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Editor plotm JSS1-m conc.m plotm [14.62:6.951 [273.15:308.15];VariablesWorkspace NamaValuo1x36 doubleTetp -139.3441+(1.5757e5/Ta)-. 6423e7/ (Ta^2) ) (1.2438210/ (Ta...

Question

Editor plotm JSS1-m conc.m plotm [14.62:6.951 [273.15:308.15];VariablesWorkspace NamaValuo1x36 doubleTetp -139.3441+(1.5757e5/Ta)-. 6423e7/ (Ta^2) ) (1.2438210/ (Ta 3)) (8.5 6228e11/(Ta^4) )-(log(ox) ) ;plot (Temp)Command Window ULMCASAISError In pot (Line Temp -139.3441+(1.5757e5/Ta)-Waring: Function plot has the same name MATLAB builtin. We suggest You rename the function to avoid potentia name contlict. plot Error using Matrix dimensions Mvst boreeError in plot (line Temp -139.3441+(1.5757e5/

Editor plotm JSS1-m conc.m plotm [14.62:6.951 [273.15:308.15]; Variables Workspace Nama Valuo 1x36 double Tetp -139.3441+(1.5757e5/Ta)-. 6423e7/ (Ta^2) ) (1.2438210/ (Ta 3)) (8.5 6228e11/(Ta^4) )-(log(ox) ) ; plot (Temp) Command Window ULMCASAIS Error In pot (Line Temp -139.3441+(1.5757e5/Ta)- Waring: Function plot has the same name MATLAB builtin. We suggest You rename the function to avoid potentia name contlict. plot Error using Matrix dimensions Mvst boree Error in plot (line Temp -139.3441+(1.5757e5/Ta) -_



Answers

Use the following information to answer the next seven exercises. A video game developer is testing a new game on three different groups. Each group represents a different target market for the game. The developer collects scores from a random sample from each group. The results are shown in Table 13.16
$$\begin{array}{|l|l|l|}\hline \text { Group A } & {\text { Group } B} & {\text { Group } C} \\ \hline 101 & {151} & {101} \\ \hline 108 & {149} & {109} \\ \hline 98 & {160} & {198} \\ \hline 107 & {112} & {186} \\ \hline 111 & {126} & {160} \\ \hline\end{array}$$

What is the df(num)?

