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Llle Expectancy 2018541Expactanay 20105Chenge 2010- 2018Hak Llte Expactancyls]Female Llte Expactancy[e}State TerrltoryHawal Cablomia New York022981.1679-J635.08793i...

Question

Llle Expectancy 2018541Expactanay 20105Chenge 2010- 2018Hak Llte Expactancyls]Female Llte Expactancy[e}State TerrltoryHawal Cablomia New York022981.1679-J635.08793i83-7281,2703.39Lannaenta80 .9400 7078 BE82.94Conneclkui90n782 B8Massachusetts Cobmdo Non Jarsoy nehingion78.2280 500o 026044374 ?278,39 77.460249Flonaa Anooe Ei2700nl0479 992776nA2 D6Anzona77.55 77,62 7818coNonh Dakota799aGuam 2 Oregon Puera Ricolz83.35 210.53777879.78Ltnoni8155NebrttVar Isia1637]9100 4Hameshirn01-48UrqinieWsconeN77 2

Llle Expectancy 2018541 Expactanay 20105 Chenge 2010- 2018 Hak Llte Expactancyls] Female Llte Expactancy[e} State Terrltory Hawal Cablomia New York 0229 81.16 79-J6 35.08 793i 83-72 81,27 03.39 Lannaenta 80 .94 00 70 78 BE 82.94 Conneclkui 90n7 82 B8 Massachusetts Cobmdo Non Jarsoy nehingion 78.22 80 50 0o 02 60443 74 ?2 78,39 77.46 0249 Flonaa Anooe Ei270 0nl04 79 992 776n A2 D6 Anzona 77.55 77,62 7818 co Nonh Dakota 799a Guam 2 Oregon Puera Ricolz 83.35 21 0.53 7778 79.78 Ltnoni 8155 Nebrtt Var Isia1637] 9100 4 Hameshirn 01-48 Urqinie WsconeN 77 20 Biso 039 *



Answers

Use the table showing state populations.
$\begin{array}{|c|c|c|c|c|}\hline 1 & {\text { California }} & {25,484,000} & {567,000} \\ \hline 2 & {\text { Texas }} & {22,118,000} & {447,000} \\ \hline 3 & {\text { New York }} & {19,190,000} & {70,000} \\ \hline 4 & {\text { Florida }} & {17,019,000} & {304,000} \\ \hline 5 & {\text { lllinois }} & {12,653,000} & {80,000} \\ \hline\end{array}$
Graph both equations for the years 2003 to 2020. Estimate when the populations of both states will be equal.

Okay for problem 30. We have to write equations that represent populations of Florida in New York X years after 2003. And we have to assume that both states continued to gain the same number of residents every year. And we have to let why equal the population? So we're gonna use the information right here. Um, Florida's population in 2003 and the annual average gain, as well as New York's population in 2003 and the average gain. So starting with Florida, we know that wise gonna represent the population S O. R equations gonna be wise equal to and are y intercept or our starting point, um is going to be the population in 2003 which is this 17,019,000. And since it's a population increase, we're going to be adding, And then we have to assume that, um, the average annual gain, um, will be the same. So in Florida, it's 304,000 and X has to represent the number of years after 2003. So since this is Thea and You'il, average gain X goes behind 304,000 then moving on to New York again. Why represents the population and R Y intercept or starting value is going to be the population during 2003 which is 19 million, 190,000. Because the population is increasing, we're adding, and then we can see that the average annual gain for New York is 70,000. So we're gonna have 70,000 X with X representing the number of years after 2003.

