We can work on Basic Linear Regression Model

What is the best regression model you can find for predicting total number of bikes rented in a particular hour (variable cnt) using environmental conditions and other available explanatory variables?
What is the interpretation of your chosen model, and how much do you trust that interpretation? Is it reasonable to view any of your fitted parameters as causal effects? Why or why not? What guidance does the form of your model provide for future studies of bike share use?
Some of you may be tempted to use sophisticated machine learning methods or time series approaches (since the data has a time series structure) for your analysis. Please remember that this course is about linear regression.
The dataset contains information on bike rentals for two years (2011 and 2012) from Capital Bikeshare System, Washington D.C. Bike sharing systems are bike rentals where the whole process from membership, rental and return is automatic. Through these systems, the user is able to easily rent a bike from a particular position and return at another position.

This dataset is collected to address the problem of predicting the number of bike rentals in a given hour given the environmental and seasonal conditions for that hour. The dataset contains 17379 observations with each observation corresponding to one particular hour. The dataset contains the following 17 variables:

  1. instant : Unique observation number.
  2. dteday: Date.
  3. season: Categorical variable (1: Spring, 2: Summer, 3: Fall, 4: Winter).
  4. yr: Stands for year. Binary variable (0 stands for 2011 and 1 stands for 2012).
  5. mnth: Stands for month. Takes the values 1, 2, . . . , 12.
  6. hr: Indicates the hour of the day (takes values 0, . . . , 23).
  7. holiday: Indicates whether the day is a holiday or not
  8. weekday: Day of the week, numbered 0 (Sunday) through 6 (Saturday)

9.workingday: Takes the value 1 if the day is neither weekend nor holiday and takes the value 0 otherwise.

  1. weathersit: Takes four values:

(a) 1 if the weather is Clear, Few clouds, Partly cloudy, Partly cloudy.
(b) 2 if the weather is Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist.
(c) 3 if the weather is Light Snow, Light Rain + Thunderstorm + Scattered clouds, Light Rain + Scattered clouds.
(d) 4 if the weather is Heavy Rain + Ice Pallets + Thunderstorm + Mist, Snow + Fog.

Sample Solution

to remain a market leader I believe that Apple needs to do a better job of insuring their customers that the information on their iPhones are being keep private. Reliability is one trait that customers consider when shopping for smartphones and computers. Apple has already set itself apart from its competitors with their products but they seem to fall into the same category when it comes to security. Apple has a privacy statement on their website that reads “Every Apple product is designed from the ground up to protect that information.” Apple needs to prove to their customers that they are doing a better job of protecting their privacy than their competitors. Each year they need to continue to keep privacy as a focus, when they release new updates and new products privacy must be top on the list. On Apples website, they have a very lengthy privacy statement about how and what they do to protect it. I think this is a good step because it lets customers know that they are being conscientious about it. Apple also releases updates when issues occur. Apple has openly said that collect information about tendencies but has assured customers that the information is not shared with outside companies or the dark web. Apple and all other tech companies that are in the smartphone market should be very diligent about insuring customer privacy. The government has considered passing legislation on mobile privacy. I recommend Apple continue to update their customers on how they are continuing to improve their privacy and how they are protecting their user’s data. I believe that will set them apart from their competitors and keep them at the top of the smartphone industry.>

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