The main purpose of this assignment is to evaluate your ability to understand real-world datasets, and extracting relevant, useful information 'om them. This class project, can give you good perspective on how all the materials you've learned during this class, is going to help you at your future jobs to help people make more informed decisions based on real-world data (i.e. empirical evidence). Context-Imagine that you've started your own management consulting company, and you have a client from London who is a bike-sharing company. Their business as you can say intuitively, highly relies on the number of bikes demanded, and their ability to meet the demand. To this end, to minimize the possibility of losing customers for bikes being not available at the time of demand, while avoiding the possibility of maintaining unnecessary large number of bikes and the associated maintenance costs, they need predictions on the demand of bikes. The company have been observing that the demand is very much correlated with the weather conditions. They are providing you with a dataset of the number of bikes in one-hour time intervals between 2015 and 2016 along with weather conditions. Instructions-Please utilize the London bike-sharing dataset to predict the number of bikes for the bike-sharing company. To be explicitly clear, you must use at minimum 4 variables to make your prediction. The more work you put into this take home (i.e., variables, justification, interpretation) the higher your exam score will be. I reserve the right to award extra credit to those who go above and beyond. 0 Please provide the list of variables you included in your regression. Format them as: 1. Variable 1 2. Variable 2 3. etc. 0 Please provide your reasoning for why you included these variables. Please provide a reason for each individual variable. Format them as: Variable 1 - The reason I believe variable one has an impact on our predicted variable is because... Variable 2 - Theory for why variable 2 has an impact on nal grades. etc. . Provide whether your regression is significant, and your coefficients are significant. Interpret the measure of variation explained. Example: My regression is significant at the 0.xxx level Variable 1 is significant at 0.xxx level Variable 2 is significant at 0.xxx level etc. (all variables) Provide an explanation of the measure of variation explained here. . You are presenting your findings to the board of the bike-sharing company. Is there anything you can tell them about the most influencing factors on the bike demands? Is there anything concerning from your results? How can you use confidence intervals of your results to explain your findings for the bike-sharing company board members?AutoSave OFF London bike sharing data Home Insert Draw Page Layout Formulas Data Review View Automate Developer Acroba ab Calibri (Body) 12 AA General Conditiona Format as Paste BI UV $ ~ % " 00 -20 i Cell Styles Open recovered workbooks? Your recent changes were saved. Do you want to continue working where you left off? 06 + X V fx A B C D E F G H J K 1 timestamp cnt t1 t2 hum wind_speed weather_code is_holiday is_weekend season N 1/4/15 0:00 182 3 2 93 6 3 1/4/15 1:00 138 3 2.5 93 5 PPPP 1/4/15 2:00 134 2.5 2.5 96.5 0 1/4/15 3:00 72 2 2 100 0 1/4/15 4:00 47 0 93 6.5 NNI 1/4/15 5:00 46 2 93 4 1/4/15 6:00 51 1 -1 100 7 1/4/15 7:00 75 1 -1 100 7 10 1/4/15 8:00 131 -1 96.5 8 11 1/4/15 9:00 301 -0.5 100 12 1/4/15 10:00 528 -0.5 93 12 13 1/4/15 11:00 727 in w N N W N in -1.5 100 12 o o o o o o o o o O O O O O O O O O O O O O 14 1/4/15 12:00 862 -1.5 96.5 13 15 1/4/15 13:00 916 0.5 87 15 - WW WW WW WW AWWW AAA 16 1/4/15 14:00 1039 0 90 10o 17 1/4/15 15:00 869 1.5 93 11 18 1/4/15 16:00 737 0 93 12 19 1/4/15 17:00 594 0 93 20 1/4/15 18:00 522 N W W W W W W N 1.5 93 6.5 21 1/4/15 19:00 379 93 22 1/4/15 20:00 328 93 23 1/4/15 21:00 221 2.5 93 24 1/4/15 22:00 178 2 93Metadata: "timestamp" - timestamp field for grouping the data "cnt" - the count of a new bike shares "t1" - real temperature in C "t2" - temperature in C "feels like" "hum" - humidity in percentage "wind_speed" - wind speed in km/h "weather_code" - category of the weather "is_holiday" - boolean field - 1 holiday / 0 non holiday "is_weekend" - boolean field - 1 if the day is weekend "season" - category field meteorological seasons: 0-spring ; 1-summer; 2-fall; 3-winter. "weathe_code" category description: 1 = Clear ; mostly clear but have some values with haze/fog/patches of fog/ fog in vicinity 2 = scattered clouds / few clouds 3 = Broken clouds 4 = Cloudy 7 = Rain/ light Rain shower/ Light rain 10 = rain with thunderstorm 26 = snowfall 94 = Freezing Fog L+A25 Content The data is acquired from 3 sources - Https://cycling.data.tfl.gov.uk/ 'Contains OS data @ Crown copyright and database rights 2016 and Geomni UK Map data @ and database rights [2019] 'Powered by Tfl Open Data' -freemeteo.com - weather data - https://www.gov.uk/bank-holidays From 1/1/2015 to 31/12/2016 The data from cycling dataset is grouped by "Start time", this represent the count of new bike shares grouped by hour. The long duration shares are not taken in the count