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It to the vertical axis, it looks like they would get about a 97. So if I go straight up, whereĭo we intersect our model? Where do we intersect our line? So it looks like they would Which is right around, let's see, this would be, 3.8 Based on this equation, estimate the score for a student that spent 3.8 hours studying. So it would be thisĬhoice right over here. And if we look at all of these choices, only this one has a slope of 20. Trying to fit to the data, is 20 over one. So our change in y overĬhange in x for this model, for this line that's 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. Of these choices here have a y-intercept of 20, so So essentially, we just want to figure out what is the equation of this line? Well, it looks like the
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Over here by this line that's trying to fit to the, that's trying to fit to the data. Model, they're really saying which of these linear equations describes or is being plotted right Of these linear equations best describes the given And so then, and these areĪll the different students, each of these points represents a student, and then they fit a line. This over here looks like a student who studied over four hours, or they reported that, and they got, looks likeĪ 95 or a 96 on the exam. 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 scoredĪbout a 64, 65 on the test. More than half an hour, and they didn't actuallyĭo that well on the test, looks like they scored aĤ3 or a 44 on the test. Point right over here, this shows that some studentĪt least self-reported they studied a little bit Which of these linear equations best describes the given model? So this, you know, this Like a pretty good fit if I just eyeball it. They don't tell us how the line was fit, but this actually looks Students spent studying and their score on the test. Shows the relationship between how many hours Hope it helps!Ĭongratulations, you can now add the regression line equation and several measures to your ggplot2 visualizations.Included a survey question asking how many hours students If you simply need an introduction into R, and less into the Data Science part, I can absolutely recommend this book by Richard Cotton.
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rr.label.)) +īy the way, if you’re having trouble understanding some of the code and concepts, I can highly recommend “An Introduction to Statistical Learning: with Applications in R”, which is the must-have data science bible. Stat_regline_equation(label.y = 350, aes(label =. Stat_regline_equation(label.y = 400, aes(label =. For every subset of your data, there is a different regression line equation and accompanying measures. BIC.label.: BIC for the fitted model.īy the way, you can easily use the measures from ggpubr in facets using facet_wrap() or facet_grid(). adj.rr.label.: Adjusted R2 of the fitted model as a character string to be parsed rr.label.: R2 of the fitted model as a character string to be parsed eq.label.: equation for the fitted polynomial as a character string to be parsed
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Here are the other measures you can access: