APPLICATION OF SUPERVISED LEARNING
LINEAR REGRESSION
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Question
[CLICK ON ANY CHOICE TO KNOW THE RIGHT ANSWER]
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Data set A and data set B are both not linear.
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Data set A is more linear than data set B.
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Data set B is more linear than data set A.
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Data set A and data set B are equally linear.
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It cannot be determined which data set is more linear without additional information.
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Detailed explanation-1: -If a correlation coefficient is 0.95 that means The relationship between two variables are strong and positive O The relationship between two variables are weak O The relationship between two variables are strong and negative O This is not a value that a correlation coefficient can take Clear my choice.
Detailed explanation-2: -Complete correlation between two variables is expressed by either + 1 or-1. When one variable increases as the other increases the correlation is positive; when one decreases as the other increases it is negative. Complete absence of correlation is represented by 0.
Detailed explanation-3: -Pearson correlation (r), which measures a linear dependence between two variables (x and y). It’s also known as a parametric correlation test because it depends to the distribution of the data. It can be used only when x and y are from normal distribution. The plot of y = f(x) is named the linear regression curve.