R is equal to the Correlation Coefficient of the graph, this can be calculated using the classpad, and gives a concrete answer to the type of Correlation as seen below.
Types of Relationships
| Value of r | What it means |
|---|---|
| r = 1 | Perfect Positive Correlation |
| 0.7 < r < 1 | Strong Positive Correlation |
| 0.5 < r ≤ 0.7 | Moderate Positive Correlation |
| 0.3 < r ≤ 0.5 | Weak Positive Correlation |
| 0 ≤ r ≤ 0.3 | No Significant Correlation |
| r = 0 | No Linear Correlation |
| -0.3 < r ≤ -0 | No Significant Correlation |
| -0.5 ≤ r < -0.3 | Weak Negative Correlation |
| -0.7 < r < -0.5 | Moderate Negative Correlation |
| -1 < r < -0.7 | Strong Negative Correlation |
| r = 1 | Perfect Negative Correlation |
Examples
Direction Of Linear Relationships
If the value of ”R” is positive, then the Linear relationship is going for Left to right, increasing as it gets further to the right. As The Explanatory Variable Increases so does the Response Variable. When the value of ”R” is negative, then the Linear Relationship graph goes from Left to Right, but the Explanatory Variable and Response Variable Decrease the further to the right they go.
Determining things about the linear model
The linear model is appropriate if:
- The Residual plot does not have a clear apttern
- The scatterplot has a linear form
- If the association is strong or at least moderate (look at the scatterplot Pearson’s Correlation Coefficient)
The linear mode lis reliable if:
- If the predication is interpolated, not extrapolated.
- If the Coefficient Of Determination is high, that means that x percent of the variation in {Response Variable}, can be explained by the variation in {Explanatory Variable}
- If a linear model is appropriate
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