Explain what each point on theâ least-squares regression line represents.

The equation of the resulting line y = b₀ +b₁ x

The resulting line with intercept b₀ and slope b1 is called the least squares regression.

Step-by-step explanation:

Explanation:-

In regression analysis, the variable that we are trying to describe or predict defines the vertical y-axis is called the response. The Association variable goes on the horizontal x-axis and is called the explanatory variable or predictor.Explanatory variables are Factors , covariates, or even independent variables.In regression analysis The symbol Y denotes the response and The symbol X denotes the explanatory variableFor example diamonds cost depends on weight cost is the response and weight is the  explanatory variable

Regression analysis can produce various types of equations. The most common equation is line. Before we fit a line ,we need to inspect the scatterplot of the data to see that Association between the Variables is linear.

The equation of the resulting line y = b₀ +b₁ xEstimated cost = b₀ +b₁ weight

least squares

It remains to choose b₀ and b₁. unless every point lie on a single line( in which the correlation r=-1 and r=1)

we have to decide the best fit line to be close to the data, but the several ways to measure the distance from a point to a  line .we could use horizontal distance, the perpendicular distance, or the vertical distance as sketched in graph.

we choose to measure the distance of each data point to the line vertically because we use the fitted line to predict the value y from x. The vertical distance is called the error of prediction.The vertical deviations from the data points to the line are called residuals.The residue formula e = y-y⁻ = y - b₀ -b₁ x

The resulting line with intercept b₀ and slope b1 is called the

least squares regression.

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21, i'm pretty sure it's right lol