To complete the statements, we will need some assumptions and standard values usually seen in these contexts. Since you have not provided the specific values of the slope, y-intercept, or the correlation coefficient, I will use placeholder variables.
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The equation for the line of best fit is \[ y = mx + b \] where \( m \) is the slope and \( b \) is the y-intercept. You should replace \( m \) and \( b \) with the actual values from Jacqueline's study.
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Using the model, the average number of hours of incubation of a chicken that weighs 25 ounces is approximately ___ hours. To find the average number of hours of incubation for a chicken that weighs 25 ounces, you would substitute \( y = 25 \) into the equation. Assuming you have the equation: \[ 25 = mx + b \] Rearranging to solve for \( x \): \[ x = \frac{25 - b}{m} \] You would need to replace \( 25 \), \( b \), and \( m \) with the appropriate values to find \( x \).
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The correlation coefficient suggests there is a __ association between the number of weeks gestation and the weight of a baby chicken. If the correlation coefficient (let’s say it's \( r \)) is close to 1 or -1, it suggests a strong linear association; if it’s close to 0, it suggests a weak association. If \( r \) is positive, it indicates a positive relationship, while a negative \( r \) indicates a negative relationship. So you might say:
- If \( r > 0 \) and is close to 1, then: "The correlation coefficient suggests there is a strong positive association."
- If \( r < 0 \) and is close to -1, then: "The correlation coefficient suggests there is a strong negative association."
- If \( |r| \) is close to 0, then: "The correlation coefficient suggests there is a weak association."
Make sure to input and adjust these based on Jacqueline's actual data.