Scatter plots and estimating correlation Aa Aa 3 The correlation coefficients have been computed for five sets of sample scores and are shown in ascending order in the following table: -0.92 -0.76 0.00 0.66 0.82 The following are scatter plots (including the regression line) for each set of scores. (Scales for the X- and Y-axes are the same on all five plots.) r= r=D Study the plots, and then match each plot with its Pearson correlation coefficient. Click on each of the five boxes within the plots, and then enter the value of the Pearson correlation coefficient. Hint: To match up the Pearson correlation coefficients to their corresponding plots, remember to consider the direction of the relationship (which of the preceding plots show a negative relationship and which show a positive relationship?) and how closely the points fit the line (correlations whose absolute values are near 1 indicate that the points fit the line very closely).

In order to determine the Pearson correlation coefficient for each scatter plot, you should consider the direction of the relationship and how closely the points fit the line.

For the first scatter plot with a Pearson correlation coefficient of -0.92, you can see that there is a strong negative relationship between the variables. This means that as the values of one variable increase, the values of the other variable decrease. The points in the scatter plot fit the line very closely, indicating a strong correlation.

For the second scatter plot with a Pearson correlation coefficient of -0.76, you can see that there is a negative relationship between the variables, but the points do not fit the line as closely as in the first scatter plot. This indicates a weaker negative correlation.

For the third scatter plot with a Pearson correlation coefficient of 0.00, you can see that there is no relationship between the variables. The points are scattered randomly and do not follow a discernible pattern. This indicates a correlation of 0.

For the fourth scatter plot with a Pearson correlation coefficient of 0.66, you can see that there is a positive relationship between the variables. This means that as the values of one variable increase, the values of the other variable also increase. The points fit the line fairly closely, indicating a moderate positive correlation.

For the fifth scatter plot with a Pearson correlation coefficient of 0.82, you can see that there is a positive relationship between the variables, and the points fit the line very closely. This indicates a strong positive correlation.

Based on these observations, you can match the Pearson correlation coefficient with the corresponding scatter plot as follows:

Scatter plot with Pearson correlation coefficient of -0.92: strong negative correlation
Scatter plot with Pearson correlation coefficient of -0.76: weak negative correlation
Scatter plot with Pearson correlation coefficient of 0.00: no correlation
Scatter plot with Pearson correlation coefficient of 0.66: moderate positive correlation
Scatter plot with Pearson correlation coefficient of 0.82: strong positive correlation

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