代做Comparison of Linear and Quadratic Regression Models调试R语言

Comparison of Linear and Quadratic Regression Models

The following data presents the growth in worldwide Internet usage from 1995 through 2011:

Year:	1995	1996	1997	1998	1999	2000	2001
No. of users (in millions):	16	36	70	147	248	361	533

Year:	2002	2003	2004	2005	2006	2007	2008
No. of users (in millions):	597	719	817	1018	1093	1319	1574

Year:	2009	2010	2011
No. of users (in millions):	1802	2013	2267

1. Enter the data into L1 and L2 and create a linear model for the data:

a. What is your linear model?___________________________

b. What is the slope?   _____

c. Interpret the slope within the context of the problem.

d.  What is the r-value?  _______  What does this tell you about the linear relationship between x and y?

e. What is the r² value?  What does this tell you about the relationship between x and y?

f.  Do a scatterplot of Y1 and the data.  What does that tell you about the linear model?

g.  Store the predictions in L3 and the Residuals in L4.

h.  Do a scatterplot of X-values vs Residuals.  When doing this plot, first Deselect Y1.  Sketch the plot here.  What does this tell you about the linear model?

i. Create a quadratic model:___________________________

j. What is the R² value?  What does this tell you about the relationship between x and y?

k.  Do a scatterplot of Y1 and the data.  What does that tell you about the quadratic  model?

l.  Store the predictions in L3 and the Residuals in L4.

m.  Do a scatterplot of X-values vs Residuals.  When doing this plot, first Deselect Y1.  Sketch the plot here.  What does this tell you about the quadratic model?

n.  Look at the Residual plot and use the Trace Key to find the ‘largest’ residual (it could be positive or negative.)  Go back to your Stat Editor and find the corresponding x-value, y-value and predicted y-value.

o.  Use your model stored in Y1 to estimate the number of internet users for 2012.