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. Also keep in mind that other factors may be involved in a cause-effect relationship.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. Of course this is not true!Īlways be careful what you infer from your statistical analyses. So, this must mean that the number of jars of strawberry jam sold in New York was causing an increase in the number of classical music CDs sold in Florida. The data was examined and was plottedįrom looking at the graph, it can be seen that there is a high positive correlation between these two sets of data. For the same time frame, the number of copies of a popular classical music CD sold in Florida was recorded. No correlation means that the data just doesn’t show if studying longer has any affect on Regents examination scores.Ĭheck out these graphs for visual interpretations of types of correlations:ĭuring the months of February and March, the weekly number of jars of strawberry jam sold at a local market in New York was recorded. If the plot on the graph is scattered in such a way that it does not approximate a line (it does not appear to rise or fall), there is no correlation between the sets of data. Under a negative correlation, the longer I study, the worse grade I would get on my Regents examination. If the slope of the line had been negative (falling from left to right), a negative correlation would exist since the slope of the line would have been negative. It all depends on the data being examined. There may be sets of data that show that there is NOT a positive correlation between hours studying and better Regents scores. Note: Just because this set of data showed a positive correlation does not mean that the relationship is positive for all sets of data concerning study time and Regents scores. This means that according to this set of data, the longer I study, the better grade I will get on my Regents examination. Since the slope of the line is positive, there is a positive correlation between the two sets of data. The data displayed on the graph resembles a line rising from left to right. Notice: Certain values may have more than one result, Remember when making a scatter plot, do NOT connect the dots. Given the data below, a scatter plot has been prepared to represent the data. Let’s decide if studying longer will affect Regents grades based upon a specific set of data. Scatter plots will often show at a glance whether a relationship exists between two sets of data. Statisticians and quality control technicians gather data to determine correlations (relationships) between such events.
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