In Medlab create a few Hilbert mattresses of larger dimension. Isn't the function ill of an using the built in can't function what conclusions can draw for Hilbert mattresses with even larger dimensions. Well first we remember the Hilbert metrics, let's say age has injuries. That position I. J. Equal one over I Plus the A -1. That is the R receiver goals of indigent numbers. To find out some of the industry's in, this is Vijay, That sound -1 for example, age five by five. And it's a Hilbert mattress. four or 5 rows and columns. Remember that? This matrix is square matrix. So we get for a position 11 we get 1/1, there is one Then 1/2, 1/3, and 1/5. As the first roll signal road started 1/2 and 1/3. 1/4, 1 5th and 16. And like that we go then one third, 1/4 one face 16 and one sevens. Then we have say this kid would have been for a moment And then we have 1/4, one fits, 1/6 1/7 and 1/8. And finally won over five. What over six went over 7, 1/8 and 1/9. And this is the human rhetoric 585 in general. Then the elements Of the metrics that position I. J. is even by 1/1 plus Zaman's one. And it's A. M by N. That is a square matrix of order. And so it is very easy to construct. And this is a typical example of uh of an ill conditioned metrics. It means the condition number, condition Yeah. And then that is the condition number of the metrics. It's a very high number indicating that these metrics has. Um It's very difficult to work with when doing numerical calculations such as southern linear system etcetera. So conditioned metrics of age, remember, is the norm of the metrics times the norm of the inverse of the metrics. And this number is always greater than or equal to one for any metric norm. And the larger the number, the larger dis number more ill condition is metrics. And there are some algorithms to estimate this number. In reality the algorithm doesn't does not calculate the university metrics, but other strategies to estimate the number. And um for these metrics we're an equal to for example, we have, we applied the ill two common that generates the H two by two matrix humor metrics of order to And for that metrics we know already how to construct it metrics. But the important thing is that the condition number is equal or is about brother about t at like condition number of the Hilbert medics to buy two is mhm 1928. It's about 1928. And this is a model moderate number. That is, we cannot say it's a very ill condition but And we bused to an equal three. That is one more row and column. The Hilbert Matrix three. The condition number. This case is about mhm uh 524. And see 14, two or 6. You see that the number grows rapidly. That is one More order only. We have another magnitude of the the condition number. And for an equal for yes condition number given by the common cont in Medlab. Give us one point five times stand to the four. That is about 15,000. It's already considered a large condition number. And let's say for an equal 10 we get a condition number of about six Time stand to the 13. It is a very large condition number already. This very ill conditioned metrics and we only have 10 rows and columns whose rapidly getting ill condition you can also calculate in order to know more things about this type of metrics. Um The determinant for example, determinant of He ordered two metrics to your point to hear to your point 083. And if we calculate this year the 10 by 10 metric silver metrics determinant, we get two point uh 16 times 10 to the negative three. Very small number. That is the determinant of the matrix is Going to zero very quickly. That is is becoming non in vertebral rapidly. So we have but the metrics is always convertible. That's very important. The determinant of the matrix can be calculated analytically by a formula and we can see that these metrics is always in variable but determinant. Mhm uh is uh different from zero but is getting small. Smaller and smaller each time we add a road at a call in to the metrics. So the magic is getting closer to be non convertible but it's always in verbal. So but determinant is very small and the condition number is very large, indicating that the metrics is getting ill conditioned or more equal conditions as we of monte order or dimension of the metrics. So the seat is a very uh well conditioned metrics. And this type of metrics appears in the problems of leads square approximation of functions by polynomial means. So it's a very common metrics in vertical calculations. Remember the least square mythology is used to feet. I said the data through a function. So if we want to feed data through pollen novel that is using a polynomial asi model for the data. If we write the necessary conditions to find that would normally we truly find out these metrics here. And that is why all calculations we have to do with these metrics which are generally solid win linear system in which this is the coefficient metrics will be very difficult to solve because the property of these metrics of being ill conditioned for moderately uh by yourself. And let's say now we are going to end with the value of 1000 for example. And let medlab calculate the condition number of that metrics. And we found this case um determinant is reported as zero of course because numerically in the computer, it's awesome. All that is considered zero and the condition number is three point it's about three points 27 times 10 to the 21 very large condition numbers. So it's very ill conditioned metrics. And getting beyond that order is a problem because the algorithm uh starts to consumed very mhm simply your time or elapsed time to calculate that. So getting beyond that, it will be a problem to find the estimation of the condition number. So this is it. This is a typical example of an ill conditioned metrics, the Hilbert matrix, which appeared in the least square approximation of functions by pulling memos.

For this question, you're not giving me the data to construct the box plot, so therefore I request you to kindly re upload the question and have the data of the box plot so I can plot it for you. So because of that, I'm unable to answer the question. Candy terry posting the question with the picture or details about the data in the question itself, so begins. Thank you for watching Yeah.

In discussion, we are going to calculate the pressure at two of the mountain from 6733 m above the sea level. Now for this use brosius left it on equation which is natural Law B two divided by fever equal to minus there. That's pepper. Why did But uh my people are by one divided by 30 to minus one, divided by Eva. Now here we have given and at weapon equal to 45 wine zero seven bellagio. Far more. And also temperature at the top of the mountain. That is key to equal to 75 degree senses. No. Uh huh, drowned level waters, boils oh, 100 degree Celsius and one atmospheric. Very sure that means that here even is equal to one. It must fear even is equal to 2 73 0.15 plus 100. So here we just don't work the value of Stephen into our Calvin. So steven is equal to 3 73 wine 15 gathering now as we have given. Yeah, well at rapper is equal to 45 point zero salin eligible are more which we can return this and 4507 is a little june five more. And also we have the value of our that is 8.814 june or more. Yeah, no and so or a mountain. It must be equal to to 73.15 plus 75. Equal to three 48.15 Calvin. And here you have to calculate the pressure. So by using closest collateral equation bigger Natural Law. People divided by one equal to minus four 5070 Yeah I did buy 8.314 Multiply by one divided by 3 48.15 minus one divided by 3 73 wine 15 No on simplifying this river. Get Natural Law we two divided by one equal to minus 1.43 It will Segal Segal through 84 seven silent 67 On. Now on removing the natural look, we will get me to derided by one is equal to E. To the power minus 1.0 for three 200284 77671 So here on simplifying this, where that T two is equal to 0.352 See two Why three 70 878 atmosphere. All we can see. Bye bye link off. This value that we do is equal to 0.352 atmosphere. This this is required solution.

So we're trying to find a D f. No. Do you have none IHS given as falling formula in which k iss the total number Oh, common. Or we can have in this case, that's what a number of groups that we have. So this would mean because we have good a group Group C writing this one. It was too, so yeah, we'll see.


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