C.Enter a number.0.40.50.3OutcomeP(A n C) =%3DP(A n D) =%3DP(B n C) =%3DP(B n D) =%3DB.

Answered: Outcome P(A n C) = %3D P(A n D) = %3D…

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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Data Set: {(−3,4), (−2, 3), (−1, 3), (0, 7), (1, 5), (2, 6), (3, 1)} 1. The regression line is: y = -2 FS 0.3 Your Turn: Data Set 2 2. Based on the regression line, we would expect the value of response variable to be 0.005 4. If x= -3.5, the y = 1 when the explanatory variable is 0. 3. For each increase of 1 in of the explanatory variable, we can expect a(n) decrease of 1 in the response variable. ovemple of F6 evtrannlation X O Search MALERINION 8 *. This is an g prt sc home
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(a) The correlation coefficient: r = (b) The least squares line: ỹ = %3D
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When a sample survey asks people about use of illegal drugs, some people who use drugs will lie and say they do not use drugs because they fear that the information will be given to the police or employers. O This is a nonsampling error that causes bias. O This is a sampling error that increases variability. O This is a nonsampling error that increases variability. O This is a sampling error that causes bias.
Q/Fit (y = ax² + (b/x)) to the following data: (x:1, 2, 3, 4), (y: -1.51, 0.99, 3.88, 7.66). *
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If Cov(X,Y)=-99, o? = 121 and o = 81 then, coefficient of correlation r(X, Y) will be %3D Select one: O +1.000 O - 1.000 O +0.0101 0.0101 PREVIOUS PAGE re to search 立

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A researcher conducts a multiple regression with Y as the dependent variable and X1, X2, X3 and X4 as explanatory variables. Using the regression output below, fully describe this model and discuss important parts of the output. What is the predicted value of Y if X1 = 3, X2 = 15, X3 = 7 and X4 = 0.003? %3D SUMMARY OUTPUT Regression Staistics Muliple R R Square Adjusted R Square Standard Emor Observations 0.7236 0.5236 0.5159 5.3928 252 ANOVA Significance F 1. 10662E-38 SS MS Regression Residual 1973 9392 29.0820 67.8749 7895.7567 7183.2599 4 247 Total 251 15079.0166 Upper 95% 33.4049 Coefficients Standard Eror t Stat Pvalue 2.2273 0.026830873 Lower 95% 7.9594 2.0508 Intercept X1 17.7278 1.5583 0.2750 5.6662 4.05265E-08 1.0166 2.0999 X2 1.8376 0.1997 9.1999 1.4442 -74708 -3721 4324 1.55861E-17 2.2310 X3 55100 -5.5348 7.94036E-08 -3.5492 X4 -3.1079 1887 8435 -0.0016 0.998687788 3715 2166
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The next four questions (29 to 32) refer to the following: To most Canadians, earthquakes are viewed as rare occurrences, but they are actually quite common. In just one month in 2001, Natural Resources Canada recorded 215 earthquakes that affected Canada from B.C. to Nunavut to Newfoundland. We would like to determine whether there is a linear relationship between the location of an earth- quake X (measured in degrees latitude north of the equator) and the magnitude of the earthquake Y (measured on the Richter scale). The explanatory and response variables are measured for a sample of 13 earthquakes. The equation of the least squares regression line is ŷ = -3.05+ 0.10x. The ANOVA table is showm below: ге Source of Variation df Sum of Squares Mean Square F Regression Error Total 1.40 25.10

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Outcome P(A n C) = %3D P(A n D) = %3D P(B n C) %3D P(B n D) = %3D