In a study relating college grade point average to time spent in various activities, you distribute a survey to several students. The students are asked how many hours they spend each week in four activities: studying, sleeping, working, and leisure. Any activity is put into one of the four categories, so that for each student the sum of hours in the four activities must be 168. a) Consider the model GPA = β0 + β1study + β2work + β3leisure + β4sleep + u. Interpret the coefficient β1. Does it make sense to hold work, leisure, and sleep fixed, while changing study? b) Explain why this model violates Assumption MLR.3. c) How could you reformulate the model so that its parameters have a useful interpretation and it satisfies Assumption MLR.3?

Step-by-step explanation:

Given

GPA = NO + B1Study + B2work + B3leisure + B4sleep + u

1. No it makes no sense. There is an intercorellation between these explanatory variables. Changing study while holding the rest variables constant or fixed would not make sense. If study us changed then one or more of the other predictors should be changed so that they can still be equal to 168

2. Study is a perfect linear function of work, leisure and sleep. There is the problem of multicollinearity among these independent variables. One assumption of the MLR is no multicollinearity.

3. To reformulate model, you have to drop the variable that is having the issue. Let's say that leisure is dropped from the model. After this the assumption is satisfied.

GPA = b0 + B1Study + b2sleep + b3work + u

chemists are often responsible for designing a chemical reaction and analyzing the products obtained from it. one significant part of designing a chemical reaction is determining the amount of each reactant that is needed and the amount of each product that will be produced. in order to do so, chemists use stoichiometry, the quantitative relationships between reactants and products.