Welcome to the Yulu Hypothesis Testing project! 🇮🇳 Yulu is India's leading micro-mobility service provider, committed to eliminating traffic congestion by offering unique shared electric cycles for daily commutes. 🚴♂️🛴
Yulu operates in key locations, including metro stations, bus stands, office spaces, residential areas, and corporate offices, to make commuting smooth, affordable, and sustainable. 🏙️🏢 Recently, Yulu has faced challenges with its revenues, and they've partnered with a consulting company to understand the factors influencing the demand for shared electric cycles in the Indian market. This project aims to uncover these insights. 📉📈
Yulu is eager to find out:
- Which variables significantly predict the demand for shared electric cycles in the Indian market?
- How well these variables describe the electric cycle demand?
- datetime : Date and time
- season: Season (1: spring, 2: summer, 3: fall, 4: winter)
- holiday: Whether it's a holiday or not
- workingday: If it's a working day (1) or not (0)
- weather: -- Clear, Few clouds, partly cloudy -- Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist -- Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds -- Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog
- temp: Temperature in Celsius
- atemp: Feeling temperature in Celsius
- humidity: Humidity
- windspeed: Wind speed
- casual: Count of casual users
- registered: Count of registered users
- count: Count of total rental bikes, including casual and registered users
- Bi-Variate Analysis
- 2-sample t-test: Testing for differences across populations
- ANOVA
- Chi-square
- Import the dataset and perform exploratory data analysis to understand its structure and characteristics.
- Establish relationships between the dependent variable, "Count," and independent variables like Workingday, Weather, Season, etc.
- Select an appropriate test to determine:
- If Working Day affects the number of electric cycles rented.
- Whether the number of cycles rented differs in different seasons.
- Whether the number of cycles rented differs under different weather conditions.
- If weather is dependent on the season.
- If Working Day affects the number of electric cycles rented.
- Set up Null Hypothesis (H0).
- State the alternate hypothesis (H1).
- Check assumptions of the test (Normality, Equal Variance). Use tools like histograms, Q-Q plots, or statistical methods like Levene’s test and Shapiro-Wilk test (optional).
- Continue with the analysis even if some assumptions fail. Double-check using visual analysis and report wherever necessary.
- Set a significance level (alpha).
- Calculate test statistics.
- Make a decision to accept or reject the null hypothesis.
- Draw meaningful inferences from the analysis.
Let's work together to help Yulu make data-driven decisions and improve their micro-mobility services! 🚴♀️🔍💡 Feel free to contribute, collaborate, and create insights for Yulu's success! 🌟📈 Happy analyzing! 📊📚🛴👩💼👨💼