If I try to do a line like this, you'll notice everything is kind of bending away from the line. But if I try to put a line on it, it's actually quite difficult. Is strong or weak? Is this linear or non-linear? Well, the first thing we wanna do is let's think about it Pause this video and think about, is it positive or negative, So, I would call this a positive, weak, linear relationship. You can use computers and other methods to actually find a more precise line that minimizes the collective distance to all of the points, but it looks like there is a positive, but I would say, this one is a weak linear relationship, 'cause we have a lot of points All right, now, let's lookĪt this data right over here. But they're all pretty close to the line, and seem to describe that trend roughly. And none of these data pointsĪre really strong outliers. So, positive, strong, linear, linear relationship. It really does look like a little bit of a fat line, if you The dots are prettyĬlose to the line there. The other one does, for these data points. And so, this one looks like it's positive. Negative, is it linear, non-linear, is it strong or weak? I'll get my ruler tool out here. Now, pause the video and see if you can think about this one. Outliers, well, what looks pretty far from the rest of the data? This could also be an outlier. Linear relationship, this one over here is reasonably high on the vertical variable, but it's low on the horizontal variable. So, for example, even though we're saying it's a positive, weak, Well, we have some data that is fairly off the line. If I said, hey, this line is trying to describe the data, Now, there's also this notion of outliers. It seems that, as we increase one, the other one increasesĪt roughly the same rate, although these data pointsĪre all over the place. As one variable increases, the other variable increases, roughly. The other variable increases as well, so something like this goes through the data andĪpproximates the direction. And it looks like I can try to put a line, it looks like, generally speaking, as one variable increases, And pause this video and think about what this one would be for you. Negative, strong, I'll call it reasonably, I'll just say strong,īut reasonably strong, linear, linear relationshipīetween these two variables. So I would call this a negative, reasonably strong linear relationship. This one gets a little bit further, but it's not, there's not And since, as we increase one variable, it looks like the other And so I would call thisĪ linear relationship. And it looks like I could plot a line that looks something like that, that goes roughly through the data. Precise ways of doing this, but I'm just eyeballing Through all of the data points, but you can try to get a You're not gonna, it's very unlikely you're gonna be able to go I could put a line through it that gets pretty close through the data. So, this data right over here, it looks like I could get a, So let's just first think about whether there's a linear And what we're going to do in this video is think about, well,Ĭan we try to fit a line, does it look like there's a linear or non-linear relationship between the variables on the different axes? How strong is that variable? Is it a positive, is itĪ negative relationship? And then, we'll think about This is often known as bivariate data, which is a very fancy way of saying, hey, you're plotting things that take two variables into consideration, and you're trying to see whether there's a pattern with how they relate. Scientists, or statisticians, went and plotted all of And that, when the age is 21 years old, this is the frequency. Whatever number this is, maybe this is 20 years old, And I could just show these data points, maybe for some kind of statistical survey, that, when the age is this, So, for example, in this one here, in the horizontal axis, we might have something like age, and then here it could be accident frequency. What we have here is six different scatter plots that show the relationship betweenĭifferent variables.
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