Question

part 1 of 4
(a) compute the least - squares regression line for predicting weight from ott. round the slope and y - intercept to at least two decimal places, if necessary.
the least - squares regression line for predicting weight from ott is \\(\hat{y}=\square\\).

Explanation:

Step1: Recall regression - line formula

The least - squares regression line is of the form $\hat{y}=b_0 + b_1x$, where $b_1=\frac{n\sum_{i = 1}^{n}x_iy_i-\sum_{i = 1}^{n}x_i\sum_{i = 1}^{n}y_i}{n\sum_{i = 1}^{n}x_i^{2}-(\sum_{i = 1}^{n}x_i)^{2}}$ and $b_0=\bar{y}-b_1\bar{x}$, with $n$ being the number of data points, $x$ being the OTT variable and $y$ being the weight variable.

Step2: Calculate means

First, calculate $\bar{x}=\frac{\sum_{i = 1}^{n}x_i}{n}$ and $\bar{y}=\frac{\sum_{i = 1}^{n}y_i}{n}$.

Step3: Calculate sums

Compute $\sum_{i = 1}^{n}x_iy_i$, $\sum_{i = 1}^{n}x_i$, $\sum_{i = 1}^{n}y_i$ and $\sum_{i = 1}^{n}x_i^{2}$.

Step4: Find slope $b_1$

Substitute the sums and $n$ into the formula for $b_1$.

Step5: Find y - intercept $b_0$

Substitute $\bar{x}$, $\bar{y}$ and $b_1$ into the formula for $b_0$.

Step6: Write regression line

The regression line is $\hat{y}=b_0 + b_1x$. But since no data is given in the problem, we cannot calculate the actual values of $b_0$ and $b_1$.

Answer:

Since no data is provided, we cannot compute the least - squares regression line.