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D. Split your data by gender. Use your data to construct two box plots next to each other to show similarities differences between the male and female students. Use...

Question

D. Split your data by gender. Use your data to construct two box plots next to each other to show similarities differences between the male and female students. Use the same scale for each: Label the box plots with the 5 number summary: Looking at the box plots what conclusions can you make about similarities or differences between the male and female students? pointsWhat is your age? Women 20,19,19,26,18,19,31,20,15,15,21,18,19,17,26,19,20,20,20,15,18,29,33,18,19,21,20,29,18,19What is your age?

d. Split your data by gender. Use your data to construct two box plots next to each other to show similarities differences between the male and female students. Use the same scale for each: Label the box plots with the 5 number summary: Looking at the box plots what conclusions can you make about similarities or differences between the male and female students? points What is your age? Women 20,19,19,26,18,19,31,20,15,15,21,18,19,17,26,19,20,20,20,15,18,29,33,18,19,21,20,29,18,19 What is your age? Men 18,21,20,30,18,19,21,20,23,21,18,24,18,30,28,19,21,24,21,19,20,61,19,27,24,20,21,19,43,20,20,21



Answers

The scatter plots show the results of a survey of 20 randomly selected males ages $24-35 .$ Using age as the explanatory variable, match each graph with the appropriate description. Explain your reasoning. (a) Age and body temperature (b) Age and balance on student loans (c) Age and income (d) Age and height (FIGURE CAN'T COPY)

In this example, we are going through a series of graphs and trying to match them to their description. So we're given these data points plotted and this is all of these have ages, the explanatory variable and what we're using here is a sample size of 20 people. So we have 20 data points for each graph here. Ages, the explanatory variable and were given these measurements. So two of which 15 and 17 you can see are measured in thousands of units Where 16 and 18 are measured in just units. All right, so let's go ahead and start with Graph 15 Right here. So, we're looking at ages are explanatory variable measured in thousands of units. So, which description might match this while looking at our descriptions which one might be in thousands of units. So age and body temperature wouldn't make sense to measure body temperature and thousands of units. So we can rule that out. B age and student loan balance, that's possible. Student loan balances are probably going to be measured in thousands. So it could be that one and see right here, age and income income again is probably going to be measured in thousands of units. So it could be that one as well. And d we can rule that out because height is not going to be measured in thousands of units either, it's probably gonna be measured in inches, So we're down to student loan balance and income. So let's think this through. So for 15, what we see is we have a relatively upward sloping pattern going on. So it looks like we have relatively strong positive correlation And what we're likely to see as people are getting older as we go from being 26 and a relatively low income or student loan balance to 30 for having a relatively high income or student loan balance, which seems more likely. Well, it seems more likely for it to be income because when people are younger, we tend to have higher student loan balances, which we pay off over time. So we should have a downward slope Whereas income, we're probably going to be earning more over time. So we could say that 15 right here, we conclude that that is going to be a job and income. So that could be that's going to be c All right. And now jumping back to the student loan balance that we were talking about. Remember, because that's gonna be the other one in thousands of units. So we know that that's going to be 17. Actually, we can conclude that, but to explain why again, it's going to be that downward sloping, right? Because student loan balances, as we said, we pay those off over time. So as we're getting older, those balances should be dropping, giving us a relatively strong negative correlation. So that makes sense for that one. All right. So now moving on to 16 were down to either age and body temperature or age and height. Now Height is probably going to be a pretty random distribution, whereas body temperature is going to remain fairly constant because people can be a certain variety of heights. That's fairly random. But body temperature where we all sit somewhere between 96 and 100°F almost always. So for 16, this doesn't look very constant. It appears to be appears to be fairly random in its distribution. So we could say that 16 is d the description is age and height, Which leaves us for 18. The description age and body temperature. And that makes sense because what we're seeing here is all of our data points fall pretty constant with one another. It's a fairly straight horizontal line line between About 98 and 100. So that makes sense for that to be body temperature as well.

