To find the linear equation represented by the given table of values, we can first identify the variables from the table. Let's denote \( x \) as the first column (which seems to be your independent variable) and \( y \) as the second column (your dependent variable).
From the table:
- When \( x = 0 \), \( y = 0 \)
- When \( x = 1 \), \( y = 2 \)
- When \( x = 2 \), \( y = 4 \)
Now, we can determine the linear equation in the form of \( y = mx + b \), where \( m \) is the slope and \( b \) is the y-intercept.
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Finding the slope (m): We can calculate the slope \( m \) using two points from the table, for instance, \( (0, 0) \) and \( (1, 2) \). \[ m = \frac{\Delta y}{\Delta x} = \frac{2 - 0}{1 - 0} = 2 \]
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Finding the y-intercept (b): From the table, we see that when \( x = 0 \), \( y = 0 \), which means the y-intercept \( b = 0 \).
Now we can write the equation based on the slope and y-intercept: \[ y = 2x + 0 \quad \text{or simply} \quad y = 2x \]
However, notice that there are two values \( [yb] \) in the table which influence the interpretation. Given this setup, it seems additional context might define the second value. To confirm an overall view, if you'd like to analyze the relationship between multiple outputs (like \( ya \) and \( yb \)), you might consider:
- \( ya \) values
- When \( x = 0 \), \( ya = 0 \)
- When \( x = 1 \), \( ya = 2 \)
- When \( x = 2 \), \( ya = 4 \)
This directly corresponds with our previously evaluated \( y = 2x \).
- Regarding \( yb \): The table shows \( y = 3, 3.5, 4 \) against each x. The relationship seems to vary. To infer, you may create a second equation analyzing \( yb \).
However, if the focus rests on one line, we conclude based on \( ya \) that the linear equation is: \[ \boxed{y = 2x} \]