Explain the concept of Slopes and Elasticities for Nonlinear Regression Functions?

Data contains brain mass in different species versus glia-neuron ratio, the latter being a measurement of brain metabolism as the glia provides the metabolic needs of the neurons.  The relationship between THE LOGARITHM of the brain mass (in the third column) and Glia-neuron ratio (fourth column) appears linear and it is these two variables that we wish to analyze via linear regression.  We would like to know if the human brain fits the trend from the other species.  Towards this end we will perform the regression on all species EXCEPT humans (Homo sapiens).  Again, throw out the human data from your analysis.  You will however need the human numbers for some of the questions.    What is the coefficient of determination (R2) for the data? What is the standard error of the slope of the regression line (SEb) ?

A weight-loss clinic wants to use regression analysis to build a model for weight loss of a client (measured in pounds), Two variables thought to affect weight loss are client's length of time on the weight-loss program and time of session These variables are described below: Y-BO+B1'X+82'D 83'X'D+E Y-Weight loss (in pounds) X- Length of time in weight-loss program (in months) D-1 if morning session. O if not in terms of the Bs in the model, what is the difference between the weight loss of an individual who has spent 3 months in the program when attending the morning session, and an individual who has spent 2 months in the program when attending the evening session? OB1+83 OB1+82-83 OB1+82+283 O81+82+383

How does utilizing linear regression benefit or help a business?