So the question here gives us a particular set of scatter plots here, and it wants us Thio use age, which is going to be the explanatory variable. Or, in other words, it's gonna be known as the independent variable here, and it wants us to match each graph with the appropriate description here. So for a here we have age and body temperature. And generally speaking, body temperature throughout age would not fluctuate much. So in this case, it's going to be the graph that is going to be a platt toe here, Um, for be here, it wants us to a match age and balance on student loans. So, um, when we are at the age of 24 here, that's when our student loans are generally going to be the highest. So over time, what we'd expect is that it's gonna decrease that it's gonna be a downward or negative, um, correlation or slope. In this particular case for see here we have aged and income and with agent income, we generally, um, expect our income to increase as we age through, for example, various promotions and vary us additional income sources. So in this case, you would have something that's going to be a positive correlation or slope here. And lastly for D here we have each and height here. What happens with height is that generally speaking, since we're only surveying people from 24 to 30 five here, you would expect it to be very similar to a plateau. However, over time we know that our grows our bones rather would start thio shrink because with age, you have a lot of gravity and pressure onto it, so we would expect it to have a slight, um, negative correlation or, um, slope.

In this example, we are going through a series of graphs and trying to match them to their description. So we're given these data points plotted and this is all of these have ages, the explanatory variable and what we're using here is a sample size of 20 people. So we have 20 data points for each graph here. Ages, the explanatory variable and were given these measurements. So two of which 15 and 17 you can see are measured in thousands of units Where 16 and 18 are measured in just units. All right, so let's go ahead and start with Graph 15 Right here. So, we're looking at ages are explanatory variable measured in thousands of units. So, which description might match this while looking at our descriptions which one might be in thousands of units. So age and body temperature wouldn't make sense to measure body temperature and thousands of units. So we can rule that out. B age and student loan balance, that's possible. Student loan balances are probably going to be measured in thousands. So it could be that one and see right here, age and income income again is probably going to be measured in thousands of units. So it could be that one as well. And d we can rule that out because height is not going to be measured in thousands of units either, it's probably gonna be measured in inches, So we're down to student loan balance and income. So let's think this through. So for 15, what we see is we have a relatively upward sloping pattern going on. So it looks like we have relatively strong positive correlation And what we're likely to see as people are getting older as we go from being 26 and a relatively low income or student loan balance to 30 for having a relatively high income or student loan balance, which seems more likely. Well, it seems more likely for it to be income because when people are younger, we tend to have higher student loan balances, which we pay off over time. So we should have a downward slope Whereas income, we're probably going to be earning more over time. So we could say that 15 right here, we conclude that that is going to be a job and income. So that could be that's going to be c All right. And now jumping back to the student loan balance that we were talking about. Remember, because that's gonna be the other one in thousands of units. So we know that that's going to be 17. Actually, we can conclude that, but to explain why again, it's going to be that downward sloping, right? Because student loan balances, as we said, we pay those off over time. So as we're getting older, those balances should be dropping, giving us a relatively strong negative correlation. So that makes sense for that one. All right. So now moving on to 16 were down to either age and body temperature or age and height. Now Height is probably going to be a pretty random distribution, whereas body temperature is going to remain fairly constant because people can be a certain variety of heights. That's fairly random. But body temperature where we all sit somewhere between 96 and 100°F almost always. So for 16, this doesn't look very constant. It appears to be appears to be fairly random in its distribution. So we could say that 16 is d the description is age and height, Which leaves us for 18. The description age and body temperature. And that makes sense because what we're seeing here is all of our data points fall pretty constant with one another. It's a fairly straight horizontal line line between About 98 and 100. So that makes sense for that to be body temperature as well.

And question number 29 five Numbers summary Order The data values from smallest to largest. So we get that to come in 34566 ago Coma 7777 And always go on so we can consider that the minimum value here is in question. Number 29 25 Numbers and question number 25 in question number 29 5 Number summary. Or there is a data that used from smallest to largest. So we get that the smallest number is two, and the largest number is 10. We can see that the minimum here is to, since the number of data values is even, the median is average off the two middle values off the 30 deficit so am equal to que two equal to seven plus seven over to equal to seven. The first school time is the median off the title values below the median, or at or 25% off the data, or 25 person of the data, so Q one equal to six for six over to equal to six. The third quarter is the median of the data values above the median, or at 75% of the data, so Q three people to eight plus eight over to equal to eight. And we can see that the maximum also, and we can see that the maximum also is 10 moving to the next box plugs. The whiskers of the box plot are at the minimum and maximum values. The box starts at the first cool tile ends at the Circle time and has a vertical line at the median. So so the first cool time. He's at 25%. I was assorted that the list The median is ed 5% and the third who died if at 75% thanks, represents and out there. So so The first court time is 25% of assorted that the list the median at 50 50%. The circle time at 75%. X x represents an out name. So from the minimum does the maximum the minimum. We have the two and the maximum we have. That is then and we have the circle tile is at eight and the medium as and the median ad seven and the minimum and the medium is at seven. On the first at six. And that the final answer. Thank you


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