*Happiness* (***): Using the data set `happy`

, generate an ordered logit regression model that regresses the dependent variable \(happiness\) on those variables that have the strongest potential causal relationship. For your model, interpret the R output and indicate why each independent variable that is included in the model would contribute to higher or lower happiness. Speak to the possibility of multicollinearity in the independent variables.

*AFV Choice* (***): The data `evdata`

contains data about the choice of consumers with respect to alternative fuel vehicles. For each consumer, you have the following variables: \(age\), \(suv\) (whether they are interested in buying a SUV), \(level2\) (indicating whether people have a fast charger for electric cars in their community), \(own \dots\) (indicating whether the respondent currently has a gas, hybrid, plug- in hybrid, or battery electric vehicle), \(gender\) (1=female) and \(numcars\) (number of cars). Estimate a multinomial logit model that estimates the probability of a consumer to purchase a gasoline, hybrid, plug-in hybrid, or battery electric vehicle. Calculate the marginal probabilities as well.

*NHTS* (***): Consider the data set `hhpub`

from the 2017 National Household Travel Survey (NHTS). The data contains information about household characteristics and some of their travel means. For this question, you will focus on the following variables: \(bike\), \(hbppopdn\), \(hhfaminc\), \(hhvehcnt\), \(homeown\), and \(urbrur\). You must read the codebook for this question and learn how the variables are coded. Go to the codebook and pick “Household” as the dataset (drop down menu). Before conducting the analysis, delete all entries
that are not complete (i.e., all the negative values). Once you have final data set, estimate
an ordered logit model with \(BIKE\) as the dependent variable and the other variables as the
independent variables. What do you conclude from the model?

*Home Heating* (***): Consider the dataset `Heating`

from the You can load the data set into R by typing: `data("Heating",package="mlogit")`

. The data contains the choice of heating systems in California homes. Estimate a multinomial logit model with installation and operating cost as the alternative-specific variables and income, age, and number of rooms as the individual-specific variables.