Quantitative Analysis

Figure adapted from: Linear coefficient of correlation surrounded by beta-cell nap and form metric weight unit throughout the lifespan in Lewis bums: mathematical function of beta-cell hyperplasia and hypertrophy. E Montanya, V Nacher, M Biarnes and J Soler (Diabetes 49:1341-1346, 2000) apply the preceding(prenominal) chart address the questions listed below:(A)For the inset graph: harmonize AND explain whether the analog linkup depicted is a bring to companionship or an indirect/inverse descent. What would be a likely range for a analog 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 compargon represent? c) What does 3.2 in this equality represent? d) Using the inset graph and the above linear regression equation, calculate the predicted trunk weight of a bewray if the Beta Cell Mass is 10 .1 mg.. Answer: The linear association depicted in the graph shows a direct arbitrary relationship between ?-cell clutch and trunk weight. That is, there hook 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 are 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 weight. That is, the expected interchange in ?-cell mass for a one-unit change in body weight. The real quash 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 prat will be 3.36 g [= (0.016×10.1)... If you want to get a f ull essay, post it on our website: OrderCustomPaper.com

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