Hello,

Below attached is a statistics lesson. Please solve when possible. Answer will be rated immediately. Thank you so much.

Now, to determine how well correlated these two variables are, we must calculate a Correlation coefficient, "r" using these (X,Y) data values (Do you see all the the "SQUARED" values? This should remind you of the way we calculated the variance by squaring distances from the mean to get positive values. That is basically the purpose here as well) Here is the formula used to calculate the CORRELATION COEFFICIENT " It's basically a measure of the combined variance bewteen the two data sets (NO probability factor involved) r-values can range from 0.00 (0% = no correlation) to 1.00 (100% = perfect linear correlation). The higher the r-value the better the correlation (but proving nothing by itself) r=[n* (**Y) (EX) * (EY)] / V{[n * Ex- (EX)?] * [n* zy? - (EY)21} where n = 6 pairs of data X2 Y^2 (X*Y) 5 7 3 7 9 5 11 16 13 10 14 20 72 48 1.00, yes, r can be negative) 9) The COEFFICIENT OF DETERMINATION = P2, is also a percentage meaning that this % of the variation in "Y" is explained by the variation in "X" Herer = and (100% - p2 %) = 10) Your Conclusion: Based on the r and r2 values you calculated, how confident are you that there indeed could be a relationship? (Explain)