year Income ($) m P 1994 6036 85.1 20.4 1995 6113 87.8 20.2 1996 6271 88.9 21.3 1997 6378 94.5 19.9 1998 6727 99.9 18 1999 7027 99.5 19.9 2000 7280 104.2 22.2 2001 7513 106.5 22.3 2002 7728 109.7 23.4 2003 7891 110.8 26.2 2004 8134 113.7 27.1 2005 8322 113 29 2006 8562 116 33.5 2007 9042 108.7 42.8 2008 8867 115.4 35.6 2009 8944 118.9 32.2 2010 9175 127.4 33.7 2011 9381 123.5 34.4 2012 9735 117.9 48.5 2013 9829 105.4 66.1 2014 9722 103.2 62.4 2015 9769 104.2 58.6 2016 9725 103.7 56.7 2017 9930 105.7 55.5 2018 10419 105.5 57.3 2019 10625 106.5 53.7 2020 10905 107.3 52.6 Make a forecast of Mr. X's Income for the next five years Make forecast of income for the given values of B-110 and P=55

Monthly Profit of an Auto Repair Shop Month Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Sep-14 Profit ($) 17,12317,123 17,27317,273 16,05516,055 18,13818,138 19,76319,763 17,31017,310 19,13119,131 18,57818,578 20,37320,373   Step 1 of 4 :   Determine the three-period moving average for the next time period. If necessary, round your answer to one decimal place.

Wins Attendance (In Millions) 65 2.06 91 2.51 66 1.73 89 3.05 75 3.06 88 2.19 91 2.06 69 1.39 83 2.88 81 2.46 80 1.54 76 2.33 67 1.62 80 3.25 80 3.56 77 2.78 94 3.22 79 2.56 95 3.77 81 1.42 97 3.65 57 1.61 90 2.13 92 3.04 61 2.09 86 3.3 96 1.84 90 2.51 85 1.63 69 1.83   Draw a scatter diagram depicting the correlation between the two variables.    Determine the correlation coefficient between the two variables using Excel.   Present a regression output between a dependent and one independent variable.     Obtain estimated coefficients and R-squared and interpret them.     Write a short report on the findings.

End of month S&P 500 End of month S&P 500 End of month S&P 500 End of month S&P 500 September-77 96.53 April-80 106.29 November-82 138.53 July-85 191.85 October-77 92.34 May-80 111.24 December-82 140.64 August-85 190.92 November-77 94.83 June-80 114.24 January-83 145.30 September-85 188.63 December-77 95.10 July-80 121.67 February-83 148.06 October-85 182.08 January-78 89.25 August-80 122.38 March-83 152.96 November-85 189.82 February-78 87.04 September-80 125.46 April-83 164.43 December-85 202.17 March-78 89.21 October-80 127.47 May-83 162.39 January-86 211.28 April-78 96.83 November-80 140.52 June-83 167.64 February-86 226.92 May-78 97.24 December-80 135.76 July-83 162.56 March-86 238.90 June-78 95.53 January-81 129.55 August-83 164.40 April-86 235.52 July-78 100.68 February-81 131.27 September-83 166.07 May-86 247.35 August-78 103.29 March-81 136.00 October-83 163.55 June-86 250.84 September-78 102.54 April-81 132.81 November-83 166.40…

Year Population 1790 3,929,214 1800 5,308,483 1810 7,239,881 1820 9,638,453 1830 12,866,020 1840 17,069,453 1850 23,191,876 1860 31,443,321 1870 39,818,449 1880 50,155,783 1890 62,947,714 1900 75,994,575 1910 91,972,266 1920 105,710,620 1930 122,775,046 1940 131,669,275 1950 150,697,361 1960 179,323,175 1970 203,302,031 1980 226,545,805 1990 248,709,873 2000 281,421,906 2010 308,745,538 Use excel to calculate the Least Squares Regression Line for the census data? (need to include correct values for a and b) Using the line, you calculated in number 5, what would you predict the population to have been in 1985? EXPLAIN. Should you use the previously calculated Regression Line to predict population in the year 2200? EXPLAIN.