- Estimating the line of best fit exercise
- Eyeballing the line of best fit
- Line of best fit: smoking in 1945
- Estimating slope of line of best fit
- Estimating with linear regression (linear models)
- Estimating equations of lines of best fit, and using them to make predictions
- Interpreting a trend line
- Interpreting slope and y-intercept for linear models
Use a regression line to make a prediction.
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- For the second part of the question, could you just plug 3.8 into the equation? Isn't that what the question was asking, and not just to look off the graph?(51 votes)
- Yes. The question specifically asks you to use the equation, in which case the answer would be 96, not 97, but since the true values seem to be varying by up to about +/-10, 97 isn't too bad an estimate.(18 votes)
- there is a huge mistake in this video. the estimate is wrong., I typed in 97 and it turns out it was 96 and then I had to restart everything because of this answer !(18 votes)
- The estimate is anything near 97, it doesn't have to be 97 exactly because it is a estimate. The way I got exactly 96 was by plugging 3.8 for x in the equation.(18 votes)
- How can you exactly estimate this?(9 votes)
- You take your variable for x (in this case 3.8) and plug it into the equation you chose. Sal simply eyeballed it, but in order to be more accurate, you should work it out with the equation.(5 votes)
- This video doesn't explain how to calculate plots that are not on the graph. How do we guess the future plots? What's the formula?(8 votes)
- There are multiple complains on this, as for myself, do not understand this "simple" format.(8 votes)
- ITS 96 ! all the trust i've built up for years.. where is it? GONE!(5 votes)
- In the next Practice lesson, Estimating equations of lines of best fit, and using them to make predictions, there is a multiple-choice question with 3 possible answers in y-intercept form. But the y has a ^ over it. Can anybody tell me what this means? Here's the link: https://www.khanacademy.org/math/cc-eighth-grade-math/cc-8th-data/cc-8th-line-of-best-fit/e/equations-of-lines-of-best-fit-to-make-predictions?modal=1
- How is it 20x. I thought it would be 10x because if you move one and go up 1 it will lead to 30 and 30-20 is 10(3 votes)
- No, it is 20x because if you look at the x-axis, the numbers increase by 0.5, not one. When the x value increases by 0.5, the y-value increases by 10, and when you divide 10 by 0.5, you get 20. It is important to look at how much the values of each axis increases. Hope that helps(3 votes)
- For the second part of the question, could you just plug 3.8 into the equation? Isn't that what the question was asking, and not just to look off the graph?(3 votes)
- [Instructor] Liz's math test included a survey question asking how many hours students spent studying for the test. The scatter plot below shows the relationship between how many hours students spent studying and their score on the test. A line was fit to the data to model the relationship. They don't tell us how the line was fit, but this actually looks like a pretty good fit if I just eyeball it. Which of these linear equations best describes the given model? So this, you know, this point right over here, this shows that some student at least self-reported they studied a little bit more than half an hour, and they didn't actually do that well on the test, looks like they scored a 43 or a 44 on the test. This right over here shows, or like this one over here is a student who says they studied two hours, and it looks like they scored about a 64, 65 on the test. And this over here or this over here looks like a student who studied over four hours, or they reported that, and they got, looks like a 95 or a 96 on the exam. And so then, and these are all the different students, each of these points represents a student, and then they fit a line. And when they say which of these linear equations best describes the given model, they're really saying which of these linear equations describes or is being plotted right over here by this line that's trying to fit to the, that's trying to fit to the data. So essentially, we just want to figure out what is the equation of this line? Well, it looks like the y-intercept right over here is 20. And it looks like all of these choices here have a y-intercept of 20, so that doesn't help us much. But let's think about what the slope is. When we increase by one, when we increase along our x-axis by one, so change in x is one, what is our change in y? Our change in y looks like, let's see, we went from 20 to 40. It looks like we went up by 20. So our change in y over change in x for this model, for this line that's trying to fit to the data, is 20 over one. So this is going to be our slope. And if we look at all of these choices, only this one has a slope of 20. So it would be this choice right over here. Based on this equation, estimate the score for a student that spent 3.8 hours studying. So we would go to 3.8, which is right around, let's see, this would be, 3.8 would be right around here. So let's estimate that score. So if I go straight up, where do we intersect our model? Where do we intersect our line? So it looks like they would get a pretty high score. Let's see, if I were to take it to the vertical axis, it looks like they would get about a 97. So I would write that my estimate is that they would get a 97 based on this model. And once again, this is only a model. It's not a guarantee that if someone studies 3.8 hours, they're gonna get a 97, but it could give an indication of what maybe, might be reasonable to expect, assuming that the time studying is the variable that matters. But you also have to be careful with these models because it might imply if you kept going that if you get, if you study for nine hours, you're gonna get a 200 on the exam, even though something like that is impossible. So you always have to be careful extrapolating with models, and take it with a grain of salt. This is just a model that's trying to fit to this data. And you might be able to use it to estimate things or to maybe set some form of an expectation, but take it all with a grain of salt.