ŷ = b0 + b1 * x

In this formula:

ŷ stands for the predicted value of the dependent variable

b0 signifies the y-intercept of the regression line

b1 represents the slope of the regression line

x denotes the value of the independent variable

ŷ = b0 + b1 * x1 + b2 * x2 +... + bn * xn

Within this equation:

ŷ stands for the predicted value of the dependent variable

b0 represents the y-intercept of the regression plane

b1, b2,..., bn denote coefficients assigned to each independent variable

x1, x2,..., xn signify values attributed to the independent variables

Residuals: These represent the discrepancies between observed values (y) and predicted values (ŷ), termed as residuals. Models with smaller residuals generally indicate a superior fit.

Coefficient of Determination (R²) : R² provides a measure of the total variation in the dependent variable explained by the regression model. A higher R² value is indicative of a better fit.

The number of deaths in Canada fluctuates annually, with various factors such as age, gender, geography, and cause of death significantly impacting the statistics.

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