Finding the Coefficient of Determination
Use your third team member's correlation value to calculate the coefficient of determination. Explain what this value tells you about your model in terms of "explained variance." Explain what this value tells you about your model in terms of "unexplained variance." Should you use the word "cause" in your explanation – why or why not? In your response, change the subject line to "Your Name – Task 4."
TASK #3 =
The relationship of the tall buildings scatterplot is one of a weak positive correlation. It is a linear form and is in a positive direction. The calculated correlation coefficient is 0.69. Since it is close to 0.70 I would say that there is a strong relationship between the variable in column A and column B. I think the team should continue with the creation of a linear model for prediction.
Task 4
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A statistical model's ability to accurately predict an event is measured by a metric called the coefficient of determination (R2 ). The dependent variable in the model is a representation of the outcome. R2 can take any value between 0 and 1, with 0 being the absolute minimum and 1 being the absolute maximum. To put it another way, a model's R2 value will be closer to 1 if it is more accurate at predicting predictions.
We square the coefficient and convert it to a percentage to find the coefficient of determination.
From task 3:
Coefficient = 0.69
R2 =0.692
0.4761 x 100
47.61%
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