Assignment 2 for Chapter 9.

Assignment 2 for Chapter 9. - as a strong, direct...

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Multivariate Correlations Length Recall Length 1.0000 0.8589 Recall 0.8589 1.0000 Covariance Matrix Length Recall Length 32.57350 54.19677 Recall 54.19677 122.22598 Scatterplot Matrix 5 10 15 20 25 30 50 60 70 80 90 Length 5 10 15 20 25 30 Recall 50 60 70 80 90 Univariate Simple Statistics Column N DF Mean Std Dev Sum Minimum Maximum Length 49 48.00 20.2061 5.7073 990.100 8.5000 34.0000 Recall 49 48.00 70.1837 11.0556 3439.00 50.5000 96.0000 Note: Statistics were calculated for each column independently without regard for missing values in other columns. Correlation Coefficients answer these three questions: 1) A relationship between “Length” and “Recall” does exist. 2) The magnitude of the relationship is strong due to the coefficients placement at .8589 on a 0-1 absolute scale.
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3) The direction of the relationship is a direct one due to the positive representation of the relationship. The overall relationship between “Length” and “Recall” of newspaper reading can be described
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Unformatted text preview: as a strong, direct relationship. It can be concluded that the more hours spent reading a newspaper on a daily basis throughout the week, the better chance one has of recalling more news from the paper. Because of the coefficients high score on the 0-1 absolute scale, it can also be concluded that such a determination can be made with a relatively high degree of certainty, but the relationship is not considered to be a significant one. VAR L = 32.57327329 (Also the independent variance of Length) VAR R = 122.2262914 (Also the independent variance of Recall) These numbers can be found under the Covariance Matrix. After step six, the Correlation values became equivalent to the values contained within the Covariance Matrix meaning the Geometric mean, when placed in the denominator, must be equal to 1.00....
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Assignment 2 for Chapter 9. - as a strong, direct...

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