Option D) .10, .30, .50 accurately represents Cohen's guidelines for interpreting the strength of a correlation coefficient.
Cohen's guidelines for interpreting the strength of a correlation coefficient, typically denoted as r, categorize small, medium, and large values based on the magnitude of the correlation. These guidelines help assess the strength and practical significance of the relationship between variables. Let's examine the options provided:
A) .46, .67, .84
B) .10, .20, .30
C) .00, .50, 1.00
D) .10, .30, .50
Among these options, the correct choice is D) .10, .30, .50.
According to Cohen's guidelines, small, medium, and large values of r correspond to approximately:
Small: r = 0.10
Medium: r = 0.30
Large: r = 0.50
Therefore, option D) .10, .30, .50 accurately represents Cohen's guidelines for interpreting the strength of a correlation coefficient.
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8. Consider the following regressions: + B3X3 + u Y=Bo + B1X₁ + B₂X₂ Y = 10 + 1X₁ + 72(X2X1)+73X3 Y=00 +0₁X₁ +0₂X2 + u +03 (X3 + X₁ - 2X₂) + u. (a) Rewrite the hypothesis 1 = 0 in te
If all three models produce straight lines when graphed, they are linear models
Rewrite hypothesis 1 = 0 in terms of the other two models.
Hypothesis 1 in the first model is:B3 = 0.
Hypothesis 1 in the second model is: B2 = 0.
Hypothesis 1 in the third model is: B1 + 2B2 + 3B3 = 0.
All three models are linear in the parameters because each explanatory variable has a coefficient attached to it. Linear regression is the process of fitting a linear equation to a given set of data points.
This equation can be used to predict the value of the dependent variable (Y) based on the value of the independent variable (X).
A linear equation is an equation that produces a straight line when graphed.
Therefore, if all three models produce straight lines when graphed, they are linear models
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