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Linear Regression- An 80 Year 
study of the Dow Jones 
Industrial Average 
Tehya Singleton 
Rivers AP Statistics
Chart Copied From Fathom
Year Since 1930 Dow Price Predicted Dow Price Residuals Transformation Dow Price Transformation Prediction Transformation Residuals ln since 1930 ln dow price 
1930 0 233.99 -2435.42 -2669.41 2.3692 1.87328 0.495915 #Domain error# 5.45528 
1931 1 135.39 -2310.12 -2445.51 2.13159 1.90036 0.231231 0 4.90816 
1932 2 53.89 -2184.83 -2238.72 1.73151 1.92743 -0.195921 0.693147 3.98694 
1933 3 90.77 -2059.54 -2150.31 1.95794 1.9545 0.00343931 1.09861 4.50833 
1934 4 88.05 -1934.24 -2022.29 1.94473 1.98158 -0.0368473 1.38629 4.4779 
1935 5 126.23 -1808.95 -1935.18 2.10116 2.00865 0.0925124 1.60944 4.83811 
1936 6 164.86 -1683.65 -1848.51 2.21712 2.03572 0.181392 1.79176 5.1051 
1937 7 184.01 -1558.36 -1742.37 2.26484 2.0628 0.202044 1.94591 5.21499 
1938 8 141.2 -1433.06 -1574.26 2.14983 2.08987 0.0599637 2.07944 4.95018 
1939 9 143.26 -1307.77 -1451.03 2.15612 2.11694 0.0391804 2.19722 4.96466 
1940 10 126.14 -1182.47 -1308.61 2.10085 2.14402 -0.0431653 2.30259 4.83739 
1941 11 128.79 -1057.18 -1185.97 2.10988 2.17109 -0.0612096 2.3979 4.85818 
1942 12 105.72 -931.884 -1037.6 2.02416 2.19817 -0.174008 2.48491 4.66079 
1943 13 137.25 -806.59 -943.84 2.13751 2.22524 -0.0877265 2.56495 4.9218 
1944 14 146.11 -681.295 -827.405 2.16468 2.25231 -0.0876325 2.63906 4.98436 
1945 15 162.88 -556.001 -718.881 2.21187 2.27939 -0.0675183 2.70805 5.09301 
1946 16 201.56 -430.706 -632.266 2.3044 2.30646 -0.00205528 2.77259 5.30609 
1947 17 183.18 -305.412 -488.592 2.26288 2.33353 -0.0706552 2.83321 5.21047 
1948 18 181.33 -180.117 -361.447 2.25847 2.36061 -0.102137 2.89037 5.20032 
1949 19 175.92 -54.8228 -230.743 2.24532 2.38768 -0.142365 2.94444 5.17003 
1950 20 209.4 70.4717 -138.928 2.32098 2.41475 -0.0937773 2.99573 5.34425 
1951 21 257.86 195.766 -62.0938 2.41138 2.44183 -0.0304436 3.04452 5.55242 
1952 22 279.56 321.061 41.5008 2.44648 2.4689 -0.0224261 3.09104 5.63322 
1953 23 275.38 446.355 170.975 2.43993 2.49597 -0.0560423 3.13549 5.61815 
1954 24 347.92 571.65 223.73 2.54148 2.52305 0.0184311 3.17805 5.85197 
1955 25 465.85 696.944 231.094 2.66825 2.55012 0.118124 3.21888 6.14386 
1956 26 517.81 822.239 304.429 2.71417 2.5772 0.136975 3.2581 6.24961 
1957 27 508.52 947.533 439.013 2.70631 2.60427 0.102039 3.29584 6.2315 
1958 28 502.99 1072.83 569.838 2.70156 2.63134 0.0702167 3.3322 6.22057 
1959 29 674.88 1198.12 523.242 2.82923 2.65842 0.17081 3.3673 6.51453 
1960 30 616.73 1323.42 706.687 2.7901 2.68549 0.104605 3.4012 6.42443 
1961 31 705.37 1448.71 743.341 2.84842 2.71256 0.135854 3.43399 6.55872 
1962 32 597.93 1574.01 976.076 2.77665 2.73964 0.0370134 3.46574 6.39347 
1963 33 695.43 1699.3 1003.87 2.84225 2.76671 0.0755429 3.49651 6.54453
