Q2.) In an experiment, the following reading were recorded:Xi124Yi7111727bx1- Derive the normalized equations for this reading if y= ae" by LSA method.2- Determine the best fitting of reading (theoretical values of y).Note: In solution of this question approximate to three decimal digits only after thepoint.

Answered: Image /qna-images/answer/0508db8a-3a60-4e6f-8255-e50921b2af45.jpg

Answered: Q2.) In an experiment, the following…

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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Heights (cm) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 130 to 190 cm and weights of 41 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x = 167.53 cm, y 81.32 kg, r 0.259, P-value 0.009, and y = 106+ 1.09x. Find the best predicted value of y (weight) given an adult male who is 181 cm tall. Use a 0.10 significance lev. The best predicted value of y for an adult male who is 181 cm tall is kg. (Round to two decimal places as needed.)
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Wrinkle recovery angle and tensile strength are the two most important characteristics for evaluating the performance of crosslinked cotton fabric. An increase in the degree of crosslinking, as determined by ester carboxyl band absorbance, improves the wrinkle resistance of the fabric (at the expense of reducing mechanical strength). The accompanying data on x = absorbance and y = wrinkle resistance angle was read from a graph in the paper "Predicting the Performance of Durable Press Finished Cotton Fabric with Infrared Spectroscopy".† x 0.115 0.126 0.183 0.246 0.282 0.344 0.355 0.452 0.491 0.554 0.651 y 334 342 355 363 365 372 381 392 400 412 420 Here is regression output from Minitab: Predictor Constant absorb S = 3.60498 Coef 321.878 156.711 SOURCE Regression Residual Error Total SE Coef 2.483 6.464 R-Sq = 98.5% DF 1 9 10 SS 7639.0 117.0 7756.0 T 129.64 24.24 0.000 0.000 R-Sq (adj) = 98.3% MS 7639.0 13.0 F P 587.81 (a) Does the simple linear regression model appear to be…
10.2.19-T Question Help ▼ Find the regression equation, letting the first variable be the predictor (x) variable. Using the listed lemon/crash data, where lemon imports are in metric tons and the fatality rates are per 100,000 people, find the best predicted crash fatality rate for a year in which there are 475 metric tons of lemon imports. Is the prediction worthwhile? Lemon Imports Crash Fatality Rate 227 262 363 499 15.4 518 16 15.7 15.4 15 Find the equation of the regression line. (Round the constant three decimal places as needed. Round the coefficient to six decimal places as needed.)
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A professor decides to investigate the relationship between midterm exam score and final exam score (in points ) of students in her introductory statistics class after taking a sample of students she finds r2=0.74 and the regression equation is final exam score=40+0.95 a correct interpretation of r2 =0.74 would be ? A) 74% of the predicted final exam scores will be correct B) 74 % of the variation in midterm exam score can be explained by the straight line relationship with final exam score c) 74%of students will have higher final exam scores than midterm exam scores D)74% of the variation in final exam score can be explained by the straight line relationship with midterm exam score
The following estimated regression equation based on 10 observations was presented ŷ = 25.1270 + 0.5309x1 + 0.4920x2 Here, SST = 6,738.125, SSR = 6,221.375, sb1 = 0.0818, and sb2 = 0.0563. Compute MSR and MSE. (Round your answers to three decimal places.) Compute F and perform the appropriate F test. Use α = 0.05. State the null and alternative hypotheses. a. H0: β1 ≠ 0 and β2 ≠ 0 Ha: One or more of the parameters is equal to zero. b. H0: β1 = β2 = 0 Ha: One or more of the parameters is not equal to zero. c. H0: β1 > β2 Ha: β1 ≤ β2 H0: β1 < β2 Ha: β1 ≥ β2 d. H0: β1 ≠ 0 and β2 = 0 Ha: β1 = 0 and β2 ≠ 0 2. Find the value of the test statistic. (Round your answer to two decimal places.) 3. Find the p-value. (Round your answer to three decimal places.) p-value = 4. State your conclusion. a. Reject H0. There is sufficient evidence to conclude that the overall model is significant. b. Do not reject H0. There…
A medical social worker is interested in examining the relationship between patients who receive social support by family members (yes/no) and the patients’ outcome (failure/success). She collected data over a period of a month (N=100). A chi square analysis revealed the following: χ2= 5.65, df = ?, p = .01. 1. Was a chi-square analysis appropriate to analyze these variables? How do you know?
From 8 observations the following results were obtainedΣXΣ =544, ΣY =552, ΣXY = 37560, ΣX^2 =37028, ΣY^2 =38132. Find i) the correlation co- efficient, ii) and, state the relationship, iii) the regression line equation = Y=mX+bY=mX+b
Solve the following problems based on the step-by-step procedure of the Non-Parametric Tests. Show complete solutions and answer. When nicotine is absorbed by the body, cotinine is produced. A measurement of cotinine in the body is therefore a good indicator of how much a person smokes. Listed below are the reported numbers of cigarettes smoked per day and the measured amounts of nicotine (in ng/ml). Is there a significant correlation at alpha = 0.05? x (cigarettes/day) 60 10 4 15 10 1 20 8 7 10 10 20 y (cotinine) 179 283 75.6 174 209 9.51 350 1.85 43.4 25.1 408 344
8. In an investigation to empirically determine the value of 1, a student measures the circumference and diameter of several circles of varying size and uses Excel to make a linear plot of circumference versus diameter (both in units of meters). A linear regression fit yields the result of: y = 3.1527x - 0.0502, with R2= 0.9967 for the 5 data points plotted. How should this student report the final result? Does the empirical ratio of C/D agree with the accepted value of n?
Heights (cm) and weights (kg) are measured for 100 randomly selected adult males, and range from heights of 139 to 188 cm and weights of 38 to 150 kg. Let the predictor variable x be the first variable given. The 100 paired measurements yield x = 167.62 cm, y = 81.37 kg, r 0.113, P-value = 0.263, and y = - 105+1.01x. Find the best predicted value of y (weight) given an adult male who is 142 cm tall. Use a 0.05 significance level. %3D The best predicted value of y for an adult male who is 142 cm tall is kg. (Round to two decimal places as needed.)

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