Introduction:
Access to essential services, such as water, sanitation, and healthcare, is crucial for human well-being and development. Within these services, the classification of service types, particularly whether they are deemed "Safely managed" or not, holds significant importance for ensuring public health and safety. In this analysis, we investigate the factors influencing the classification of service types using logistic regression modeling. Specifically, I explore how geographical region, type of residence, and service coverage contribute to the likelihood of a service being classified as "Safely managed." Understanding these factors can provide valuable insights for policymakers, planners, and organizations striving to improve service delivery and address disparities in access.
Selecting the Binary Response Variable:
In this task, I selected the "Service.Type" column as the binary response variable, and converted it into a binary variable indicating whether the service type is "Safely managed service" or not. This variable serves as the target variable for logistic regression modeling.
Insight:
By choosing this variable, I aim to understand the factors influencing whether a service is classified as "Safely managed" or not.
Significance:
Understanding the determinants of "Safely managed" services can help policymakers and organizations focus their efforts on improving service delivery where it is most needed.
Further Questions:
Are there specific regions or types of residences more likely to have "Safely managed" services? What role does coverage play in determining service type classification?
Selecting Explanatory Variables:
I selected three explanatory variables: Region, Residence.Type, and Coverage. These variables represent different aspects that might influence the classification of service type.
Insight:
I interested in understanding how geographical region, type of residence, and service coverage contribute to the likelihood of a service being classified as "Safely managed.
Significance:
These explanatory variables capture various dimensions that could affect the quality and accessibility of services, providing insights into potential disparities or patterns.
Further Questions:
Are there other variables, such as economic factors or infrastructure development, that could also influence service classification? How do these selected variables interact with each other?
Building the Logistic Regression Model:
I built a logistic regression model using the binary response variable and the selected explanatory variabl.
Insight:
The model helps quantify the relationship between the explanatory variables and the likelihood of a service being classified as "Safely managed".
Significance:
Understanding the model coefficients allows us to identify which variables have a significant impact on service classification and their direction of influence.
Further Questions:
Are there multicollinearity issues among the explanatory variables? How well does the model fit the data, and are there any areas for improvement?
Interpreting Model Coefficients:
I examined the coefficients of the logistic regression model to understand the direction and strength of the relationship between the explanatory variables and the response variable.
Insight:
Positive coefficients indicate an increase in the log odds of the response variable, while negative coefficients suggest a decrease.
Significance:
Interpreting coefficients helps identify which variables are positively or negatively associated with "Safely managed" service classification, aiding in understanding the drivers behind service quality.
Further Questions:
What are the practical implications of these coefficient estimates? How do they align with existing knowledge or hypotheses?
Calculating Confidence Interval for a Coefficient:
I calculated a confidence interval for the coefficient of Coverage to assess the precision of its estimate.
Insight:
The confidence interval provides a range of values within which we are reasonably confident the true coefficient lies.
Significance:
A narrower confidence interval indicates greater precision and reliability of the coefficient estimate, while a wider interval suggests more uncertainty.
Further Questions:
How does the width of the confidence interval affect the interpretation of the coefficient estimate?
Are there any outliers or influential data points affecting the precision of the estimate?
Conclusion:
Through our logistic regression analysis, I have gained valuable insights into the factors influencing the classification of service types. My findings suggest that geographical region, type of residence, and service coverage play significant roles in determining whether a service is classified as "Safely managed." Positive coefficients indicate factors that increase the likelihood of a service being classified as such, while negative coefficients suggest factors that decrease this likelihood.
Furthermore, The confidence interval analysis highlights the precision of our coefficient estimates, providing confidence in our model's reliability. Overall, this analysis provides a foundational understanding of the drivers behind service type classification, paving the way for targeted interventions and policies aimed at improving service quality and accessibility for all.
# Read the CSV file data <- read.csv("C:\\Users\\am790\\Downloads\\washdash-download (1).csv") # Task 1: Select the binary response variable based on whether the service type is "Safely managed service" or not. data$Binary_Service <- as.integer(data$Service.Type == "Safely managed service") # Task 2: Select explanatory variables (let's choose Region, Residence.Type, and Coverage) explanatory_variables <- c("Region", "Residence.Type", "Coverage") # Task 3: Convert categorical variables into factors if needed (assuming they are already factors) # Task 4: Build logistic regression model model <- glm(Binary_Service ~ Region + Residence.Type + Coverage, data = data, family = binomial)
## Warning: glm.fit: algorithm did not converge
# Task 5: Interpret coefficients summary(model)
## ## Call: ## glm(formula = Binary_Service ~ Region + Residence.Type + Coverage, ## family = binomial, data = data) ## ## Coefficients: ## Estimate Std. Error z value Pr(>|z|) ## (Intercept) -2.657e+01 2.606e+04 -0.001 0.999 ## RegionCentral and Southern Asia 8.003e-13 2.823e+04 0.000 1.000 ## RegionEastern and South-Eastern Asia 7.936e-13 2.864e+04 0.000 1.000 ## RegionEurope and Northern America 7.971e-13 2.954e+04 0.000 1.000 ## RegionLatin America and the Caribbean 7.931e-13 2.932e+04 0.000 1.000 ## RegionNorthern Africa and Western Asia 7.971e-13 2.883e+04 0.000 1.000 ## RegionOceania 7.889e-13 2.917e+04 0.000 1.000 ## RegionSub-Saharan Africa 7.970e-13 2.829e+04 0.000 1.000 ## Residence.Typetotal -1.281e-13 1.504e+04 0.000 1.000 ## Residence.Typeurban 8.155e-15 1.517e+04 0.000 1.000 ## Coverage 1.524e-15 2.379e+02 0.000 1.000 ## ## (Dispersion parameter for binomial family taken to be 1) ## ## Null deviance: 0.0000e+00 on 3366 degrees of freedom ## Residual deviance: 1.9534e-08 on 3356 degrees of freedom ## AIC: 22 ## ## Number of Fisher Scoring iterations: 25
# Task 6: Calculate Confidence Interval for a coefficient (e.g., Coverage) coef_summary <- summary(model)$coefficients coef_index <- match("Coverage", rownames(coef_summary)) coef <- coef_summary[coef_index, "Estimate"] std_err <- coef_summary[coef_index, "Std. Error"] # For a 95% confidence interval z_value <- qnorm(0.975) margin_of_error <- z_value * std_err lower_bound <- coef - margin_of_error upper_bound <- coef + margin_of_error # Task 7: Translate the meaning of the CI cat("The 95% confidence interval for the coefficient of Coverage is [", round(lower_bound, 3), ", ", round(upper_bound, 3), "].\n")
## The 95% confidence interval for the coefficient of Coverage is [ -466.258 , 466.258 ].
cat("This means that we are 95% confident that the true value of the coefficient for Coverage falls within this interval.\n")
## This means that we are 95% confident that the true value of the coefficient for Coverage falls within this interval.