Choose a link to write about. Comment what you think about the link and what you learned. For example: \"This week's topics definitely are familiar. In a recent psychology class, the professor had us do one week covering basic statistics such as correlation. So I'm glad to see something I'm familiar with! We cannot confuse correlation with cause and effect because a correlation does not prove effect, it only shows that there is a relationship between the two. For example, a study was done in relation to hot weather and sexual assault. There was a definite correlation, but one does not cause the other. Another (funny) example is when people say "spoons make you fat." This week we've got 2 big topics to cover. Chi-square is one of the most widely used hypothesis tests. It's the only one we cover this term that it used when data are categorical (qualitative). We compare the data we collected to what we would have expected to get. The distribution changes are df increases. Here is a fabulous, step by step example of chi-square from a previous student: Step by step chi square from student Even though there is a whole new set of tests this week, it shouldn't be too overwhelming. I have great hopes that correlation and regression will look very familiar. Correlation is another "biggie." Tests tell us about relationships or associations between two variables by providing us with a number and a sign. The sign tells us the direction: negative if one variable goes up as the other goes down or positive if they go in the same direction. The number tells us the strength of the relationship ranging from 0 (no relationship) to 1 (a perfect relationship). Keep in mind that -0.9 is a strong as +0.9. We graph correlation using a scatterplot. Regression uses the correlation coefficient as a foundation for prediction. We predict data based on a model we have created from data we know. We find the straight line through the scatterplot that best fits through the data points on the scatterplot. Regression line Here's a starter if you'd like to give an opinion on any of these studies: Real life correlation Another recent correlation study One more So clearly, a huge amount of what we hear and read is based on correlational studies. We need to be careful not to let "them" confuse us with cause and effect