·  Download the exercise dataset .CSV file from: https://archive.ics.uci.edu/da

·  Download the exercise dataset .CSV file from: https://archive.ics.uci.edu/dataset/560/seoul+bike+sharing+demand
·  Read the context and the questions below (Questions 1,2,3 & 4).
·  Analyze the Dataset in MS Excel and paste the solutions for each question in this word document (Charts, tables, numbers and explanations).
·  Upload a ZIP file with the written answers in .docx (you can use this file as a template) plus the excel file with the workings in .xlsx.
Context:
The dataset contains count of public bicycles rented per hour in the Seoul Bike Sharing System, with corresponding weather data and holiday information. Currently Rental bikes are introduced in many urban cities for the enhancement of mobility comfort. It is important to make the rental bike available and accessible to the public at the right time as it lessens the waiting time. Eventually, providing the city with a stable supply of rental bikes becomes a major concern. The crucial part is the prediction of bike count required at each hour for the stable supply of rental bikes. The dataset contains weather information (Temperature, Humidity, Windspeed, Visibility, Dewpoint, Solar radiation, Snowfall, Rainfall), the number of bikes rented per hour and date information. (Sathishkumar and Cho 2020)
Your task is to 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:
1.  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%]
2.  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%]
3.  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%]
4.  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%]
Which variables are more relevant to predict the Rented Bike Count? Interpret the results. [5
o  Wordcount: between 50 min and 500 max words per bullet point.
o  Cover, Table of Contents, References and Appendix are excluded of the total wordcount.
o  Font: Arial 12,5 pts.
o  Text alignment: Justified.
o  The in-text References and the Bibliography have to be in Harvard’s citation style.