Figure adapted from: Linear correlation between beta-cell mass and body weight throughout the lifespan in Lewis rats: role of beta-cell hyperplasia and hypertrophy. E Montanya, V Nacher, M Biarnes and J Soler (Diabetes 49:1341-1346, 2000) Using the above graph address the questions listed below:(A)For the inset graph: State AND explain whether the linear association depicted is a direct association or an indirect/inverse relationship. What would be a likely range for a linear correlation coefficient for this graph? Explain your reasoning. If given the linear regression equation [ lt;em>y = 0.016x + 3.2] which is in the form of y=mx+b: a) What does 'y' represent? b) What does 0.016 in this equation represent? c) What does 3.2 in this equation represent? d) Using the inset graph and the above linear regression equation, calculate the predicted body weight of a rat if the Beta Cell Mass is 10.1 mg..

Answer: The linear association depicted in the graph shows a direct positive relationship between ÃÂ²-cell mass and body weight.

That is, there will greater ÃÂ²-cell mass for excessive body weight. This strong relationship suggests a likely range for a linear correlation coefficient to be 0.9 - 1.0 because the more closely the variables are associated the higher the r value. Further, for the given linear regression equation y = 0.016x + 3.2, the dependent variable 'y' represents the ÃÂ²-cell mass in mg. The real number 0.016 represents the magnitude of the linear relationship between ÃÂ²-cell mass and body weight. That is, the expected change in ÃÂ²-cell mass for a one-unit change in body weight. The real number 3.2 in the equation is the value of ÃÂ²-cell mass when body weight equals zero. Finally, if the ÃÂ²-cell mass is 10.1 mg the predicted body weight of a rat will be 3.36 g [= (0.016ÃÂ10.1)...