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Linear regression is a statistical model that allows solving a variety of situations, determining the degree of dependence of the variable Y from values observed in the independent variable X; utilizing the linear equation. The corresponding linear equation for the example is: y = 1.5511x – 7.0648 The slope in the scatter diagram shows that it is positive, which in advance tells me that the value of r will be positive. To determine if there is an exact linear relationship between the data, the correlation coefficient is used, in this example the result of this coefficient is: r = 0.8778 According to the result obtained in the exercise it can be concluded that the correlation between the variables is positive since its value is close to 1 and therefore the linear regression model is used to make forecasts. Therefore, based on the exercise of 10-year-old children, it can be concluded that the greater the stature, the greater the weight. To finalize and verify the relationship of the data, the coefficient of determination will be used, which measures the proportion of total variability of the dependent variable (y) with respect to its average. It is usual to express this measure as a percentage. For the exercise, the coefficient of determination is: r2 = 0.7705 = 77.05% According to the result, it can be concluded that there is a linear relationship, that is, the data is closer to a straight line that represents the whole set of points with 75.05% accuracy. A scenario in which this application could be used would be in a school or in a pediatric office, where it would be used to control the weight of children and avoid early obesity.
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