A unitless measure that describes the strength of the linear relationship between two variables is called the linear correlation a.number b.value c.variable d. Coefficient?
there are many types of correlation coefficients, one of my favorite in Grad School (Information Theory) was the auto-correlation coefficient, used in signal processing.
Bob i have to pick a.b.c.d as the answer to fill in
Goodness. YOu can't figure out the answer a,b,c or d from what I gave you? I thought i put it on a silver platter.
Thank you bob. Sorry
At least I didn't try to snow you with the auto-crosscorrelation coefficent, also used in signal processing (it is used in forward error correcting code processing).
The correct answer is d. Coefficient. The unitless measure that describes the strength of the linear relationship between two variables is called the linear correlation coefficient.
To calculate the linear correlation coefficient, also known as Pearson's correlation coefficient, you need to follow these steps:
1. Gather your data: Collect the values of the two variables for which you want to calculate the linear correlation coefficient.
2. Calculate the means: Calculate the mean (average) of both variables separately.
3. Calculate the deviations: Find the difference between each value of each variable and its respective mean (deviation from the mean).
4. Calculate the products: Multiply the deviations of the two variables together for each pair of values.
5. Calculate the sum of the products: Sum up all the products calculated in the previous step.
6. Calculate the square of the deviations: Square each deviation for both variables and sum them up.
7. Calculate the square root of the sums of the squares of the deviations.
8. Divide the sum of the products (calculated in step 5) by the square root of the sums of the squares of the deviations (calculated in step 7) to get the linear correlation coefficient.
The resulting coefficient ranges from -1 to 1. A coefficient of -1 indicates a perfect negative linear relationship, a coefficient of 1 indicates a perfect positive linear relationship, and a coefficient of 0 indicates no linear relationship between the variables. The value of the coefficient also indicates the strength of the relationship, with values close to -1 or 1 indicating a strong linear relationship.