1. Homelessness is a problem in many large U.S. cities. To better understand the problem, a multiple regression was used to model the rate of homelessness based on several explanatory variables. The following data were collected for 50 large U.S. cities. The variables measured and results of the multiple regression are given below. Note that s is the residual standard error = 2.861. Homeless Poverty Unemployment Temperature Vacancy Rent Control number of homeless people per 10,000 in a city percent of residents with income under the poverty line percent of residents unemployed average yearly temperature (in degrees F.) percent of housing that is unoccupied a dummy variable, 1 = city has rent control, 0 = no rent control a. Write down the equation for the estimated multiple regression model. Include the response variable, predictors, and the numerical values of the intercept and partial slopes in the equation. b. Using a 5% level of significance, which variables are associated with the number of Homeless? c. Explain the meaning of the partial slope of Temperature in the context of this problem. Include names of predictors and response, the value of the partial slope, and correct units in the answer. d. Explain the meaning of the partial slope of Rent Control in the context of this problem. e. Do the results suggest that having Rent Control laws in a city causes higher levels of homelessness? Explain. f. If we created a new model by adding several more explanatory variables, which statistic should be used to compare it to this model, R2 or Adjusted R2 ? Explain. g. State 2 assumptions of the multiple regression model that can be checked using the plots below. For each assumption explain why the plot does or does not suggest the assumption is satisfied