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H a study of grcund mctior caused bj earthquakes; the peak relocity (n m :) and peak aCce leraticn (ir * ?, were recofded for five eatkquakes The result? are presen...

Question

H a study of grcund mctior caused bj earthquakes; the peak relocity (n m :) and peak aCce leraticn (ir * ?, were recofded for five eatkquakes The result? are presented in the fcllowing tableVelocity 1.4 1.60 0.93 1.30 292 Acceleration 17.64 8.04 8.04 6.37 3.2Comlgute the correlation Coefficient beziveen Feak Felocity and peat acceleraticnConst c: scaterplot for tbese data.Is the correlation ccefficient ar apprcpriate sunmary for these data? Explain wky Why not.Someone suggests converting the uni

h a study of grcund mctior caused bj earthquakes; the peak relocity (n m :) and peak aCce leraticn (ir * ?, were recofded for five eatkquakes The result? are presented in the fcllowing table Velocity 1.4 1.60 0.93 1.30 292 Acceleration 17.64 8.04 8.04 6.37 3.2 Comlgute the correlation Coefficient beziveen Feak Felocity and peat acceleraticn Const c: scaterplot for tbese data. Is the correlation ccefficient ar apprcpriate sunmary for these data? Explain wky Why not. Someone suggests converting the unit: from meters to centimeter: and from seconds to mnutes What effec: would tiabare Or tnle correlation?



Answers

Data Set 16 in Appendix B lists earthquake depths, and the summary statistics are n = 50 , x
? = 9.81 km, and s = 5.01 km. Use a 0.01 significance level to test the claim of a seismologist that these earthquakes are from a population with a mean depth equal to 10 km. See the accompanying Minitab display that results from this hypothesis test. (FIGURE CAN'T COPY)

Way have given here the data do you take given here? And this is 1991 1990 toe 1993 1994 1995 1996 1997 1998 1999 and 2000. And now the get data Responding. 11 23 16 15 25 22 2016 and 23 18. Now we have deplored a whisker instagram and signed the median. So the number of village 12345 6789 10. So the median is here. Take every of this. So millionaires 25 plus 22 days ahead of it too. So this will with 47 divided two minutes, 23.5 with the media. And now I mean it. I mean, it's a mission off all terms, my son off and and then divide with so submission off. This is so now we have mean 18.9 and we don't have a strong to the point side. No, we have the blood with school starting from here. So for this we have to range in the increasing orders off the minimum. It's 11 then 15 then 16 16 2 times. So, no, this is the and we have to find the median. So the median is 18. Let's 20 to add to that is the 19. And now this is the median. So here the 21 will be he had. This is the term 15.5 and the Q tree will be 22.5. And no, this is the law. 11 and high as 25. Now we have to plot it. So here, this is the 1,000,019. This is the Cuban, and this is cute tree. And now this is the high here. Knowledge. 11. So this is the law and now joined this. So here. So now, by joining, we get here the whiskers, Lord. Now compare it and get all your solutions.

We are given the data points X. Y. Listen to the top of this document. And we want to use these data points to answer the following three questions. We start with heart A. On the left, draw a scatter plot of these data points X. Y. Which I've already provided for you below and estimate the best fit line of that data. The best fit line is a line that best represents the linear relationship between X explains very variable and why response variable. We can estimate this best foot line. As for here. Next we want to describe the correlation between the explanatory X and response. Why we see that there is a general increase in Y with X. But it isn't terribly strong so we can write this as a moderate but positive correlation. Finally, part C. We want to compute the following sums and the correlation coefficient R. Then we want to answer as X increases. Should Why tend to increase or decrease? The thumbs are computed simply by following their formulas and are listed here. The correlation coefficient R. Is given by the formula here, which uses the sums above and the sample size and plug in. We obtain our equals 0.5111 which we are able to conclude from that as X increases, we should expect why to tend to increase.

Looking at a data set giving those values for altitude and the speed of sound. We want to create a scatter plot which I've done in Excel 1st. And the scatter plot looks something similar to this. It appears to be very linear for these first few points. And then we plateau at the end here with the same values. So we want to now know what this correlation coefficient is and we can calculate that in Excel if you're doing it in the same way I am with the function where equals C O R R E L. And then in parentheses we have our X values and are Y values. And what this yields for us is a correlation coefficient equal to -09722. Because that's very close to negative one. We can say that we have a very strong negative linear correlation within the status set. So interpreting this, I think it helps to draw trend line in here and it looks something similar to that if we take into account these plateau at the end, so interpreting that what we see is that as these X values are increasing, so as altitude increases the speed of sound is tending to decrease. So interpreting this, we say exactly what I just did. We see that as altitude increases there appears to be a tendency for the speed of sound to decrease. Now I should stress here that this does not imply any bit of causality. Correlation is just an explanation of a relationship and the direction. So the strength and direction of relationship between two sets of variables. So we're seeing just this tendency for the speed of sound to decrease as altitude increases.

We are given the data points X. Y. Listen to the top of this document. And we want to use these data points to answer the following three questions. We start with heart A. On the left, draw a scatter plot of these data points X. Y. Which I've already provided for you below and estimate the best fit line of that data. The best fit line is a line that best represents the linear relationship between X explains very variable and why response variable. We can estimate this best foot line. As for here. Next we want to describe the correlation between the explanatory X and response. Why we see that there is a general increase in Y with X. But it isn't terribly strong so we can write this as a moderate but positive correlation. Finally, part C. We want to compute the following sums and the correlation coefficient R. Then we want to answer as X increases. Should Why tend to increase or decrease? The thumbs are computed simply by following their formulas and are listed here. The correlation coefficient R. Is given by the formula here, which uses the sums above and the sample size and plug in. We obtain our equals 0.5111 which we are able to conclude from that as X increases, we should expect why to tend to increase.


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