70 Model | 187647307 1 187647307 Prob > F = 0.0000 Residual | 2751112.55 244 11275.0514 R-squared = 0.9856 -------------+---------------------------------- Adj R-squared = 0.9855 Total | 190398419 245 777136.405 Root MSE = 106.18 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 13.79687 .106947 129.01 0.000 13.58621 14.00753 _cons | 17.60822 9.208341 1.91 0.057 -.5297613 35.7462…
A study was conducted to investigate the number of collisions possible between bumper cars at an amusement park. Assuming that no two bumper cars can collide more than once, which of the following curves of best fit most closely describes the data in the table? # of Cars Possible Collisions 1 1 3 3 4 6 5 10 6 15 7 21 O y = 0.5a? – 0.5x O y = -0.5z? + 0.5x O y = 3.5x – 6 O y = 6x – 3.5
2. The data below obtained from the experiment: 1.5 1.8 2.4 2.7 3.2 3.6 f(a) 0.7555 0.6206 0.5052 0.4812 0.4498 0.4337 This data can be fitted to the curve y where C and D are C + Dr constants. a. Show that the curve above can be written as 1 = D+ b. Hence, by using the least squares linearization process, determine the value of C and D. Start your calculation with S =E( -Y(X.)) where S is the total squares error. c. Estimate the value of f(2.5) based on the results obtained in part (b).
The image shows the estimation of the model with quarterly car sales in the U.S. from 1975 to 1990. Based on the parameter estimates, what is the predicted effect of a 10% increase in price on the number of cars sold? What would be the effect of that price increase on the value of car sales?
You estimated a regression with the following output. Source | SS df MS Number of obs = 494 -------------+---------------------------------- F(1, 492) = 38566.69 Model | 803403712 1 803403712 Prob > F = 0.0000 Residual | 10249120.6 492 20831.546 R-squared = 0.9874 -------------+---------------------------------- Adj R-squared = 0.9874 Total | 813652832 493 1650411.42 Root MSE = 144.33 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 23.00296 .1171325 196.38 0.000 22.77281 23.2331 _cons | 34.71944 13.12788 2.64 0.008 8.925808 60.51307…
6
HW22: Please help me answer all sections
Calculate the standard error of the estimate of Y from the regression of Y on X for the following data: n= 10 E y- 90, Σw- 120. Ex - 200 where x-X-X, y-Y Y.
#11). Please show how to solve and circle the answers.

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