# Statistical measure relationship between two variables whose product

### Introduction to Correlation and Regression Analysis

The calculation and interpretation of the sample product moment correlation and emergency unit (A&E), we could use correlation and regression to determine When investigating a relationship between two variables, the first step is to. Relationships Between Variables, Part 3: Measures of Relationships. In this section, we discuss measures of relationships between two variables X and correlation coefficient and it is simply sXY divided by the product of. more specifically the Pearson Product Moment correlation coefficient. The correlation between two variables can be positive (i.e., higher You say that the correlation coefficient is a measure of the "strength of There are also statistical tests to determine whether an observed correlation is statistically.

Plot 5 shows a very strong circular relationship while Plot 6 a very strong quadratic pattern. It seems that a measure of a relationship should depend on what type of relationship it is. In this section, we will only be concerned for the most part about linear relationships and we will consider measures of such a relationship.

It should not be surprising that this measure will indicate no linear relationship for the two strongest relationships in the plots. Scatter plots Consider Plot 2 again. We want to measure the linear relationship exhibited in this plot. Two simple lines will help a lot. On the x-axis locate the sample mean of the X's and draw a vertical line through this point. On the y-axis locate the sample mean of the Y's and draw a horizontal line through this point.

Plot 2 with sample means The lines intersect atlocate it. This is our new center. The coordinates of X,Y relative to the new center are. Then it's easy to come up with many measures of linear relationships.

A simple one is to count the number of points with the same sign those in quadrants I and III and subtract the number of points with different signs those in quadrants II and IV. High values of this measure indicate a positive linear relationship while low values indicate a negative linear relationship. Instead of counting like and unlike signs, we consider a measure which takes the product of these new coordinates. Thus we have n products, one for each point in the plot.

Consider as a measure their average: Positive values of this measure indicate a positive linear relationship while negative values indicate a negative linear relationship.

## Correlation coefficient

Is this measure robust? No, you are catching on. For a given data set, we can always make this measure larger or smaller by changing the units. Suppose we have a positive linear relationship and X is measured in feet. If we change the X's to inches then sXY increases by the factor If we change the X's to mm's then sXY increases by the factor Thus we need to standardize our measure.

### Statistics review 7: Correlation and regression

In this chapter we revisit this problem in Chapter 11we will insist on an absolute measure which in absolute value cannot exceed 1. As we said, for all data sets. The extreme values are interesting: Values of r close to zero indicate little or no linear relationship. Scatter plots with values of r As we thought, the strongest relationships score 0 with our measure because they are both nonlinear.

The best linear pattern is Plot 2, although Plot 3 is close.

### Relationships Between Variables, Part 3: Measures of Relationships

We can do a bit more with the sample correlation coefficient. It is associated with the LS fit. It can be shown that where is the LS estimate of slope.

Each point represents an x,y pair in this case the gestational age, measured in weeks, and the birth weight, measured in grams. Note that the independent variable is on the horizontal axis or X-axisand the dependent variable is on the vertical axis or Y-axis. The scatter plot shows a positive or direct association between gestational age and birth weight.

Infants with shorter gestational ages are more likely to be born with lower weights and infants with longer gestational ages are more likely to be born with higher weights. The formula for the sample correlation coefficient is where Cov x,y is the covariance of x and y defined as are the sample variances of x and y, defined as The variances of x and y measure the variability of the x scores and y scores around their respective sample meansconsidered separately.

The covariance measures the variability of the x,y pairs around the mean of x and mean of y, considered simultaneously. To compute the sample correlation coefficient, we need to compute the variance of gestational age, the variance of birth weight and also the covariance of gestational age and birth weight.

We first summarize the gestational age data. The mean gestational age is: To compute the variance of gestational age, we need to sum the squared deviations or differences between each observed gestational age and the mean gestational age. The computations are summarized below.

## Correlation Coefficients

The variance of gestational age is: Next, we summarize the birth weight data. The mean birth weight is: The variance of birth weight is computed just as we did for gestational age as shown in the table below.

- Correlation and dependence
- Introduction to Correlation and Regression Analysis
- Statistics review 7: Correlation and regression

The variance of birth weight is: Next we compute the covariance, To compute the covariance of gestational age and birth weight, we need to multiply the deviation from the mean gestational age by the deviation from the mean birth weight for each participant i. Notice that we simply copy the deviations from the mean gestational age and birth weight from the two tables above into the table below and multiply. The covariance of gestational age and birth weight is: We now compute the sample correlation coefficient: Not surprisingly, the sample correlation coefficient indicates a strong positive correlation.

In practice, meaningful correlations i. There are also statistical tests to determine whether an observed correlation is statistically significant or not i. Procedures to test whether an observed sample correlation is suggestive of a statistically significant correlation are described in detail in Kleinbaum, Kupper and Muller. Boston University School of Public Health.