![]() ![]() Using this line, we can predict how much money Mateo will earn in his 20th week of work (assuming he continues this pattern).īased on this line, Mateo will earn approximately $157 in week 20. If there is a point that is much higher or lower (an outlier), it shouldn't be on the line. ![]() When drawing the line, you want to make sure that the line fits with most of the data. The line we draw through the points on the graph just needs to look like it fits the trend of the data. There are many complicated statistical formulas we could use to find this line, but for now, we will just estimate it. We use a "line of best fit" to make predictions based on past data. The word Correlation is made of Co-(meaning 'together'), and Relation. When the two sets of data are strongly linked together we say they have a High Correlation. In this example, each dot shows one persons weight versus their height. Mateo's scatter plot has a pretty strong positive correlation as the weeks increase his paycheck does too. A Scatter (XY) Plot has points that show the relationship between two sets of data. Video game scores and shoe size appear to have no correlation as one increases, the other one is not affected. No Correlation: there is no apparent relationship between the variables.Time spent studying and time spent on video games are negatively correlated as your time studying increases, time spent on video games decreases. Negative Correlation: as one variable increases, the other decreases.Height and shoe size are an example as one's height increases so does the shoe size. Positive Correlation: as one variable increases so does the other.There are three types of correlation: positive, negative, and none (no correlation). With scatter plots we often talk about how the variables relate to each other. ![]() Maybe his father is giving him more hours per week or more responsibilities. For example, with this dataset, it is clear that Mateo is earning more each week. Using this plot, we can see that in week 2 Mateo earned about $125, and in week 18 he earned about $165. In general, the independent variable (the variable that isn't influenced by anything) is on the x-axis, and the dependent variable (the one that is affected by the independent variable) is plotted on the y-axis. The weeks are plotted on the x-axis, and the amount of money he earned for that week is plotted on the y-axis. Here's a scatter plot of the amount of money Mateo earned each week working at his father's store: Just make sure that you set up your axes with scaling before you start to plot the ordered pairs. Creating a scatter plot is not difficult. 1: Scatter Plots Showing Types of Linear Correlation. These types of plots show individual data values, as opposed to histograms and box-and-whisker plots. Here are some examples of scatter plots and how strong the linear correlation is between the two variables. Scatter plots are an awesome way to display two-variable data (that is, data with only two variables) and make predictions based on the data.
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