Suppose a firm has had the following historic sales figures. Year: 2016 2017 Sales $3,300,000 $4,550,000 2018 2019 $3,200,000 $2,800,000 Next year's sales 2020 $3,400,000 What would be the forecast for next year's sales using the naïve approach?

Gary is creating an index that helps compare the salaries of two jobs that he is considering. In the table below, what is the 2017 value for the Salary Index of Job 2? Base Salaries (2015-2017)     2015 2016 2017 Job 1 $30,000 $37,000 $39,000 Job 2 $40,000 $45,000 $53,000   Salary Index (2015-2017)     2015 2016 2017 Job 1 100 123 130 Job 2 100 113 ?   133 153 130 123

Consider the following monthly sales data for a company: Jan-2015 147772 Feb-2015 144335 Mar-2015 163943 Apr-2015 169634 May-2015 175371 Jun-2015 174825 Jul-2015 176995 Aug-2015 176208 Sep-2015 170202 Oct-2015 175510 Nov-2015 180364 Dec-2015 217629 Jan-2016 154400 Feb-2016 155596 Mar-2016 183782 Apr-2016 181209 May-2016 186359 Jun-2016 189244 Jul-2016 184978 Aug-2016 192925 Using the method of moving averages over 2 months, which month in 2016 matches the predicted value the worst? O May June July January 2

Below are percentages for annual sales growth and net sales attributed to loyalty card usage at 74 Noodles & Company restaurants.  Annual Sales Growth (%) and Loyalty Card Usage (% of Net Sales)(n = 74 restaurants) Store Growth% Loyalty% Store Growth% Loyalty%   1     -8.3     2.1     38     7.1     1.6     2     -4.0     2.5     39     7.4     1.8     3     -3.9     1.7     40     7.7     2.2     4     -3.4     2.1     41     7.9     2.2     5     -3.3     2.5     42     8.1     2.8     6     -1.9     3.0     43     8.3     2.4     7     -0.8     2.3     44     8.5     3.1     8     -0.4     2.3     45     8.6     2.2     9     -0.2     2.2     46     8.7     1.3     10     -0.2     2.3     47     8.8     1.8     11     0.5     2.1     48     8.8     2.5     12     0.6     2.5     49     8.9     1.9     13     0.8     2.0     50     9.1     2.0     14     1.9     2.0     51     9.5     2.4     15     2.0     2.0     52     10.2     2.2     16…

Using the accompanying​ table, determine the most appropriate forecasting technique for the accompanying data about the Dow Jones Industrial Average yearly closing value and implement the model. Year Close 1983 1258.64 1984 1211.57 1985 1546.67 1986 1895.95 1987 1938.83 1988 2168.57 1989 2753.2 1990 2633.66 1991 3168.83 1992 3301.11 1993 3754.09 1994 3834.44 1995 5117.12 1996 6448.27 1997 7908.25 1998 9181.43 1999 11497.12 2000 10786.85 2001 10021.5 2002 8341.63 2003 10453.92 2004 10783.01 2005 10717.5 2006 12463.15 2007 13264.82 2008 8776.39 2009 10428.05 2010 11557.5 2011 12217.6 2012 13104.1 Use the model forecast the closing value for 2015.Use α=0.7​,β=0.3​,and γ=0.4​,as needed. F2015?

The following graph shows the annual number of car accidents in California. Which of the following statements about the annual number of car accidents is an accurate conclusion? Yearly Car Accidents 180,000 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 2000 2005 Year 1985 1990 1995 2010 2015 2020 e 2018 Glynlyon, Inc. O There is a greater decrease in the annual number of car accidents from 1994 to 1995 than from 1997 to 1998. O There is a smaller decrease in the annual number of car accidents from 1999 to 2000 than from 1997 to 1998. O There is a smaller increase in the annualnumber of car accidents from 1995 to 1996 than from 1998 to 1999. O There is a greater increase in the annual number of car accidents from 1993 to 1994 than from 1996 to 1997. Car Accidents

Q5B The table below contains the value of the house and the amount of rental income in a year that the house brings in ("Capital and rental," 2013).  Table:  Data of House Value versus Rental Value Rental Value Rental Value Rental Value Rental 81000 6656 77000 4576 75000 7280 67500 6864 95000 7904 94000 8736 90000 6240 85000 7072 121000 12064 115000 7904 110000 7072 104000 7904 135000 8320 130000 9776 126000 6240 125000 7904 145000 8320 140000 9568 140000 9152 135000 7488 165000 13312 165000 8528 155000 7488 148000 8320 178000 11856 174000 10400 170000 9568 170000 12688 200000 12272 200000 10608 194000 11232 190000 8320 214000 8528 208000 10400 200000 10400 200000 8320 240000 10192 240000 12064 240000 11648 225000 12480 289000 11648 270000 12896 262000 10192 244500 11232…

An operation analyst is forecasting this year's demand for one of his company's products based on the following historical data: (4 year ago Quantity sold was 1059), (3 year ago Quantity sold was 1169 ), (2 year ago Quantity sold was 1740 ) and the last year Quantity sold was 1903 . The previous trend line had predicted 1218 for three years ago, 1287 for two years ago and 1355 for last year. What was the mean squared error (MSE) for these forecasts? a. 169304.67 b. 19.67 c. 350 d. 0.2

The following table represents the percentage of the U.S. population who have completed four years of college or more from 1976 to 2016, by gender. Year 1976 1981 1986 1991 1996 2001 2006 2011 2016 % of Women 11.3 13.4 16.1 18.8 21.4 24.3 26.9 30.1 33.7 % of Men 18.6 21.1 23.2 24.3 26 28.2 29.2 30.8 33.2 (a) What is the AROC in the percentage of women who had completed four years of college or more from the year 1986 to 2006? (b) What is the AROC in the percentage of men who had completed four years of college or more from the year 1986 to 2006? (c) From the years 1976 to 1986, the AROC in the percentage of men who had completed 4 years or more of college is AROC for women is Σ %. (d) From the years 2006 to 2016, the AROC in the percentage of men who had completed 4 years or more of college is AROC for women is (e) What are the inferences you can make from your answers to parts (a)-(d)? Choose all that apply. Σ Σ Z %, and the A. The percentage of men who are completing 4 years of more…

U.S. Civilian Labor Force (thousands) Year Labor Force   Year Labor Force 2007 153,918   2012 155,628 2008 154,655   2013 155,151 2009 153,111   2014 156,238 2010 153,650   2015 157,957 2011 153,995   2016 159,640   1. Fit three trend models: linear, exponential, and quadratic. Which model would offer the most believable forecasts? 2. Make forecasts using the following fitted trend models for years 2017-2019. t          Exponential 11 12 13