I need several examples of causation using statistics. Please cite your sources as well.?

Question

Here is the original question:

Define “causation” in statistical analysis. Describe at least two factors that influence relationships between two variables and can lead to misinterpretation of data analysis

Answer 1

read what I have posted here about correlation and causation.  the point is that correlation is not causation.  there is a high correlation between grade school student's shoe size and their vocabulary.  but having a bigger shoe does not make you smarter.  there is a confounding factor of age which increases both.

The correlation coefficient, r, is a measure of the linear relationship between two variables.  If the data is non-linear then the correlation coefficient is meaningless.

r takes on values between -1 and 1.  negative values indicate the relationship between the variables is indirect, i.e., on a scatter plot the data tends to have a negative slope.  Positive values for r indicate the data tends to have a positive slope.  if r = 0 we say the variables are uncorrelated.

the closer the absolute value of r is to 1, the stronger the linear association between the two variables.

there are many different formulas for calculating the value of r.  if we let xbar and ybar be the means of two data sets.  sx and sy are the standard deviations in the data sets and n = total sample size then:

r = 1/(n - 1) * Σ( ((xi - xbar)/sx) * ((yi - ybar)/sy)) with the sum going from i = 1 to n

r = Covariance(X,Y) / [(√(Var(X))√(Var(Y))]

r = Σ(xi - xbar)(yi - ybar) / [ √(Σ(xi - xbar)²) √(Σ(yi - ybar)²)]

the second equation shows that the correlation coefficient the ratio between the measure of spread between the variables and the product of the spread within each variable.

r is unit less.

r is not affected by multiplying each data set by a constant, and a constant to each data set or interchanging x and y.

r is subject to outliers.

r² is called the coefficient of determination.  It is a measure of the proportion of variance in y explained by regression.

Also note that correlation is not causation.  Here is an example: the shoe size of grade school students and the student's vocabulary are highly correlated.  In other words, the larger the shoe size, the larger the vocabulary the student has.  Now it is easy to see that shoe size and vocabulary have nothing to do with each other, but they are highly correlated.   The reason is that there is a confounding factor, age.  the older the grade school student the larger the shoe size and the larger the vocabulary.

you cannot compare models by comparing the r values.  This is a long discussion, a full day lecture in the prob/stat courses I've instructed.  Model comparison is a topic usually saved for high level under grad courses or graduate level courses.

good sites with info about correlation are:

http://mathworld.wolfram.com/Correlation...

http://mathworld.wolfram.com/LeastSquare...