- Find and interpret the confidence interval for the mean response
- Find and interpret the prediction interval for an individual response
- Identify whether a confidence interval or a prediction interval is more appropriate in context of the problem
Capital Bikeshare Rentals
Suppose you are a data scientist for Capital Bikeshare in Washington, D.C., and your job is to develop a linear regression model to predict the number of bike rentals based on the temperature. These predictions will be used to help determine the number of bikes to make available across the city each day.
Previously, you’ve used the regression model to calculate a predicted value of the response given a particular value of the explanatory variable. This time, you decide to include an interval with your predictions, so you report a plausible range of values the number of bike rentals might take, given a particular value of temperature.
Data set: Capital Bikeshare in Washington, D.C.
It contains daily information about the number of bike rentals, weather, day of the week, and other details for days in 2011 and 2012. Your primary objective in this activity is to predict the number of daily bike rentals during the winter months (December [latex]21[/latex] to March [latex]20[/latex]). To do so, you’ll use data from [latex]50[/latex] randomly selected winter days in 2011 and 2012. The variables of interest for this activity are:
- count: Total number of bikes rented
- temperature: Approximate high temperature in degrees Fahrenheit
Step 2: Under “Enter Data,” select “Enter Own.”
Step 3: Select the appropriate explanatory variable ([latex]x[/latex]) and response variable ([latex]y[/latex]).
Step 4: Enter the data.
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