| Let f (x) =10 Sx S0 if (a) x 2 2. (b) 1.5

Answered: 1 Let f (x) =, 0 sx5 0 if (a) x 2 2.…

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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Let f(x) = g(x) / h(x) and consider the following values: g(14) = 4, h(14) = 4, g'(14) = 5, f'(14) = 1. Determine the value of h'(14). * %3D
Forest Fires and Acres Burned Numbers (in thousands) of forest fires over the year and the number (in hundred thousands) of acres burned for 6 recent years are shown. The regression line equation is =y′+−45.79501.34704x. The correlation coefficient is =r0.858. Compute the standard error of the estimate rounded to at least two decimal places, if appropriate. Assume =α0.05 The standard error of the estimate should or should not be calculated. S est= Number of fires x 57 45 72 69 58 47 Number of acres burned y 30 15 62 42 19 26
Question 2 Evaluate / a V3x + 2 dx. x3 dx. x + 3 Question 3 Evaluate e3x Question 4 Evaluate dx. 4+ e6x
Correlations Pearson Correlation 444 Sig. (2-tailed) .199 10 10 y Pearson Correlation 444 1 Sig. (2-tailed) .199 N 10 10 Descriptive Statistics Mean Std. Deviation 10 83.40 9.009 10 46.70 18.185 Valid N (listwise) 10
correct the x pls ty!
To examine whether there is any association between the performances on the two tests, T1 and T2 (test T1 was conducted before T2), and whether T2 scores could be predicted in terms of T1 scores. A random sample of 150 students in Statistics major was selected. Let the scores be denoted S1 and S2 for tests T1 and T2, respectively. The tests had different maximum points. The following outputs using R codes were obtained. fit[t]) (Intercept) 0.10152 2.51003 0.04 0.968 S1 0.26723 0.01921 13.91 <2e-16 *** Signif. codes: O ***' 0.001 **' 0.01 (*' 0.05 '.' 0.1 '? 1 Residual standard error: 4.472 on 148 degrees of freedom Multiple R-squared: F-statistic: 193.6 on 1 and 148 DF, 0.5667, Adjusted R-squared: 0.5638 p-value: < 2.2e-16 plot (fit$fitted,fit$residuals, xlab="fitted values", ylab="residuals") qqnorm (fit$residuals) Normal Q-Q Plot oo 00 00 00 O Go a god 00 00 0. of 25 30 35 40 45 -2 2 fitted values Theoretical Quantiles esiduals 15 Score: Sample Quantiles 091

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1 Let f (x) =, 0 sx5 0 if (a) x 2 2. (b) 1.5 sx S2 be the critical regions for testing H: 0 = 1 ~ H, : 0 = 2 then find %3D %3D 0. 1. two types of errors and power of the test.