Rahul5077only!!! 5-1 quantitative

profilepcwc0506
m4a1.xlsx
Home>Mathematics homework help>Numerical analysis homework help>Rahul5077only!!! 5-1 quantitative

Question 1

1. The Excel file Closing Stock Prices provides data for four stocks over a one-month period.
a. Develop spreadsheet models for forecasting each of the stock prices using single moving average and single exponential smoothing.
b. Using MAD, MSE, and MAPE as guidance, find the best number of moving average periods and best smoothing constant for exponential smoothing.

1 - Closing Stock Prices

Closing Stock Prices
Date IBM INTC CSCO GE DJ Industrials
9/3/10 127.58 18.43 21.04 15.392 10447.93
9/7/10 125.95 18.12 20.58 15.44 10340.69
9/8/10 126.08 17.9 20.64 15.7 10387.01
9/9/10 126.36 18 20.61 15.91 10415.24
9/10/10 127.99 17.97 20.62 15.98 10462.77
9/13/10 129.61 18.557 21.26 16.25 10544.13
9/14/10 128.85 18.74 21.45 16.16 10526.49
9/15/10 129.43 18.72 21.59 16.34 10572.73
9/16/10 129.67 18.97 21.93 16.23 10594.83
9/17/10 130.19 18.81 21.863 16.29 10607.85
9/20/10 131.79 18.93 21.75 16.55 10753.62
9/21/10 131.98 19.14 21.64 16.52 10761.03
9/22/10 132.57 19.01 21.67 16.5 10739.31
9/23/10 131.67 18.98 21.53 16.14 10662.42
9/24/10 134.11 19.423 22.09 16.66 10860.26
9/27/10 134.65 19.235 22.11 16.43 10812.04
9/28/10 134.89 19.505 21.863 16.44 10858.14
9/29/10 135.48 19.24 21.87 16.36 10835.28
9/30/10 134.14 19.2 21.9 16.25 10788.05
10/1/10 135.64 19.32 21.91 16.36 10829.68

Question 3

3. For the data in the Excel file Ohio Prison Population do the following:
a. Develop spreadsheet models forecasting both male and female populations using single moving average and single exponential smoothing.
b. Using MAD, MSE, and MAPE as guidance, find the best number of moving average periods and best smoothing constant for exponential smoothing.

3 - Ohio Prison Population

Ohio Prison Population Data
Male Male Female Female Total Total
Year Intake Population Intake Population Intake Population
1972 4677 8846 236 275 4913 9121
1973 4635 8049 253 271 4888 8320
1974 5538 7449 367 268 5905 7717
1975 7014 8978 442 348 7456 9326
1976 6859 10985 493 436 7352 11421
1977 6317 12086 627 542 6944 12628
1978 5993 12269 558 577 6551 12846
1979 6849 12768 583 582 7432 13350
1980 7698 12762 631 598 8329 13360
1981 9046 12557 792 581 9838 13138
1982 9530 14071 919 725 10449 14796
1983 9293 16272 917 875 10210 17147
1984 8855 16795 780 971 9635 17766
1985 9132 17382 868 969 10000 18351
1986 9436 19416 1002 1123 10438 20539
1987 9871 20981 1071 1194 10942 22175
1988 11170 22659 1296 1284 12466 23943
1989 14673 24373 1833 1476 16506 25849
1990 15423 28346 1986 1954 17409 30300
1991 17450 29605 2196 1896 19646 31501
1992 18209 33189 2385 2257 20594 35446
1993 17460 35627 2374 2364 19834 37991
1994 16546 37715 2346 2538 19198 40253
1995 17395 39065 2520 2544 19915 41609
1996 16753 41595 2431 2743 19184 44338
1997 15589 43183 2011 2779 17600 45962
1998 16076 44997 2177 2811 18253 47808
1999 16122 45285 2203 2886 18325 48171
2000 17334 43813 2387 2806 19721 46619
A change in policy in 1994 no longer counts inmates AWL to court as prison population

Female Population 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 275 271 268 348 436 542 577 582 598 581 725 875 971 969 1123 1194 1284 1476 1954 1896 2257 2364 2538 2544 2743 2779 2811 2886 2806

Male Population 1972 1973 1974 1975 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 8846 8049 7449 8978 10985 12086 12269 12768 12762 12557 14071 16272 16795 17382 19416 20981 22659 24373 28346 29605 33189 35627 37715 39065 41595 43183 44997 45285 43813

Question 6

6. Consider the data in the Excel file Consumer Price Index.
a. Use simple linear regression to forecast the data. What would be the forecast for the next two months?
b. Are the data autocorrelated? Construct first- and second-order autoregressive models and compare the results to part (a).

6 - Consumer Price Index

Consumer Price Index - All Urban Consumers Bureau of Labor Statistics
Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
2000 169.300 170.000 171.000 170.900 171.200 172.200 172.700 172.700 173.600 173.900 174.200 174.600
2001 175.600 176.000 176.100 176.400 177.300 177.700 177.400 177.400 178.100 177.600 177.500 177.400
2002 177.700 178.000 178.500 179.300 179.500 179.600 180.000 180.500 180.800 181.200 181.500 181.800
2003 182.600 183.600 183.900 183.200 182.900 183.100 183.700 184.500 185.100 184.900 185.000 185.500
2004 186.300 186.700 187.100 187.400 188.200 188.900 189.100 189.200 189.800 190.800 191.700 191.700
2005 191.600 192.400 193.100 193.700 193.600 193.700 194.900 196.1000 198.800 199.100 198.100 198.100
2006 199.200 199.400 199.700 200.600 201.400 201.900 202.900 203.700 202.900 201.800 202.000 203.100
2007 203.372 204.258 205.312 205.959 206.850 207.202 207.651 207.671 208.503 209.073 210.740 211.434
2008 212.225 212.703 213.543 214.106 215.287 217.279 219.102 218.779 218.846 216.832 212.923 211.339
2009 211.959 212.877 212.643 212.810 213.050 214.558 214.774 215.566 215.911 216.357 216.859 217.224
2010 217.587 217.591 217.729 217.579 217.224 216.929 217.597 218.150

Question 14

14. Data in the Excel file Microprocessor Data shows the demand for one type of chip used in industrial equipment from a small manufacturer.
a. Construct a chart of the data. What appears to happen when a new chip is introduced?
b. Develop a causal regression model to forecast demand that includes both time and the introduction of a new chip as explanatory variables.
c. What would the forecast be for the next month if a new chip is introduced? What would it be if a new chip is not introduced?

14 - Microprocessor Data

Microprocessor Data
Demand Month New Chip Introduced
3949 1 0
3544 2 0
6138 3 0
7752 4 0
10700 5 1
7766 6 0
5482 7 0
8789 8 0
13103 9 1
7326 10 0
8669 11 0
7219 12 0
7456 13 0
9744 14 0
14561 15 1
8564 16 0
12214 17 0
19020 18 1
14751 19 0
14269 20 0
14896 21 0
15359 22 0
16527 23 0
17029 24 0