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

question 2

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:

Overt Discrimination

Clear, open, and deliberate unequal treatment of individuals based on categories such as race, age, gender, or religion.

Subtle Discrimination

Discriminatory practices or behaviors that are not overt or blatant, often difficult to identify and address.

Work-related Outcomes

Consequences or results that stem from one's engagement in work activities, including job satisfaction, productivity, and performance levels.

Predominantly Feminine

Characterized by qualities or values culturally associated with women, such as empathy, cooperation, and nurturing.

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