Yuhana Kennedy July 2011. Hypothesis The growth of cell phone usage in the U.S. has leveled off due to the fact that even though the U.S. population will.

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Presentation transcript:

Yuhana Kennedy July 2011

Hypothesis The growth of cell phone usage in the U.S. has leveled off due to the fact that even though the U.S. population will continue to increase it does not do so without bound. This is important to U.S. manufacturers and distributors as they continue to market cell phones. It is the reason for the widespread manufacture and distribution of cell phones in other countries.

Number of U.S. Cell Phone Users (Subscribers) vs. Years Year, t Number of Subscribers (in millions), S 1985 (t = 1) (t = 2) (t = 3) (t = 4) (t = 5) (t = 6) (t= 7) (t = 8) (t = 9) (t = 10) (t = 11) Source: 2010 CTIA – The Wireless Association

Number of U.S. Cell Phone Users (Subscribers) vs. Years (cont’d) Year, t Number of Subscribers (in millions), S 1996 (t = 12) (t = 13) (t = 14) (t = 15) (t = 16) (t = 17) (t = 18) (t = 19) (t = 20) (t = 21) (t = 22) Source: 2010 CTIA – The Wireless Association

Number of U.S. Cell Phone Users (Subscribers) vs. Years (cont’d) Year, t Number of Subscribers (in millions), S 2007, (t = 23) , (t = 24) , (t = 25) , (t = 26) Source: 2010 CTIA – The Wireless Association

Exponential Regression Results S(t) = S(t) =1.0339(1.2923) x r=0.9576

Exponential Regression Results (cont’d) The Excel graphs of the data points do not show a strong exponential correlation even though the value of the correlation coefficient is approximately equal to 1, r= This illustrates why it is important to closely observe the graph of a given set of data points before drawing a conclusion about the type of correlation one has. This also shows the limitations of technology in assessing the type of correlation one has.

Logistics Regression Results

Conclusion By graphing the function using Maple and using my calculator to find the logistic function, my hypothesis is supported in the logistic functions in the previous slide. They both show how the data points start off increasing slightly, then rapidly before leveling off to a maximum value C = millions. I conclude that the correlation is strong logistically more so than exponentially.