1964 34 841.1 1824.6 983.495 2.92485 2.79378 0.131063 3.52636 6.73471 
1965 35 881.74 1949.89 1068.15 2.94534 2.82086 0.124483 3.55535 6.7819 
1966 36 847.38 2075.18 1227.8 2.92808 2.84793 0.0801469 3.58352 6.74215 
1967 37 904.24 2200.48 1296.24 2.95628 2.875 0.0812788 3.61092 6.80709 
1968 38 883 2325.77 1442.77 2.94596 2.90208 0.0438822 3.63759 6.78333 
1969 39 815.47 2451.07 1635.6 2.91141 2.92915 -0.0177441 3.66356 6.70376 
1970 40 734.12 2576.36 1842.24 2.86577 2.95623 -0.0904586 3.68888 6.59867 
1971 41 858.43 2701.66 1843.23 2.9337 2.9833 -0.0495944 3.71357 6.75511 
1972 42 924.74 2826.95 1902.21 2.96602 3.01037 -0.0443532 3.73767 6.82951 
1973 43 926.4 2952.25 2025.85 2.9668 3.03745 -0.0706479 3.7612 6.83131 
1974 44 757.43 3077.54 2320.11 2.87934 3.06452 -0.185177 3.78419 6.62993 
1975 45 831.51 3202.83 2371.32 2.91987 3.09159 -0.171726 3.80666 6.72324 
1976 46 984.64 3328.13 2343.49 2.99328 3.11867 -0.12539 3.82864 6.89228 
1977 47 890.07 3453.42 2563.35 2.94942 3.14574 -0.196317 3.85015 6.7913 
1978 48 862.27 3578.72 2716.45 2.93564 3.17281 -0.237171 3.8712 6.75957 
1979 49 846.42 3704.01 2857.59 2.92759 3.19989 -0.272302 3.89182 6.74102 
1980 50 935.32 3829.31 2893.99 2.97096 3.22696 -0.256001 3.91202 6.84089 
1981 51 952.34 3954.6 3002.26 2.97879 3.25404 -0.275243 3.93183 6.85892 
1982 52 808.6 4079.9 3271.3 2.90773 3.28111 -0.373375 3.95124 6.6953 
1983 53 1199.22 4205.19 3005.97 3.0789 3.30818 -0.229283 3.97029 7.08943 
1984 54 1115.28 4330.49 3215.21 3.04738 3.33526 -0.287872 3.98898 7.01686 
1985 55 1347.45 4455.78 3108.33 3.12951 3.36233 -0.232817 4.00733 7.20597 
1986 56 1775.31 4581.07 2805.76 3.24927 3.3894 -0.140129 4.02535 7.48173 
1987 57 2572.07 4706.37 2134.3 3.41028 3.41648 -0.00619381 4.04305 7.85247 
1988 58 2128.73 4831.66 2702.93 3.32812 3.44355 -0.11543 4.06044 7.66328 
1989 59 2660.66 4956.96 2296.3 3.42499 3.47062 -0.0456344 4.07754 7.88633 
1990 60 2905.2 5082.25 2177.05 3.46318 3.4977 -0.0345213 4.09434 7.97426 
1991 61 3024.82 5207.55 2182.73 3.4807 3.52477 -0.0440714 4.11087 8.01461 
1992 62 3393.78 5332.84 1939.06 3.53068 3.55184 -0.0211608 4.12713 8.1297 
1993 63 3539.47 5458.14 1918.67 3.54894 3.57892 -0.0299799 4.14313 8.17173 
1994 64 3764.5 5583.43 1818.93 3.57571 3.60599 -0.0302844 4.15888 8.23337 
1995 65 4708.47 5708.73 1000.26 3.67288 3.63307 0.0398145 4.17439 8.45712 
1996 66 5528.91 5834.02 305.11 3.74264 3.66014 0.0825007 4.18965 8.61775 
1997 67 8222.61 5959.31 -2263.3 3.91501 3.68721 0.227797 4.20469 9.01464
1998 68 8883.29 6084.61 -2798.68 3.94857 3.71429 0.234288 4.21951 9.09193 
1999 69 10655.1 6209.9 -4445.25 4.02756 3.74136 0.2862 4.23411 9.2738 
2000 70 10522 6335.2 -4186.78 4.0221 3.76843 0.253664 4.2485 9.26122 
2001 71 10522.8 6460.49 -4062.32 4.02213 3.79551 0.226625 4.26268 9.2613 
2002 72 8736.59 6585.79 -2150.8 3.94134 3.82258 0.118762 4.27667 9.07528 
