![]() ![]() The cookie is used to store the user consent for the cookies in the category "Analytics". This cookie is set by GDPR Cookie Consent plugin. These cookies ensure basic functionalities and security features of the website, anonymously. Necessary cookies are absolutely essential for the website to function properly. If 0 ≤ |\(r\)| ≤ 0.2 the data points are in no correlation. If 0.2 < |\(r\)| ≤ 0.4 the data points are in weak correlation.ĥ. If 0.4 < |\(r\)| ≤ 0.7 the data points are in moderate correlation.Ĥ. If 0.7 < |\(r\)| ≤ 1 the data points are in strong correlation.ģ. The range of \(r\) is between -1 and 1, inclusive.Ģ. The correlation coefficient has the following characteristics:ġ. The solution to this system gives us the parameters \(a\) and \(b\): These lead to the set of two linear equations with two variables. The condition for the sum of the squares of the offsets to be a minimum is that the derivatives of this sum with respect to the approximating line parameters are to be zero. The linear least squares regression line method is a mathematical procedure for finding the best-fitting straight line to a given set of points by minimizing the sum of the squares of the offsets of the points from the approximating line. ![]() This equation has the form of a linear regression model, so we can apply a linear least squares method. Taking the natural log of both sides of the equation, we have the following equivalent equation: ![]() In particular, we consider the following exponential model: The exponential regression is a form of nonlinear regression analysis, in which observational data are modeled by an exponential function. ![]()
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