STATISTICAL TECHNIQUES AND TOOLS
REGRESSION
|
Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
|
|
|
The scatterplots for these two data sets both display a strong linear pattern.
|
|
|
In Meg’s data, 83% of the data points are closely related.
|
|
|
In Elise’s data, 83% of the variability in y can be explained by the linear association with x.
|
|
|
Meg’s data is more linear than Elise’s data.
|
|
|
Nothing can be concluded about the two data sets without looking scatterplots of the data.
|
Detailed explanation-1: -Meg has a set of bivariate numerical data with a correlation coefficient of r=0.83. Elise has a data set with a correlation coefficient of r=-0.83. What can you conclude about the two sets of data? The scatterplots for these two data sets both display a strong linear pattern.
Detailed explanation-2: -The magnitude of the correlation coefficient indicates the strength of the association. For example, a correlation of r = 0.9 suggests a strong, positive association between two variables, whereas a correlation of r =-0.2 suggest a weak, negative association.
Detailed explanation-3: -A correlation coefficient of 1.0 means that two variables have perfectly positive correlation.
Detailed explanation-4: -The correlation coefficient indicates both the strength and the direction of the linear relationship between two variables.