2003 73 9233.8 6711.08 -2522.72 3.96538 3.84965 0.115727 4.29046 9.13063 
2004 74 10139.7 6836.38 -3303.33 4.00603 3.87673 0.129298 4.30407 9.22421 
2005 75 10640.9 6961.67 -3679.24 4.02698 3.9038 0.123178 4.31749 9.27246 
2006 76 11185.7 7086.97 -4098.71 4.04866 3.93087 0.117788 4.33073 9.32239 
2007 77 13212 7212.26 -5999.73 4.12097 3.95795 0.16302 4.34381 9.48888 
2008 78 11378 7337.55 -4040.47 4.05607 3.98502 0.0710448 4.35671 9.33944 
2009 79 9171.61 7462.85 -1708.76 3.96245 4.0121 -0.0496499 4.36945 9.12387 
2010 80 10465.9 7588.14 -2877.8 4.01978 4.03917 -0.0193908 4.38203 9.25588
Graphs and Questions
1. Describe the association between “DJIA price” and “Years 
Since 1930”. 
 There is a strong positive linear relationship between the two 
variables
2. What is the equation for your linear model? (Use 
descriptive variables) 
 Dow price=125.3(since 1930)-2.4425 
3. Interpret the slope of the line in context. 
 As the “years since” increases the “Dow Price” also 
increases. 
4. Does the y-intercept of your model have a meaningful 
interpretation or is it just a hypothetical base value? 
Explain. 
 The y-intercept is the Dow price over 80 years. It is a 
meaningful interpretation these are numbers from the 
stock market there is always a meaning behind those 
numbers.
5. Look at the residuals plot for your linear 
model. Do you have any concerns about 
predictions made by your 
model? Explain. 
 No, the residual plot looks exactly like 
the linear model the only difference is the 
direction they are facing.
6. What is the equation of your new model? (Use descriptive 
variables) 
 Transformation Dow price=0.02707(since 1930)-50.38 
7. Interpret the slope of the line in context. 
• As the “years since” increases the “Dow Price” also 
increases. 
8. This time, does the y-intercept of your model have a meaningful 
interpretation? Explain. 
• Yes it’s the same data it’s just a transformation of the data
9. The residuals plot for your 
transformed model still doesn’t look 
perfect, but has it improved? How 
do you feel about the 
appropriateness of your new 
model? 
• It has improved. Its looks like the 
linear model so it’s appropriate to 
use.
Collection 1 
Transformation_Dow_Price 
Year 0.972085 
S1 = correlation 
10. What is the correlation for your transformed data? What does this indicate about the 
association? 
 The correlation is 0.97 there is a strong positive association 
11. What is R2 for your transformed data? Interpret this value in context. 
 R2 is 0.94 and that tells us that 94% of the variation in y is explained by the variation in x 
12. Use your model to make a prediction about the Dow price in July of 2012. 
 The predicted Dow price for July 2012 is 252101.1575 
13. You will most likely retire sometime between 2040 and 2050. What does your model predict 
for the Dow price in 2045? Comment on the appropriateness of this prediction. 
