exponential regression project part 1
Choose your data set:
US Population Data, from 1790-2010 (tab 1) + 2020 added (tab 2)
USPOP-1.xlsDownload USPOP-1.xls
Number of Cell Phone Subscribers Worldwide from 1997 – 2008 (tab 1) + 2009-2022 added (tab 2)
CELLSUBS-1.xlsDownload CELLSUBS-1.xls
You will compare the textbook’s data and models with the added data.
Step 1: Download the data and reinitialize the years
Step 2: Use Desmos to create a scatter plot, add a regression line, and show the equation. (Desmos allows you to choose what type of equation to use; you should use y=Cax)
Step 3: Be sure to identify the variables and their units. What is the annual growth factor? What is the growth factor? What is a growth rate?
Step 4: Predict the number of (people or cell phone subscribers) in 2015 and 2022
Step 5: Repeat Steps 1 through 3 with the data on the second tab.
Step 6: Compare your results in Step 4 with the actual data. Was the model a good prediction of the actual numbers?
Step 7: Use the equation from the second tab to repeat step 4. Is the model more accurate with the added data?
Submit a screenshot of your graph and your equations.
part 2 linear regression project
Choose from one of the datasets in the course files: https://cmsv.instructure.com/courses/17800/files/folder/Data%20sets%20for%20Linear%20Regression
Or search for something you are interested in and find some data. Here are a few free data websites:
https://www.kaggle.com/datasetsLinks to an external site. (you must sign up for a free account)
https://opendata.cityofnewyork.us/Links to an external site.
https://data.worldbank.org/Links to external site.
https://www.pewresearch.org/download-datasets/Links to an external site.
Similar to what we did in class, you will need to plot your data and create a linear regression line. (See Miro Day 11)
The hard part is cleaning the data to ensure that you are comparing two things that make sense and that your data analysis is being done in a way that makes sense. Chapter 2.11 shows how to make a large data set suitable for analysis. You can use this as a model.
Remember that time (reinitialized) should always be your independent variable if you use it. Otherwise, you can choose your independent variable, but your analysis when you talk about the relationship must make sense.
You should hand in a document with a graph and a short paragraph explaining why you chose that data and what you learned, including an analysis of the data using linear regression and correlation coefficient.
