#Mental Health in Urban Areas
Group 1: Ysabel Gamon, Khyle Capulong, Logan Salcido, Samantha Magana
We will be creating a multiple regression on our results from our survery on mental health in urban areas
# Read the CSV file
data <- read.csv("mktg4000.surveryresults.csv")
# Run multiple regression
model <- lm(data, data = data)
summary(model)##
## Call:
## lm(formula = data, data = data)
##
## Residuals:
## Min 1Q Median 3Q Max
## -0.68751 -0.31161 -0.01294 0.18297 1.07993
##
## Coefficients: (1 not defined because of singularities)
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) -15.62586 4.80135 -3.254 0.01397 *
## negative_feelings -0.03643 0.26447 -0.138 0.89431
## satisfaction_living_conditions -0.67364 0.20727 -3.250 0.01406 *
## frequency_noise_disturbances -0.48769 0.38579 -1.264 0.24664
## mental_resources -0.28630 0.18343 -1.561 0.16253
## safety NA NA NA NA
## city_anxiety 4.98342 1.32816 3.752 0.00715 **
## support -0.21471 0.31831 -0.675 0.52163
## personalization 0.31746 0.44861 0.708 0.50204
## device.access 0.30950 0.49416 0.626 0.55097
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Residual standard error: 0.6885 on 7 degrees of freedom
## (1 observation deleted due to missingness)
## Multiple R-squared: 0.815, Adjusted R-squared: 0.6036
## F-statistic: 3.855 on 8 and 7 DF, p-value: 0.04601# Plot diagnostic graphs
par(mfrow = c(2, 2))
plot(model)## Warning: not plotting observations with leverage one:
## 14
#Interpreting Results: R-squared: 0.815 Good Model Fit Adjusted R-Squared: 0.604 Strong model when accounting for multiple predictors P-Value: 0.046 Statistically significant overall model
#Significant Predictors: Satisfaction(living conditions): -0.674 p-value 0.014 indicates significant negative effect City Anxiety: 4.983 p-value: 0.007 indicating strong positive predictor of worse mental health
#Conclusion: Satisfaction with living conditions and city anxiety are signifacant predictors when it comes to mental health in urban areas.