Hello, folks, is number 16? We have four parts to this. Gives us some information in the crab and a chart. We're supposed to make a graph using a graphing utility. So I chose the T I 89 e put the information here in the information for 2005 goes into the five. It's corresponds to five in our first column here. That way, it'll all work out, like in the years they're all five apart. So 15 4020 etcetera. Okay, and here's the bottom of the list. So we're gonna go ahead and do the linear regression on this on this part right here. Me? Go ahead and choose linear aggression. Enter and then we're gonna go have we have to go tell it where to find the information, which is in columns C one and C two. So I'm gonna do column C one and then down here. Oops, that's not a one. Let's start that again. Off. See One and we'll get it this time. See one and the next one's going to be C two. This middle part is good because it puts the equation directly into the graph for So we're gonna go ahead and say on this, Put that in. Why one when we don't? That way we can look at it. Okay, Ready to go there. It gives us the bits of information that we need for our equation. We're going to keep this in the calculator and take a look at it anyway, because that's what they ask us to do on part C. So we've used the regression feature completed part B, and we're gonna go ahead and take a look at it now. So very good chip there. I could go to graph and graph is going to show us the dots here represent the data points and then this is the best fit line. If you aren't seeing this, it's probably because you're zoomed correctly. If you go to, like Zoom, there's lots of different choices here. Even do zoom Data is a good one because it centers around the day that we entered and they ask us a question. And the question they ask is, Is the model of good fit So you would definitely say that Yes, the models of great fit. It fits the data just fine. And the fact that. It's really good. We can also tell that if you want to by looking at what the correlation coefficient was. But just by eyeballing it, yes, it's clearly good. So we're gonna go ahead and write that in here. Okay? We've completed part A. We completed part B. And now we're gonna go ahead and just say, Yeah, yes, the model follows the data is essentially what I'm trying to say. It's but closely, because that's what we want, right? We want to be close. Okay, then. Part being, they say, use this model to predict the population of the state. What state do they choose? Looks like New Jersey in 2050. Um, and they asked. Doesn't seem reasonable. Well, if you go back to our calculator, we could go look at 2050. Now, if 2005 is t five, well, we can go ahead and just go through this table of values. And this is what the calculator has come up with, each for each value. Okay. And we have our five or 10 or 15. We're going to go up to 2050. So we just have to goto sell 50. We're going to skip rolling down here and there is 50. So it would be 10,685 and there's one of the decimal, but it's been rounded, but they're really not asking. Yesto go much farther than that. Since we're talking about people, probably partial people are not as important. So let's go ahead and say it's about 10,685. And the question is, does that result seems reasonable? And, yeah, I think that it does, given the linear function that we were looking at earlier, Clearly this would be right in line. So it does for the day that now I'm going to go ahead and say a couple of things on this and give a totally complete answer. So the short answer is yes, it makes sense. Um, but here's my issue with it. Um, they probably you're trying to ask you, does the population of New Jersey sound like it's going to be 10,000? Right? And it's not It's not a gondola. Um, if you go back to the original chart where they have the information in there tells us that we've got it's not like people. It's thousands of people I believe so. Let's take a look. Yeah, So you have to make sure that you know you're looking at if you think that you're looking at just, you know, um, the total number of people that seems like too small population from New Jersey. But if we understand that we're looking for it and thousands, then we have a pretty good, pretty good idea. And yes, that seems reasonable. However, So I'm gonna put this on here, and you didn't choose whether I do. Yes, it makes sense, but population growth is exponential, right? We all know this from our science classes, so I'm gonna put that in there. But population growth, it's really never linear. That's just the nature of how populations grow. Um, exponential, right? A population doubles every so often, so yeah, it makes sense, given the data that we have. But to be devil's advocate, the population growth is going to be exponential. It's modeling a really population that's growing. So keep that in mind. I'd like to add that because I think that it's important part of it. So we didn't exactly say no. We said, yes. It makes sense because of the mark. Right? And that should be your answer. Thank you.