• The predicted Dow price for 2045 is 256236.0575 that prediction is fairly appropriate based on 
the fact that as the years go by the predicted Dow price increases.
14. What is the equation of the exponential model that Microsoft 
Excel fit to the original data? 
 ln Dow price= 0.0623(x)+4.3 
15. Use the exponential model to make a prediction about the Dow 
price in 2012. Compare it to the prediction made by your 
model. Are they close? 
 The prediction made by the exponential model is 129.6476. No 
they are not close. 
16. Calculate the y-intercept of your model and the y-intercept of the 
exponential model. Are they close? Are these predictions lower 
or higher than the actual Dow price on that date? 
• Y intercept for linear model (-244250) 
• Y intercept for exponential model (116) 
• They are not close 
• These predictions are lower than the actual Dow price
17. Recently, concerns about the U.S. economy, unemployment 
rate, national debt, foreign relations, the world economy, 
financial troubles in countries like Greece and China, climate 
change, and population expansion, among others have led 
many to question whether common stocks will continue to 
grow at 10-12% as we move into the future. Soon, you will 
have finished college, secured a position in a fulfilling 
career, and started earning a rewarding salary. You, too, will 
have to make decisions about the best way to invest your 
hard earned money in order to insure that you have a healthy 
nest egg to retire on. You’ve just studied the trend of the 
broader market over an 80-year period that included 
numerous wars, periods of political unrest, economic 
recessions, energy crises, population shifts, and corporate 
scandals (just to name a few). So, are you convinced? How 
do you feel about the strength of this trend? Will the market 
continue to reward you the way it rewarded long-term 
investors of the previous century? Or, will these new 
troubling developments send you seeking other methods of 
investment? Explain. 
I am convinced. Even with the new 
troubling developments I feel that there 
will still be a strong trend in the future 
because this isn’t the first time that there 
has been problems facing the economy. 
The market is never down for to long and 
I am confident that it will continue to 
reward myself and future investors like it 
has for the previous.
Linear 
Exponential 
Power 
The exponential graph best fits the data gathered in this study.

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Linear regression an 80 year study of the dow jones industrial average

  • 1. Linear Regression- An 80 Year study of the Dow Jones Industrial Average Tehya Singleton Rivers AP Statistics
