Scenario: You work as an accountant for Hamilton City Council. The city council has a biking plan to transform Hamilton into a bikefriendly city.1 A bike-sharing system has recently been introduced to promote greater social participation. improve urban mobility, and reduce environmental impact from fuel consumption. For this system to be effective, the city council needs to ensure bicycles are readily available to users when they want to use them and that they do not oversupply bicycles as that poses higher risks of vandalism and theft.2 The project manager {Levi Ackenrlan} has asked you to investigate the factors that influence bicycle demand and build a model to forecast the number ofbicycles required over the coming months. Hamster includes hourly data for one month on the following variables}: I Obs lClbservation number I Year Year I Bike Number of shared bicycles in use I Temp Temperature recorded in degree Celsius I Humidity Relative humidity expressed as a percentage I Wind 1|Wind speed measured in meters per second I Rain Amount of rainfall measured in millimetres Complete your Excel analysis directly on Batmxfsx. Copy and paste relevant Excel output onto Answe'mfcx and type any text responses directly in the 1|Ih'orid document. Submit both the Excel workheek and Word document onto Meadle. Submissions without the Excel workbook will not be marked. 1. Create an appropriate chart that shows the trend in hourly bicycle use over the month. (Format your chart as appropriate). (10 marks) 2. Create a scatter plot that shows the relation between Bike and Temp. (Format your chart as appropriate). (10 marks) 3. Run correlation analysis on all variables apart from Obs and Year. Use conditional formatting to highlight any moderate to strong relations. (20 marks)4. Using the data provided, form the most appropriate regression model that can be used to predict bicycle demand. Comment on the goodness-of-fit and statistical significance of the variable(s) in your final model. (35 marks) 5. Write an email to Levi that provides an overview of your findings, including the factors that influence bicycle demand and how well your prediction model performs. Within the email, describe three distinct factors that are not captured in the dataset but their inclusion may improve model predictability. (Maximum 250 words, no minimum word count. Must be in the format and structure of a professional business email. Proofread thoroughly). (25 marks)