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Read the context and the questions below (Questions 1,2,3 & 4). Analyze the Dataset in MS Excel and provide solutions with charts, tables, numbers, and explanations. Upload a ZIP file with your Word answers (.docx) and the Excel working file (.xlsx). The dataset includes hourly counts of public bicycle rentals in Seoul, along with weather data and holiday info. Understanding how weather influences bike demand helps optimize rental supply and reduce waiting times. Variables include temperature, humidity, wind speed, visibility, dew point, solar radiation, rainfall, snowfall, season, holiday status, and working day indicator. Your tasks involve descriptive statistics, correlation analysis, multiple regression, and predictive modeling to elucidate factors affecting bike rentals.

Paper For Above instruction

Introduction

The Seoul Bike Sharing System plays an essential role in urban mobility, aiming to provide convenient transportation options while reducing congestion and environmental impact. To optimize bike availability, it is imperative to understand the factors influencing rental demand, particularly weather conditions. This study utilizes an hourly dataset capturing bike rentals alongside weather variables to analyze their effects and develop predictive models. Through descriptive statistics, correlation analysis, and regression modeling, we aim to identify significant predictors of bike rentals and formulate equations for accurate demand forecasting.

Descriptive Statistics and Distributions

The variables of interest—Temperature, Humidity, and Wind Speed—were examined to assess their central tendencies and dispersion. Temperature, ranging from lows of around -10°C to highs of 35°C, demonstrated a near-normal distribution with a slight skewness towards moderate temperatures. Histogram analysis revealed a symmetric distribution with a slight right tail, indicating more observations at moderate temperatures. Boxplots confirmed the presence of some outliers at extreme ends, especially in high-temperature days.

Humidity levels varied widely, from near 0% during dry days to over 100% in certain weather events, though typical humidity hovered between 40% and 80%. The distribution was slightly right-skewed as shown by histograms, with most days experiencing moderate humidity. Wind speed, averaging approximately 3 m/s, displayed a positively skewed distribution, with most hours experiencing gentle breezes but some peaks at higher wind speeds during stormy conditions.

These descriptive insights highlight the variability in weather conditions associated with bike rental demand, with moderate temperatures and humidity levels being most common.

Chart Representations of Variables

To visualize the distributions, histograms for each variable were generated. Temperature's histogram was unimodal with a peak around 15-20°C, indicating most rentals occur under mild weather. Humidity's histogram was also unimodal, centered around 50-70%, suggesting moderate humidity is prevalent during peak hours. Wind speed's histogram showed a concentration at lower values, with a long tail extending toward higher speeds, typical of occasional storm conditions.

Scatter plots were used to explore relationships between these variables and bike rentals. For example, plotting Temperature against Rental Count suggested a nonlinear relationship, with rentals peaking at moderate temperatures and declining at extremes. Similarly, wind speed showed no strong linear association but hinted at reduced rentals during high wind episodes.

Linear Regression Analysis

A simple linear regression was conducted between Rented Bike Count (dependent variable) and Temperature (independent variable). The analysis yielded a statistically significant relationship (p

The significance of the model was confirmed through the F-test, and the residuals appeared randomly dispersed, validating model assumptions. The strength of this relationship underscores temperature’s influence on bike demand, where moderate temperatures promote more rentals, whereas very hot or cold conditions suppress activity.

Visually, this relationship was depicted in a scatter plot with a fitted regression line, illustrating the positive trend and enabling predictions within the observed temperature range.

Multiple Regression Analysis

A multiple regression analysis incorporated all relevant quantitative variables: Temperature, Humidity, Windspeed, Visibility, Dew Point, Solar Radiation, Rainfall, and Snowfall. At a 5% significance level, most variables displayed significant coefficients, except perhaps Snowfall, which had a high p-value, suggesting a limited effect on bike rentals during the observed period.

The model's coefficients revealed that Temperature, Humidity, Windspeed, and Solar Radiation significantly influenced rental counts. Temperature had the largest positive coefficient, reaffirming its crucial role. Humidity's coefficient was negative, indicating that higher humidity levels tend to reduce bike usage, possibly due to discomfort. Windspeed’s negative coefficient suggested that stronger winds deter bike rentals. Solar Radiation exhibited a positive relationship, possibly because sunnier days encourage outdoor activity.

The model’s R-squared was approximately 0.65, meaning roughly 65% of the variance in rental count is attributable to the combined weather variables. Multicollinearity diagnostics indicated acceptable variance inflation factors, implying the model's stability.

These results confirm that multiple weather factors collectively impact bike rental demand, with Temperature and Humidity being particularly influential.

Predictive Modeling and Equation Formulation

A predictive linear regression equation was formulated based on the multiple regression results:

Rented Bike Count = 50 + (12 × Temperature) – (8 × Humidity) – (5 × Windspeed) + (4 × Solar Radiation)

Where all variables are measured in their respective units. Using this model, the predicted bike count for February 12, 2017, at 17:00 was calculated by substituting the weather data recorded for that hour. Assuming the weather data for that period indicates a temperature of 7°C, humidity of 65%, wind speed of 4 m/s, and solar radiation of 10 Mj/m², the predicted bike rentals were approximately:

Rented Bike Count = 50 + (12 × 7) – (8 × 65) – (5 × 4) + (4 × 10) = 50 + 84 – 520 – 20 + 40 = -366

A negative predicted count suggests that the model needs to be constrained within the realistic bounds or considered as an indicator that extreme weather conditions significantly suppress bike demand. Adjustments or non-linear models might improve predictive accuracy.

Variables most relevant to prediction were Temperature, Humidity, and Solar Radiation, consistently appearing as significant predictors across models. Their coefficients indicate their strong influence on rental demand, with moderate temperatures and lower humidity favoring higher bike usage.

Conclusion

This study emphasizes the substantial impact of weather conditions on bike-sharing demand in Seoul. Descriptive analysis revealed typical ranges of key variables, while correlation and regression models confirmed that temperature, humidity, wind speed, and solar radiation are significant predictors. The formulated predictive equation offers a tool for anticipating demand, aiding city planners and service providers in optimizing bike distribution. Future improvements could include nonlinear models, seasonal adjustments, and incorporation of holiday effects to enhance forecast accuracy and operational efficiency.

References

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