# Cause and effect relationship regression

### Statistical Language - Correlation and Causation

Thus in this regression relationship, we can say that there is a 'cause' and 'effect' relationship between the variables. A special food may be tested on poultry. Tags: Six Sigma regression analysis cause and effect analysis cost This analysis shows that the relationship between Interactions and Sales. Regression analysis involves identifying the relationship between a dependent analyses can be interpreted as establishing cause-and-effect relationships.

Thus dependence does not mean response effect due to some cause. Some examples are discussed here to elaborate upon the idea. The sun rises and the shining sun increases the temperature.

## Using Regression Analysis to Improve Cause and Effect Analysis

Let temperature be noted by. With an increase inthe ice on the mountains melts and the average thickness of ice decreases.

It is possible that the thickness of ice decreases due to an increase in temperature. But it is also possible that the thickness of the ice is decreasing due to the weight and hardening of the ice. We may be regressing the thickness against the temperature only while another important factor is being ignored.

In this type of problem, more than one regression equation is developed and then the equations are solved simultaneously to estimate the unknown parameters.

### Introduction to Correlation and Regression Analysis

We may think that an increase in the number of workers increases the production of fans in a factory. The increase in may be due to a change in the administration and some changes about the leave rules and other benefits. In this example, birth weight is the dependent variable and gestational age is the independent variable.

The data are displayed in a scatter diagram in the figure below. 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. 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.

• Introduction to Correlation and Regression Analysis
• Australian Bureau of Statistics
• Econometric Theory/Regression versus Causation and Correlation

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.