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Homelessness Is a Problem in Many Large U FF ) Dependent Variable Is Homeless

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Essay

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
regression results appear below. Homeless number of homeless people per 10,000 in a city Poverty percent of residents with income under the poverty line Unemployment percent of residents unemployed Temperature average yearly temperature (in degrees FF .)
 Vacancy  percent of housing that is unoccupied  Rent Control indicator variable, 1= city has rent control, 0= no rent control \begin{array} { l l } \text { Vacancy } & \text { percent of housing that is unoccupied } \\ \text { Rent Control indicator variable, } 1 = \text { city has rent control, } 0 = \text { no rent control } \end{array}

Dependent variable is Homeless
R\mathrm { R } squared =38.4%R= 38.4 \% \quad \mathrm { R } squared (adjusted) =31.5%= 31.5 \%
s=2.861\mathrm { s } = 2.861 with 506=4450 - 6 = 44 degrees of freedom


 Variable  Coeff  SE(Coeff)  t-ratio  p-value  Constant 4.2753.4651.230.2239 Poverty 0.08230.08231.000.3228 Unemployment 0.1590.2180.730.4699 Temperature 0.1350.05872.300.0262 Vacancy 0.2470.1381.790.0809 Rent Control 2.9441.372.150.0373\begin{array} { l c c r l } \text { Variable } & \text { Coeff } & \text { SE(Coeff) } & \text { t-ratio } & \text { p-value } \\ \text { Constant } & - 4.275 & 3.465 & - 1.23 & 0.2239 \\ \text { Poverty } & 0.0823 & 0.0823 & 1.00 & 0.3228 \\ \text { Unemployment } & 0.159 & 0.218 & 0.73 & 0.4699 \\ \text { Temperature } & 0.135 & 0.0587 & 2.30 & 0.0262 \\ \text { Vacancy } & - 0.247 & 0.138 & - 1.79 & 0.0809 \\ \text { Rent Control } & 2.944 & 1.37 & 2.15 & 0.0373 \end{array}
a. Using a 5% level of significance, which variables are associated with the number of
homeless in a city?
b. Explain the meaning of the coefficient of temperature in the context of this problem.
c. Explain the meaning of the coefficient of rent control in the context of this problem.
d. Do the results suggest that having rent control laws in a city causes higher levels of
homelessness? Explain.
e. If we created a new model by adding several more explanatory variables, which statistic
should be used to compare them  - the R2 or the adjusted R2 ? Explain. \text { - the } R ^ { 2 } \text { or the adjusted } R ^ { 2 } \text { ? Explain. }
f. Using the plots below, check the regression conditions.  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 regression results appear below. Homeless number of homeless people per 10,000 in a city Poverty percent of residents with income under the poverty line Unemployment percent of residents unemployed Temperature average yearly temperature (in degrees  F .)  \begin{array} { l l } \text { Vacancy } & \text { percent of housing that is unoccupied } \\ \text { Rent Control indicator variable, } 1 = \text { city has rent control, } 0 = \text { no rent control } \end{array}   Dependent variable is Homeless  \mathrm { R }  squared  = 38.4 \% \quad \mathrm { R }  squared (adjusted)  = 31.5 \%   \mathrm { s } = 2.861  with  50 - 6 = 44  degrees of freedom    \begin{array} { l c c r l } \text { Variable } & \text { Coeff } & \text { SE(Coeff) } & \text { t-ratio } & \text { p-value } \\ \text { Constant } & - 4.275 & 3.465 & - 1.23 & 0.2239 \\ \text { Poverty } & 0.0823 & 0.0823 & 1.00 & 0.3228 \\ \text { Unemployment } & 0.159 & 0.218 & 0.73 & 0.4699 \\ \text { Temperature } & 0.135 & 0.0587 & 2.30 & 0.0262 \\ \text { Vacancy } & - 0.247 & 0.138 & - 1.79 & 0.0809 \\ \text { Rent Control } & 2.944 & 1.37 & 2.15 & 0.0373 \end{array}   a. Using a 5% level of significance, which variables are associated with the number of homeless in a city? b. Explain the meaning of the coefficient of temperature in the context of this problem. c. Explain the meaning of the coefficient of rent control in the context of this problem. d. Do the results suggest that having rent control laws in a city causes higher levels of homelessness? Explain. e. If we created a new model by adding several more explanatory variables, which statistic should be used to compare them  \text { - the } R ^ { 2 } \text { or the adjusted } R ^ { 2 } \text { ? Explain. }   f. Using the plots below, check the regression conditions.


Definitions:

Mutually Exclusive Projects

Investment projects where the acceptance of one project prevents the acceptance of another due to resource constraints or other factors.

Net Present Values

The gap between the present value of cash entering and the present value of cash exiting over a certain period, used within capital budgeting to gauge the financial viability of an investment.

Payback Period

The duration it takes for an investment to generate enough cash flow to recover its initial cost.

Average Accounting Returns

A financial ratio that measures a firm's average profitability relative to its investment, typically calculated as net income divided by average book value of investment.

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