  • 3. Year Since 1930 Dow Price Predicted Dow Price Residuals Transformation Dow Price Transformation Prediction Transformation Residuals ln since 1930 ln dow price 1930 0 233.99 -2435.42 -2669.41 2.3692 1.87328 0.495915 #Domain error# 5.45528 1931 1 135.39 -2310.12 -2445.51 2.13159 1.90036 0.231231 0 4.90816 1932 2 53.89 -2184.83 -2238.72 1.73151 1.92743 -0.195921 0.693147 3.98694 1933 3 90.77 -2059.54 -2150.31 1.95794 1.9545 0.00343931 1.09861 4.50833 1934 4 88.05 -1934.24 -2022.29 1.94473 1.98158 -0.0368473 1.38629 4.4779 1935 5 126.23 -1808.95 -1935.18 2.10116 2.00865 0.0925124 1.60944 4.83811 1936 6 164.86 -1683.65 -1848.51 2.21712 2.03572 0.181392 1.79176 5.1051 1937 7 184.01 -1558.36 -1742.37 2.26484 2.0628 0.202044 1.94591 5.21499 1938 8 141.2 -1433.06 -1574.26 2.14983 2.08987 0.0599637 2.07944 4.95018 1939 9 143.26 -1307.77 -1451.03 2.15612 2.11694 0.0391804 2.19722 4.96466 1940 10 126.14 -1182.47 -1308.61 2.10085 2.14402 -0.0431653 2.30259 4.83739 1941 11 128.79 -1057.18 -1185.97 2.10988 2.17109 -0.0612096 2.3979 4.85818 1942 12 105.72 -931.884 -1037.6 2.02416 2.19817 -0.174008 2.48491 4.66079 1943 13 137.25 -806.59 -943.84 2.13751 2.22524 -0.0877265 2.56495 4.9218 1944 14 146.11 -681.295 -827.405 2.16468 2.25231 -0.0876325 2.63906 4.98436 1945 15 162.88 -556.001 -718.881 2.21187 2.27939 -0.0675183 2.70805 5.09301 1946 16 201.56 -430.706 -632.266 2.3044 2.30646 -0.00205528 2.77259 5.30609 1947 17 183.18 -305.412 -488.592 2.26288 2.33353 -0.0706552 2.83321 5.21047 1948 18 181.33 -180.117 -361.447 2.25847 2.36061 -0.102137 2.89037 5.20032 1949 19 175.92 -54.8228 -230.743 2.24532 2.38768 -0.142365 2.94444 5.17003 1950 20 209.4 70.4717 -138.928 2.32098 2.41475 -0.0937773 2.99573 5.34425 1951 21 257.86 195.766 -62.0938 2.41138 2.44183 -0.0304436 3.04452 5.55242 1952 22 279.56 321.061 41.5008 2.44648 2.4689 -0.0224261 3.09104 5.63322 1953 23 275.38 446.355 170.975 2.43993 2.49597 -0.0560423 3.13549 5.61815 1954 24 347.92 571.65 223.73 2.54148 2.52305 0.0184311 3.17805 5.85197 1955 25 465.85 696.944 231.094 2.66825 2.55012 0.118124 3.21888 6.14386 1956 26 517.81 822.239 304.429 2.71417 2.5772 0.136975 3.2581 6.24961 1957 27 508.52 947.533 439.013 2.70631 2.60427 0.102039 3.29584 6.2315 1958 28 502.99 1072.83 569.838 2.70156 2.63134 0.0702167 3.3322 6.22057 1959 29 674.88 1198.12 523.242 2.82923 2.65842 0.17081 3.3673 6.51453 1960 30 616.73 1323.42 706.687 2.7901 2.68549 0.104605 3.4012 6.42443 1961 31 705.37 1448.71 743.341 2.84842 2.71256 0.135854 3.43399 6.55872 1962 32 597.93 1574.01 976.076 2.77665 2.73964 0.0370134 3.46574 6.39347 1963 33 695.43 1699.3 1003.87 2.84225 2.76671 0.0755429 3.49651 6.54453
  • 4. 1964 34 841.1 1824.6 983.495 2.92485 2.79378 0.131063 3.52636 6.73471 1965 35 881.74 1949.89 1068.15 2.94534 2.82086 0.124483 3.55535 6.7819 1966 36 847.38 2075.18 1227.8 2.92808 2.84793 0.0801469 3.58352 6.74215 1967 37 904.24 2200.48 1296.24 2.95628 2.875 0.0812788 3.61092 6.80709 1968 38 883 2325.77 1442.77 2.94596 2.90208 0.0438822 3.63759 6.78333 1969 39 815.47 2451.07 1635.6 2.91141 2.92915 -0.0177441 3.66356 6.70376 1970 40 734.12 2576.36 1842.24 2.86577 2.95623 -0.0904586 3.68888 6.59867 1971 41 858.43 2701.66 1843.23 2.9337 2.9833 -0.0495944 3.71357 6.75511 1972 42 924.74 2826.95 1902.21 2.96602 3.01037 -0.0443532 3.73767 6.82951 1973 43 926.4 2952.25 2025.85 2.9668 3.03745 -0.0706479 3.7612 6.83131 1974 44 757.43 3077.54 2320.11 2.87934 3.06452 -0.185177 3.78419 6.62993 1975 45 831.51 3202.83 2371.32 2.91987 3.09159 -0.171726 3.80666 6.72324 1976 46 984.64 3328.13 2343.49 2.99328 3.11867 -0.12539 3.82864 6.89228 1977 47 