Can Highway Builders Predict Pavement Strength Using Regression Lines?

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Highway builders utilize regression lines to predict concrete pavement strength after 28 days based on 7-day strength measurements. The regression equation is y-hat = 1389 + 0.96x, where x represents strength after 7 days in pounds per square inch. A graph should be created with the x-axis labeled for strength (3000 to 4000 psi) and the y-axis for predicted strength after 28 days. There was confusion regarding graphing the intercept, as it was mistakenly labeled with days instead of strength. The issue was resolved by the user, indicating successful understanding of the graphing process.
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Concrete road pavement gains strength over time as it cures. Highway builders use regression lines to predict the strength after 28 days (when curving is complete) from measurements made after 7 days. Let x be strenth after 7 days (in pounds per square inch) and y the strength after 28 days.

y-hat = 1389 + .96x

Draw a graph of this line, with x running from 3000 to 4000 lbs per square inch.

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I don't know how to graph it because the words are confusing me . . . I labed my explanatory (x-axis) as psi from 3000 to 4000 lbs and the response variable (y-axis) as days. How do I graph 1389 (the intercept) on my graph if the y-axis is days?
 
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I solved it, thanks anyway
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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