If a researcher wanted to predict how well a student might do in college, what variables do you think he or she might examine?

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1. If a researcher wanted to predict how well a student might do in college, what variables do you think he or she might examine? What statistical procedure would he or she use?

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2. The research of “Eating Dark Chocolate and the theory of it Reducing the Risk of Depression,

Research Shows. Retrieved from https://philadelphia.cbslocal.com/2019/10/28/eating-dark-chocolate-reduces-risk-of-depression-research-shows/

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3. I find this to be a very interesting research as I enjoy eating dark chocolate with almonds and I have heard of the great nutrients and good it is for your health. I was not aware of all the different benefits it has to our body from helping with depression to improving cognitive function, circulation, immune response and mood boost (Stahl, 2019). The research provide evidence that consuming dark chocolate may be associated with the reduction of levels of depression due to its high concentration of cacao an antioxidant that lowers inflammation. Research does show a correlation between eating dark chocolate and the reduction on depression. The correlation on this research is dark chocolate and depression. According to Jackson (2017), correlation allows the researcher to describe the relationship between two measured variables and allows the prediction from one variable to another, however, I believe additional research might be necessary to clarify causation as there might be different reasons why people might or might not be interested on eating chocolate. Further investigation can determine if chocolate consumption prevents depression.

Reference

Jackson, S. L. (2017). Statistics plain and simple (4th ed.).

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4. Correlations can either be positive or negative, and the negative sign seen in this data set is telling us the direction of the relationship between the two variables. If variables change in the same direction, the correlation is called a direct or positive correlation, and if variables change in opposite directions, the correlation is called indirect or negative correlation, (Jackson, 2017). From the above data, we can see that each variable has a sample size of 8, and the relationship between the two variables is being measured. A correlation can range in value from -1.00 to +1.00, where a value of 1 would indicate a perfect correlation, and if the correlation were to be 0 there would be no correlation at all, (Jackson, 2017). The negative sign in the Pearson Correlation value tells us the strength of the association between both variables, and in this case, is a strong correlation. The higher the correlation coefficient, the stronger the relationship is, and researchers would consider the correlation above of -.800 to be a strong correlation, meaning the variables are strongly correlated with one another. The negative sign seen also indicates the direction of the relationship between the variables.

Jackson, S. (2017). Statistics Plain and Simple (4th ed.). Boston, Ma. Cengage Learning.