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Accounting Introduction to data science

Analyze the dataset and answer the following questions to help understand the effects of different weather variables on the demand of rental bikes in the city. These are the variables that the file contains:

Variable: Type of data: Units:

Date Date

Rented Bike Count Integer

Hour Integer

Temperature Continuous C

Humidity Integer %

Wind speed Continuous m/s

Visibility Integer 10m

Dew point temperature Continuous C

Solar Radiation Continuous Mj/m2

Rainfall Integer mm

Snowfall Integer cm

Seasons Categorical

Holiday Binary

Functioning Day Binary

Questions:

Descriptive Statistics and Distributions:

§ Describe the following variables: Temperature, Humidity, Wind speed. [10%]

§ Represent these variables using at least two types of charts and discuss their distributions/frequencies. [15%]

Linear Regression:

§ Perform a linear regression between Rented Bike Count and another quantitative variable of your choice. [10%]

§ Discuss the significance and the strength of the relationship between them. Interpret the results. [10%]

§ Represent it using a chart. [5%]

Multiple Regression:

§ Perform a multiple regression analysis to identify the relationships between Rented Bike Count and all the other quantitative variables of the dataset. Discuss the results at a level of significance of α=5%. [15%]

§ What are the coefficients for each variable? Interpret the results. [10%]

Predictive Modeling:

§ Create a linear regression equation to predict Rented Bike Count. [15%]

§ Using the equation, predict the number of Rented Bike Count on the 2/12/2017 at 17:00. [5%]

Full Answer Section

2. Linear Regression

Variable Selection: Choose a quantitative variable that you believe has a strong correlation with Rented Bike Count (e.g., Temperature).
Model Building: Fit a linear regression model to predict Rented Bike Count based on the selected variable.
Model Evaluation: Assess the model's performance using metrics like R-squared, adjusted R-squared, and p-values.
Interpretation: Interpret the regression coefficients to understand the relationship between the variables.

3. Multiple Regression

Model Building: Fit a multiple regression model to predict Rented Bike Count based on all quantitative variables.
Variable Selection: Consider using techniques like stepwise regression or feature selection to identify the most important variables.
Model Evaluation: Assess the model's performance using metrics like adjusted R-squared, F-statistic, and p-values.
Interpretation: Interpret the coefficients of each variable to understand their impact on Rented Bike Count.

4. Predictive Modeling

Model Training: Use the multiple regression model to train the model on the historical data.
Prediction: Input the specific values for date and time (2/12/2017 at 17:00) into the model to obtain a predicted value for Rented Bike Count.

Tools and Techniques

Statistical Software: Use statistical software like R, Python (with libraries like Pandas, NumPy, and Scikit-learn), or specialized data analysis software (e.g., SPSS, SAS) to perform the analysis.
Data Visualization: Use libraries like Matplotlib, Seaborn, or ggplot2 to create informative visualizations.
Machine Learning: For more complex models and predictions, consider machine learning techniques like random forest, gradient boosting, or neural networks.

Additional Considerations

Data Cleaning: Ensure that the data is clean and free of errors or missing values.
Outlier Detection and Handling: Identify and handle outliers appropriately.
Feature Engineering: Create new features or transform existing ones to improve model performance.
Model Validation: Use techniques like cross-validation to assess the model's generalization performance.

By following these steps and leveraging appropriate statistical tools, you can gain valuable insights into the factors affecting bike rental demand and make informed decisions.

Sample Answer

1. Descriptive Statistics and Distributions

Descriptive Statistics: Calculate measures of central tendency (mean, median, mode) and dispersion (standard deviation, range) for temperature, humidity, and wind speed.
Data Visualization:
- Histograms: Visualize the distribution of each variable.
- Box Plots: Show the distribution, outliers, and quartiles.
- Scatter Plots: Explore relationships between variables.