R Markdown - A tall white fountain played

#For his model, we are going to look into the food insecure population of DC. We want to see how factors such as unemployment, poverty, home ownership affect their situation. In the report, there doesn’t appear to be any other conditional factors such as wages, health conditions, cost of living to help determine what causes food insecurity in these tracts.

options (scipen = 100)
library(readxl)
library(tidyverse)
## ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
## ✔ dplyr     1.1.4     ✔ readr     2.1.5
## ✔ forcats   1.0.0     ✔ stringr   1.5.1
## ✔ ggplot2   4.0.0     ✔ tibble    3.3.0
## ✔ lubridate 1.9.4     ✔ tidyr     1.3.1
## ✔ purrr     1.1.0     
## ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
## ✖ dplyr::filter() masks stats::filter()
## ✖ dplyr::lag()    masks stats::lag()
## ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
CAFB_SetUp <- read.csv("Capital_Area_Food_Bank_Hunger_Estimates.csv")
CAFB_Condition_Report <- lm(F15_FI_POP ~ UNEMPLOYME+POVERTY_RA+HOME_OWN, data = CAFB_SetUp)
summary(CAFB_Condition_Report)
## 
## Call:
## lm(formula = F15_FI_POP ~ UNEMPLOYME + POVERTY_RA + HOME_OWN, 
##     data = CAFB_SetUp)
## 
## Residuals:
##     Min      1Q  Median      3Q     Max 
## -866.33 -133.04  -39.22  117.72 1031.11 
## 
## Coefficients:
##             Estimate Std. Error t value            Pr(>|t|)    
## (Intercept)   482.37      27.53  17.521 <0.0000000000000002 ***
## UNEMPLOYME   2965.30     160.93  18.426 <0.0000000000000002 ***
## POVERTY_RA    118.01     122.32   0.965               0.335    
## HOME_OWN     -466.63      33.08 -14.107 <0.0000000000000002 ***
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 212.8 on 1035 degrees of freedom
## Multiple R-squared:  0.5703, Adjusted R-squared:  0.569 
## F-statistic: 457.8 on 3 and 1035 DF,  p-value: < 0.00000000000000022

#After summarizing the model, the Multiple R-squared reports a 57.03% of representing the actual outcome. The p-value for this is reporting a margin of error of 2.2x10^-11. This gives the impression that the model can adequately determine the actual values to suggest what causes food insecurity. In this model we look at three factors: Unemployment, poverty rate, and home ownership. Unimportant to the model is the geography, the sku codes used for tracts

#For this model, I beleive unemployement plays a larger factor in determing food insecurity in a tract. Comparative to the other variables, unemployment has an estimate standard deviation of -3105.7 with a P value of 7.94 x 10^-3. What this indicates is that when the population increases, the likeliness of unemployment increases exponentially. When lookin at each variable seperately, It’s easy to see how unemployment, poverty rate, and even owning a home can affect food insecurity. As stated previously, we do not have other conditional factors such as the lowest common income information, nor health conditions,

plot(CAFB_Condition_Report, which = 1)

#I think the model does meet the assumption of linearity for the most part. A large portion of the variable measured can be found at the beginning of the line, but petters out as the line moves further dowm the graph.