The least squares regression line that best fits the data set, you typically need more information. Option 3 (y = -2.3x + 97.9) seems to be the most plausible least squares regression line based on the provided options, as it is in the correct form and has reasonable coefficients.
The general form of a least squares regression line is given by y = mx + b, where m is the slope of the line and b is the y-intercept.
In your provided options: 1. y = -10.13x + 118.8 2. g = -0.31x + 37.3 (It seems there might be a typo here, as it's written in terms of g instead of y.) 3. y = -2.3x + 97.9 4. y = -0.09% + 11.5 (It seems there might be a typo here, as it's written as a percentage instead of a variable.)
Option 3 (y = -2.3x + 97.9) seems to be the most plausible least squares regression line based on the provided options, as it is in the correct form and has reasonable coefficients. However, without the actual data points, it's not possible to definitively say which equation best fits the data. The best-fitting equation minimizes the sum of the squared differences between the observed and predicted values.
