![]() ![]() Draw a line by going across from 3 mm and then down.Īn estimated 19 umbrellas would be sold if there was 3 mm of rainfall. The value of 3mm is within the range of data values that were used to draw the scatter graph.įind where 3 mm of rainfall is on the graph. To estimate the number sold for 3mm of rainfall, we use a process called interpolation. For example, how many umbrellas would be sold if there was 3mm of rainfall? What if there was 10mm of rainfall? The line of best fit for the scatter graph would look like this: Interpolation and extrapolationįrom the diagram above, we can estimate how many umbrellas would be sold for different amounts of rainfall. It should also follow the same steepness of the crosses. Lines of best fitĪ line of best fit is a sensible straight line that goes as centrally as possible through the coordinates plotted. No correlation means there is no connection between the two variables. Negative correlation means as one variable increases, the other variable decreases. Positive correlation means as one variable increases, so does the other variable. Graphs can either have positive correlation, negative correlation or no correlation. If data plotted on a scatter graph shows correlation, we cannot assume that the increase in one of the sets of data caused the increase or decrease in the other set of data – it might be coincidence or there may be some other cause that the two sets of data are related to. However, it is important to remember that correlation does not imply causation. On days with higher rainfall, there were a larger number of umbrellas sold. The graph shows that there is a positive correlation between the number of umbrellas sold and the amount of rainfall. The number of umbrellas sold and the amount of rainfall on 9 days is shown on the scatter graph and in the table. 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.Scatter graphs are a good way of displaying two sets of data to see if there is a correlation, or connection. 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. Mateo's scatter plot has a pretty strong positive correlation as the weeks increase his paycheck does too. 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: These types of plots show individual data values, as opposed to histograms and box-and-whisker plots. 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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