Quantitative Analysis

Figure adapted from: Linear coefficient of correlation amid beta-cell draw and form cargo throughout the lifespan in Lewis bums: situation of beta-cell hyperplasia and hypertrophy. E Montanya, V Nacher, M Biarnes and J Soler (Diabetes 49:1341-1346, 2000) development the preceding(prenominal) chart address the questions listed below:(A)For the inset interpret: infix AND explain whether the analog linkup depicted is a call for to railroad tie or an indirect/inverse descent. What would be a likely range for a running(a) correlation coefficient for this graph? Explain your reasoning. If given the linear regression equality [ 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 par represent? c) What does 3.2 in this equation represent? d) Using the inset graph and the above linear regression equation, calculate the predicted trunk weight of a backside if the Beta Cell Mass is 10.1 mg.. Answer: The linea r association depicted in the graph shows a direct peremptory relationship between ?-cell upsurge and ashes weight. That is, there perpet shope up stakes greater ?-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 argon associated the higher the r value. Further, for the given linear regression equation y = 0.016x + 3.

2, the restricted variable ?y? represents the ?-cell mass in mg. The real sum 0.016 represents the magnitude of the linear relationship between ?-cell mass and body wei ght. That is, the expected interchange 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 will be 3.36 g [= (0.016×10.1)... If you want to get a full essay, post it on our website: OrderCustomPaper.com

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