1. Report the sample you selected and the question that was explored in the study. * There is a correlation between x and y. The scatter chart shows that the more hours a student studies the higher their grade percentage will be.

2. Report the r2 linear correlation coefficient and the linear regression equation produced in the Excel spreadsheet. * The linear correlation coefficient is positive. * The r^2 linear correlation coefficient is 0.785 * The linear regression equation is y=1.5608x+55.767 3. What would be the value of Pearson’s r (simply the square root of r2)? * R^2 is 0.785, which means that 78.5% of the total variation in y can be explained by the relationship between x and y. The other 21.5% remains unexplained. 4. Would Pearson’s r be positive or negative? What does this imply about the relationship between the factors in this study? * Pearson’s r is positive and equal to 1.0. Since Pearson r is positive this implies that there is a strong correlation between x and y. 5. What is the implication of any correlation found between the variables in the study you picked? * X & Y have a strong positive correlation. It seems the more hours put into studying the better the grade percentages will be. 6. Does this correlation imply a causal relationship? Explain. * There is a casual relationship, because on the graph it shows positive correlation, because the slopes are increasing. That implies if you spend more time studying your results are a higher grade. 7. Are there other variables that you think should have been examined that would have improved this study or help to pinpoint what factors are causal? * The environment/ home setting (single family home, number of siblings) can affect an individual’s studying