Questions: Before calculating the regression line, you expect the slope to be negative based on the points you plotted, suggesting that there may be a negative linear relationship between fatigue and depression. For the data above, the value of X̄=5, Y=6.35, covXY=-7.500, sX²=10.000, and sY²=9.644. Find the regression line for predicting Y given X. The slope of the regression line is approximately -0.75, and the Y intercept is approximately 10.1. The Pearson correlation is r=-0.7637 approximately. The variation amount in the depression scores (explained by their fatigue scores) is The standard error of the estimate is Suppose you want to predict the depression score for a new patient. The only information given is that this new patient is similar to patients A through E; therefore, your best guess for the new patient's level of depression is The error associated with this guess (that is, the "standard" amount your guess will be away from the true value) is

Transcript text: Before calculating the regression line, you expect the slope to be negative based on the points you plotted, suggesting that there may be a negative $\nabla$ linear relationship between fatigue and depression. For the data above, the value of $\bar{X}=5, Y=6.35, \operatorname{cov}_{X Y}=-7.500, s_{X}^{2}=10.000$, and $s_{Y}^{2}=9.644$. Find the regression line for predicting $Y$ given $X$. The slope of the regression line is $\underline{-0.75} \boldsymbol{\sim}$, and the $Y$ intercept is $10.1 \sim$. The Pearson correlation is $\mathrm{r}=-0.7637 \sim$. The variation amount in the depression scores (explained by their fatigue scores) is The standard error of the estimate is Suppose you want to predict the depression score for a new patient. The only information given is that this new patient is similar to patients A through E; therefore, your best guess for the new patient's level of depression is The error associated with this guess (that is, the "standard" amount your guess will be away from the true value) is