This file contains the quarterly revenues of an online retailer from the first quarter of 2001 to the second quarter or 2017. For each observation, the Fiscal Year and Fiscal Quarter are recorded, as well as a variable counting quarters from the first to the last observation in the data. There is also a tab containing a scatter plot of the data with Quarter on the horizontal axis and Quarterly Revenue on the vertical axis.

Date	Fiscal Year	Fiscal Quarter	Quarter	Quarterly Revenue
03/31/2001	2001	1	1	700.356
06/30/2001	2001	2	2	667.625
09/30/2001	2001	3	3	639.281
12/31/2001	2001	4	4	1,115.171
03/31/2002	2002	1	5	847.422
06/30/2002	2002	2	6	805.605
09/30/2002	2002	3	7	851.299
12/31/2002	2002	4	8	1,428.610
03/31/2003	2003	1	9	1,083.559
06/30/2003	2003	2	10	1,099.912
09/30/2003	2003	3	11	1,134.456
12/31/2003	2003	4	12	1,945.772
03/31/2004	2004	1	13	1,530.349
06/30/2004	2004	2	14	1,387.341
09/30/2004	2004	3	15	1,462.475
12/31/2004	2004	4	16	2,540.959
03/31/2005	2005	1	17	1,902.000
06/30/2005	2005	2	18	1,753.000
09/30/2005	2005	3	19	1,858.000
12/31/2005	2005	4	20	2,977.000
03/31/2006	2006	1	21	2,279.000
06/30/2006	2006	2	22	2,139.000
09/30/2006	2006	3	23	2,307.000
12/31/2006	2006	4	24	3,986.000
03/31/2007	2007	1	25	3,015.000
06/30/2007	2007	2	26	2,886.000
09/30/2007	2007	3	27	3,262.000
12/31/2007	2007	4	28	5,672.000
03/31/2008	2008	1	29	4,135.000
06/30/2008	2008	2	30	4,063.000
09/30/2008	2008	3	31	4,265.000
12/31/2008	2008	4	32	6,703.000
03/31/2009	2009	1	33	4,889.000
06/30/2009	2009	2	34	4,652.000
09/30/2009	2009	3	35	5,448.000
12/31/2009	2009	4	36	9,520.000
03/31/2010	2010	1	37	7,131.000
06/30/2010	2010	2	38	6,566.000
09/30/2010	2010	3	39	7,560.000
12/31/2010	2010	4	40	12,947.000
03/31/2011	2011	1	41	9,857.000
06/30/2011	2011	2	42	9,913.000
09/30/2011	2011	3	43	10,876.000
12/31/2011	2011	4	44	17,431.000
03/31/2012	2012	1	45	13,185.000
06/30/2012	2012	2	46	12,834.000
09/30/2012	2012	3	47	13,806.000
12/31/2012	2012	4	48	21,268.000
03/31/2013	2013	1	49	16,070.000
06/30/2013	2013	2	50	15,704.000
09/30/2013	2013	3	51	17,091.000
12/31/2013	2013	4	52	25,587.000
03/31/2014	2014	1	53	19,741.000
06/30/2014	2014	2	54	19,340.000
09/30/2014	2014	3	55	20,578.000
12/31/2014	2014	4	56	29,329.000
03/31/2015	2015	1	57	22,717.000
06/30/2015	2015	2	58	23,184.000
09/30/2015	2015	3	59	25,358.000
12/31/2015	2015	4	60	35,747.000
03/31/2016	2016	1	61	29,128.000
06/30/2016	2016	2	62	30,404.000
9/30/2016	2016	3	63	32,714.000
12/31/2016	2016	4	64	43,741.000
3/31/2017	2017	1	65	35,714.000
6/30/2017	2017	2	66	37,955.000

Question 1:Which of the following observations about a scatter plot of the data (see the "Scatter QR-Quarter" tab) is false?

A.QR increases with Quarter at an increasing rate

B.QR increases with Quarter at a constant rate C.The Quarterly Revenue in the fourth quarter of every year is noticeably larger than the Quarterly revenue of the previous three quarters

Question 2: Creating a variable called LnQR that is equal to the natural log of Quarterly Revenue. Run a regression where LnQR is the dependent variable and Quarter is the independent variable, and use it to forecast Quarterly Revenue in the third quarter of 2017. Which of the following is a true statement about this regression?

A.If Quarter changes by one unit quarterly revenue change by 0.065 units

B.The forecasted value of Quarterly Revenue for the third quarter of 2017 is 10.807

C.The standard error of the forecasted value of Quarterly Revenue for the third quarter of 2017 is 0.196

D.A 95% confidence interval for the forecasted value of Quarterly Revenue for the third quarter of 2017 is [33,365.179 , 73,011.828]

Question 3:Creating a variable called Lag4QR that is equal to the value of Quarterly Revenue 4 periods ago. Run a regression where QR is the dependent variable and Lag4QR is the independent variable, and use it to forecast Quarterly Revenue in the third quarter of 2017. In comparing the forecast from this model to the one you obtained in the previous question, which of the following statements is correct.

A.The model from the previous question is better for forecasting

B.The model from this question is better for forecasting

C.We cannot decide which model is better for forecasting