Hey, there were doing problem number 17. Um, in 17. They give us a bunch of numbers here, and they talk about the population of Wyoming s. Oh, we've got some data, and we have to perform some tasks. Um, they're asking us to use a graphing utility thio create a scatter plot. Then they went D five equal 2005 data. So I've gone ahead and enter the information here on the T I 89 calculated may be different. Or you could use a website. There's lots of websites out there that are very, very similar to this. Um, anyway, and so I'm gonna go ahead and run some the new regression on it. So that could tell us what the best fit line is to match this data on this. I do f five, and then I go ahead and set it up. So it says linear regression. And then I'm gonna go ahead and tell it where to find the data, which is in see one and C two, and I'm going to tell it to save that equation into the graph for so that we can find it and look at it. Why one great place for it. All right, we're ready to roll. Intern. All right, This is the full version of our A and B, and you'll notice that put the equation for a line up there so that we can see it. Um, if we're writing the equation, we could put these ones in. Okay, Um uh ah. Linear model of the data would include this. We could use rounded versions if you wanted to. Let's go ahead. And, uh, we created our scatter plot, and we found the model for the data. Let's go ahead and right this in, shall we? Okay, this is the equation, and it's again, it's sort of it's rounded form. Um, next, we're gonna get back over here, and let's take a look at what this is. They want to step, model and put the model and the data points all in one, which we could do fairly easily. Uh, let's enter here and then remember, I sent it over to the graph. So if your graph looks like this, you probably need to zoom. My favorite zoom is number nine zoom data. Whoa. Okay, this is good, because this is different than the other problems. Um, is the model a good fit? Well, you know, it ain't too good. It just doesn't look very good to me. What it looks like is I've got a clear sort of parabolic growth curve. It goes up and down again, and I'm trying to model that using a line. So unfortunately, I'm gonna have to say that. No, it doesn't look like a very good fit. It all, um I want Yeah, because it it looks like it's not really the right shape to be using it. So I'm gonna say no, it doesn't fit the data. No, comma, it doesn't fit the data. The data doesn't look like a line. Right? Okay. And then I'm in for D. They ask us to predict the population of Wyoming when it's the year 2050. I've got some family in Wyoming, so let's see, in 2050 what will that be? We could substitute back in 2050. We used 50 instead of 2050 for our X value here. T I guess it probably should be. I wouldn't change it to to you while I'm here just because it's time. And they asked for tea. E like that better t But we can also go back to our calculator. And since this is in the graph, are already we can just look at the table view, which gives us all the results, just those numbers that it's calculated already for us. So we don't have to necessarily go through the entire solving the equation by hand. We already did solve the equation for all numbers because we entered it in here and got a model. So it's asking for what, 2050? So that would correspond to 50. That would be right there. 543.43 by those people. I've 43.43 now. This is in thousands said eso the actual population people would be 543,000 comma, comma, 430 people. I like to go and fill this out all the way. People in 2050. Okay, so does the results seem reasonable and explain This is probably the toughest part. So for D Okay, when we're looking at this, I was already saying that I didn't like the way that this sort of curve was modeled using the line. Um, the way that the trend is going. If we were to extrapolated just, you know, imagining where the next data point would be, we would think it would be lower than the value for the line. And indeed, in 2050 this number is higher, like it's a growing population. But if it actually does sort of follow this curve, 2050 might be way down there, and then we might be way off. You understand what I'm saying? I mean, this is probably a more accurate like representation. If I just do a curve, it would go with the data better. Okay, so in this case, yeah, it's like 10 kinds of messed up. How will we phrase that there's results seem reasonable? I'll say, given the data. No, it doesn't s. So you don't have to watch me right when deposit. Okay, So given the data trend, it doesn't seem reasonable that it would be increasing. Now we know the populations increase over time. We know that, um, but if you've ever been to Wyoming, sometimes Wyoming hasn't issues. It's like there's just not a whole lot of people, and the population could very easily go up for some reason, and then decrease for some reason. Um, so in my opinion, it does not seem reasonable, but these were only predictions, so I mean, you know, most populations grow, so that part seems reasonable. It's a little bit of both. Honestly, All right. And that Make sure we check that off. Yes, and that's the end.

So in this question we are told that our total population P is 298 475 point it six we're told that are male population is 145 472.3 and were asked to predict the female population. So to get the female population, we basically subtract the male population from the total population. So we have point it. Three three zero zero being minus five minutes. Three fine one. So a total female population here is 153 zero. Zahra drink point.


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