Introductions

App Name: Simple MPG Predictor Goal: Provide a quick, interactive tool to estimate a car’s fuel efficiency based on its engine power.

Technologies: Shiny, R Markdown

Link to App: https://nthd65.shinyapps.io/shiny_pitch_project/

The Challenge and The Solution

Challenge: Estimating Fuel Efficiency In automotive analysis, quickly assessing the MPG-HP trade-off is crucial. Our application addresses this by providing an immediate, data-driven estimate.

Our Solution: A Simple Predictive Model We use the classic mtcars dataset and a Linear Regression Model to establish the relationship: \[MPG \approx \beta_0 + \beta_1 \times HP\]

Model Verification

Core Model Statistics The application’s logic is based on this simple linear model. Here are the key coefficients from the R code evaluated during the presentation generation process:

##                Estimate Std. Error   t value     Pr(>|t|)
## (Intercept) 30.09886054  1.6339210 18.421246 6.642736e-18
## hp          -0.06822828  0.0101193 -6.742389 1.787835e-07

Interpretation (Intercept): The estimated MPG when HP is zero (not practically meaningful, but essential for the model).

hp: The negative coefficient (-0.0682) shows that for every 1 unit increase in HP, MPG decreases by about 0.0682 units.

Application Features

Key Features of the App Input Widget: A slider for smooth, intuitive selection of Horsepower. (Meets requirement for widget input)

Reactive Calculation: The prediction is updated instantly using the R model in server.R. (Meets requirement for calculation)

Visualization: An interactive plot displays the data and highlights the prediction point in red. (Meets requirement for reactive output)

App Screenshot

Next Steps & Conclusion

Conclusion The Shiny application successfully demonstrates the ability to translate a simple statistical model into a practical, interactive web tool.

Next Steps Integrate more predictors (e.g., Weight, Displacement) for multi-variable prediction.

Add a visual output displaying the Confidence Interval for the prediction to show uncertainty.