890.07 3453.42 2563.35 2.94942 3.14574 -0.196317 3.85015 6.7913 1978 48 862.27 3578.72 2716.45 2.93564 3.17281 -0.237171 3.8712 6.75957 1979 49 846.42 3704.01 2857.59 2.92759 3.19989 -0.272302 3.89182 6.74102 1980 50 935.32 3829.31 2893.99 2.97096 3.22696 -0.256001 3.91202 6.84089 1981 51 952.34 3954.6 3002.26 2.97879 3.25404 -0.275243 3.93183 6.85892 1982 52 808.6 4079.9 3271.3 2.90773 3.28111 -0.373375 3.95124 6.6953 1983 53 1199.22 4205.19 3005.97 3.0789 3.30818 -0.229283 3.97029 7.08943 1984 54 1115.28 4330.49 3215.21 3.04738 3.33526 -0.287872 3.98898 7.01686 1985 55 1347.45 4455.78 3108.33 3.12951 3.36233 -0.232817 4.00733 7.20597 1986 56 1775.31 4581.07 2805.76 3.24927 3.3894 -0.140129 4.02535 7.48173 1987 57 2572.07 4706.37 2134.3 3.41028 3.41648 -0.00619381 4.04305 7.85247 1988 58 2128.73 4831.66 2702.93 3.32812 3.44355 -0.11543 4.06044 7.66328 1989 59 2660.66 4956.96 2296.3 3.42499 3.47062 -0.0456344 4.07754 7.88633 1990 60 2905.2 5082.25 2177.05 3.46318 3.4977 -0.0345213 4.09434 7.97426 1991 61 3024.82 5207.55 2182.73 3.4807 3.52477 -0.0440714 4.11087 8.01461 1992 62 3393.78 5332.84 1939.06 3.53068 3.55184 -0.0211608 4.12713 8.1297 1993 63 3539.47 5458.14 1918.67 3.54894 3.57892 -0.0299799 4.14313 8.17173 1994 64 3764.5 5583.43 1818.93 3.57571 3.60599 -0.0302844 4.15888 8.23337 1995 65 4708.47 5708.73 1000.26 3.67288 3.63307 0.0398145 4.17439 8.45712 1996 66 5528.91 5834.02 305.11 3.74264 3.66014 0.0825007 4.18965 8.61775 1997 67 8222.61 5959.31 -2263.3 3.91501 3.68721 0.227797 4.20469 9.01464
  • 5. 1998 68 8883.29 6084.61 -2798.68 3.94857 3.71429 0.234288 4.21951 9.09193 1999 69 10655.1 6209.9 -4445.25 4.02756 3.74136 0.2862 4.23411 9.2738 2000 70 10522 6335.2 -4186.78 4.0221 3.76843 0.253664 4.2485 9.26122 2001 71 10522.8 6460.49 -4062.32 4.02213 3.79551 0.226625 4.26268 9.2613 2002 72 8736.59 6585.79 -2150.8 3.94134 3.82258 0.118762 4.27667 9.07528 2003 73 9233.8 6711.08 -2522.72 3.96538 3.84965 0.115727 4.29046 9.13063 2004 74 10139.7 6836.38 -3303.33 4.00603 3.87673 0.129298 4.30407 9.22421 2005 75 10640.9 6961.67 -3679.24 4.02698 3.9038 0.123178 4.31749 9.27246 2006 76 11185.7 7086.97 -4098.71 4.04866 3.93087 0.117788 4.33073 9.32239 2007 77 13212 7212.26 -5999.73 4.12097 3.95795 0.16302 4.34381 9.48888 2008 78 11378 7337.55 -4040.47 4.05607 3.98502 0.0710448 4.35671 9.33944 2009 79 9171.61 7462.85 -1708.76 3.96245 4.0121 -0.0496499 4.36945 9.12387 2010 80 10465.9 7588.14 -2877.8 4.01978 4.03917 -0.0193908 4.38203 9.25588
  • 7. 1. Describe the association between “DJIA price” and “Years Since 1930”.  There is a strong positive linear relationship between the two variables
  • 8. 2. What is the equation for your linear model? (Use descriptive variables)  Dow price=125.3(since 1930)-2.4425 3. Interpret the slope of the line in context.  As the “years since” increases the “Dow Price” also increases. 4. Does the y-intercept of your model have a meaningful interpretation or is it just a hypothetical base value? Explain.  The y-intercept is the Dow price over 80 years. It is a meaningful interpretation these are numbers from the stock market there is always a meaning behind those numbers.
