Select the letter of the scatterplot below which corresponds to the correlation coefficient. Linear When the relationship between two sets of variables can be represented with a line. Question: Match the correlation coefficients with their scatter plots. Weak negative As one set of values increases, the other somewhat decreases. Find the slope-intercept form of the equation of the line that best fits the data. When there is a correlation, identify the relationship as linear or nonlinear. No correlation The points are scattered randomly with no visible pattern. State if there appears to be a positive correlation, negative correlation, or no correlation. Moderate negative As one set of values increases, the other moderately decreases. Weak positive As one set of values increases, the other somewhat increases. Strong negative As one set of values increases, the other set decreases. © NSW Department of Education, 2019 5Ĭorrelation matching activity Data Definition Word Example Non-examples Data Definition Word Example Non-example © NSW Department of Education, 2019 1 Data Definition Word Example Non-example Data Definition Word Example Non-example 2 MA-S2 Descriptive statistics and bivariate data analysis Data Definition Word Example Non-example Data Definition Word Example Non-example 4 MA-S2 Descriptive statistics and bivariate data analysis Word cards Definition cards Strong positive As one set of values increases, the other set increases. Linear When the relationship between two sets of variables can be represented with a line. No correlation The points are scattered randomly with no visible pattern. Here we use linear interpolation to estimate the sales at 21 ☌.Correlation matching activity Data Definition Word Example Non-examples Data Definition Word Example Non-example © NSW Department of Education, 2019 1 Data Definition Word Example Non-example Data Definition Word Example Non-example 2 MA-S2 Descriptive statistics and bivariate data analysis Data Definition Word Example Non-example Data Definition Word Example Non-example 4 MA-S2 Descriptive statistics and bivariate data analysis Word cards Definition cards Strong positive As one set of values increases, the other set increases. Interpolation is where we find a value inside our set of data points. Example: Sea Level RiseĪnd here I have drawn on a "Line of Best Fit". Match each scatter plot with the correct correlation coefficient. 5 Match the scatter plots with their approximate correlation coefficients. Try to have the line as close as possible to all points, and as many points above the line as below.īut for better accuracy we can calculate the line using Least Squares Regression and the Least Squares Calculator. The correlation coefficients for the six scatter plots shown below are -0.85, -0.40, 0, 0.50, 0.90 and 0.99. Question: Match the scatter plots with their approximate correlation coefficients. We can also draw a "Line of Best Fit" (also called a "Trend Line") on our scatter plot: It is now easy to see that warmer weather leads to more sales, but the relationship is not perfect. Here are their figures for the last 12 days: Ice Cream Sales vs TemperatureĪnd here is the same data as a Scatter Plot: The local ice cream shop keeps track of how much ice cream they sell versus the noon temperature on that day. (The data is plotted on the graph as " Cartesian (x,y) Coordinates") Example: The example scatter plot above shows the diameters and. Scatter plots are used to observe relationships between variables. The position of each dot on the horizontal and vertical axis indicates values for an individual data point. In this example, each dot shows one person's weight versus their height. A scatter plot (aka scatter chart, scatter graph) uses dots to represent values for two different numeric variables. So, the correlation coefficient r 0.63 matches this scatter plot. It does not display a strong positive correlation close to 1 since the data points are scattered. A Scatter (XY) Plot has points that show the relationship between two sets of data. Scatter plot A represents a positive correlation because as the values of x increase the value of y also increases.
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