Quantitative Analysis

Figure adapted from: Linear coefficient of coefficient of correlation between beta-cell mussiness and mannequin freight throughout the lifetime in Lewis bums: role of beta-cell hyperplasia and hypertrophy. E Montanya, V Nacher, M Biarnes and J Soler (Diabetes 49:1341-1346, 2000) Using the preceding(prenominal) map address the questions listed below:(A)For the cut-in represent: accede AND pardon whether the analog linkup render is a subscribe to acquaintance or an indirect/ rearward descent. What would be a potential range for a bianalogue correlation coefficient for this graph? explain your reasoning. If given the analog reverting equality [ lt;em>y = 0.016x + 3.2] which is in the form of y=mx+b: a) What does ?y form? b) What does 0.016 in this equation dally? c) What does 3.2 in this equation spiel? d) Using the inset graph and the above linear relapsing equation, calculate the predicted trunk clog of a rat if the important Cell Mass is 10.1 mg.. attend: The linear association represent in the graph shows a direct commanding blood between ?-cell jackpot and organic structure weight. That is, there pull up stakes greater ?-cell chew for excessive ashes weight. This healthful relationship suggests a credibly range for a linear correlation coefficient to be 0.9 ? 1.0 because the more(prenominal) closely the variables are associated the high the r value. Further, for the given linear regression equation y = 0.016x + 3.

2, the restricted variable ?y? represents the ?-cell stool in mg. The significant sum 0.016 represents the magnitude of the linear relationship between ?-cell mass and body weight. That is, the expected tag on in ?-cell mass for a one-unit change in body weight. The real number 3.2 in the equation is the value of ?-cell mass when body weight equals zero. Finally, if the ?-cell mass is 10.1 mg the predicted body weight of a rat go away be 3.36 g [= (0.016×10.1)... If you trust to get a adept essay, post it on our website: Ordercustompaper.com

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