  • 9. 5. Look at the residuals plot for your linear model. Do you have any concerns about predictions made by your model? Explain.  No, the residual plot looks exactly like the linear model the only difference is the direction they are facing.
  • 10. 6. What is the equation of your new model? (Use descriptive variables)  Transformation Dow price=0.02707(since 1930)-50.38 7. Interpret the slope of the line in context. • As the “years since” increases the “Dow Price” also increases. 8. This time, does the y-intercept of your model have a meaningful interpretation? Explain. • Yes it’s the same data it’s just a transformation of the data
  • 11. 9. The residuals plot for your transformed model still doesn’t look perfect, but has it improved? How do you feel about the appropriateness of your new model? • It has improved. Its looks like the linear model so it’s appropriate to use.
  • 12. Collection 1 Transformation_Dow_Price Year 0.972085 S1 = correlation 10. What is the correlation for your transformed data? What does this indicate about the association?  The correlation is 0.97 there is a strong positive association 11. What is R2 for your transformed data? Interpret this value in context.  R2 is 0.94 and that tells us that 94% of the variation in y is explained by the variation in x 12. Use your model to make a prediction about the Dow price in July of 2012.  The predicted Dow price for July 2012 is 252101.1575 13. You will most likely retire sometime between 2040 and 2050. What does your model predict for the Dow price in 2045? Comment on the appropriateness of this prediction. • The predicted Dow price for 2045 is 256236.0575 that prediction is fairly appropriate based on the fact that as the years go by the predicted Dow price increases.
  • 13. 14. What is the equation of the exponential model that Microsoft Excel fit to the original data?  ln Dow price= 0.0623(x)+4.3 15. Use the exponential model to make a prediction about the Dow price in 2012. Compare it to the prediction made by your model. Are they close?  The prediction made by the exponential model is 129.6476. No they are not close. 16. Calculate the y-intercept of your model and the y-intercept of the exponential model. Are they close? Are these predictions lower or higher than the actual Dow price on that date? • Y intercept for linear model (-244250) • Y intercept for exponential model (116) • They are not close • These predictions are lower than the actual Dow price
  • 14. 17. Recently, concerns about the U.S. economy, unemployment rate, national debt, foreign relations, the world economy, financial troubles in countries like Greece and China, climate change, and population expansion, among others have led many to question whether common stocks will continue to grow at 10-12% as we move into the future. Soon, you will have finished college, secured a position in a fulfilling career, and started earning a rewarding salary. You, too, will have to make decisions about the best way to invest your hard earned money in order to insure that you have a healthy nest egg to retire on. You’ve just studied the trend of the broader market over an 80-year period that included numerous wars, periods of political unrest, economic recessions, energy crises, population shifts, and corporate scandals (just to name a few). So, are you convinced? How do you feel about the strength of this trend? Will the market continue to reward you the way it rewarded long-term investors of the previous century? Or, will these new troubling developments send you seeking other methods of investment? Explain. I am convinced. Even with the new troubling developments I feel that there will still be a strong trend in the future because this isn’t the first time that there has been problems facing the economy. The market is never down for to long and I am confident that it will continue to reward myself and future investors like it has for the previous.
  • 15. Linear Exponential Power The exponential graph best fits the data gathered in this study.