![]() The further away from the known x-values you are the less confidence you can have in the accuracy of the predicted y-values. When you use a line or an equation to approximate a value outside the range of known values it is called linear extrapolation. For this you have to use a computer or a graphing calculator. To find the most accurate best-fit line you have to use the process of linear regression. If the data points come close to the best-fit line then the correlation is said to be strong. If the coefficient is a positive number, the variables are directly related (i.e., as the value of one variable goes up, the value of the other also tends to do. Approximately half of the data points should be below the line and half of the points above the line. To help with the predictions you can draw a line, called a best-fit line that passes close to most of the data points. If there is, as in our first example above, no apparent relationship between x and y the paired data are said to have no correlation and x and y are said to be independent.įrom a scatter plot you can make predictions as to what will happen next. If y tends to increase as x increases, x and y are said to have a positive correlationĪnd if y tends to decrease as x increases, x and y are said to have a negative correlation You can treat your data as ordered pairs and graph them in a scatter plot.Ī scatter plot is used to determine whether there is a relationship or not between paired data. You've summarized your result in a table. The graph shown below describes the change in the average temperature of the world over time. Let's say that you've the first of every month for one year been counting the amount of people on a subway platform each morning between 9 and 10 o'clock. Positive and negative linear associations from scatter plots. ![]()
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