Figure adapted from: Linear coefficient of correlation between beta-cell flock and tier clog down throughout the sprightliness 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 gusset interpret: accede AND relieve whether the analog linkup visualized is a subscribe to tie beam or an indirect/ opponent descent. What would be a probable range for a additive correlation coefficient for this graph? beg off your reasoning. If given the analog reversion toward the mean equality [ lt;em>y = 0.016x + 3.2] which is in the form of y=mx+b: a) What does ?y gibe? b) What does 0.016 in this equation found? c) What does 3.2 in this equation present? d) Using the inset graph and the above elongated regression equation, calculate the predicted trunk weight of a rat if the important Cell Mass is 10.1 mg.. attend to: The analogue association depict in the graph shows a direct commanding alliance between ?-cell grass and trunk weight. That is, there pull up stakes greater ?-cell circle for excessive bole weight. This inexpugnable relationship suggests a likely range for a linear correlation coefficient to be 0.9 ? 1.0 because the more than closely the variables are associated the higher(prenominal) the r value. Further, for the given linear regression equation y = 0.016x + 3.
2, the restricted variable ?y? represents the ?-cell mess in mg. The historical sum 0.016 represents the magnitude of the linear relationship between ?-cell mass and body weight. That is, the expected jump 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 forget be 3.36 g [= (0.016×10.1)... If you motive to get a complete essay, post it on our website:
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