Business Forecasting;Using the United Kingdom statistics locates Banks consumer credit: Gross lending figures from 1993-2003

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1. Examine the data by plotting and/or otherwise for seasonal effects, trends and cycles.

Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1 454 61 454 4405 4788 4576 41 45 478714 41 80 487 46 4657 457 464 551 48 4688 505 57

15 481 4571 5485 508 56 5678 5587 6116 556 576 5781 5855

16 576 5448 577 650 658 60 7066 6841 6480 617 6468 7161

17 6 6088 646 78 700 747 7 7516 750 74 71 8751

18 7516 77 86 8180 87 884 17 8606 4 878 80 445

1 8107 84 767 88 8511 874 85 80 87 460 1057 10450

000 50 1006 108 4 11156 10806 1077 1088 06 10857 108 1070

001 1114 100 1106 117 1188 1171 1 15 1158 147 184 165

00 176 1165 167 184 14085 111 147 115 18 144 14 1

00 10 110

Resource http//www.statistics.gov.uk/statbase/TSDtimezone.asp (Accessed /04/0)

The figure shows Banks consumer credit Gross lending on a monthly basis starting with the first month of 14 and ending with the twelfth month of 00. And we will use the data in 00 to check on the forecast.

As seen from the graph above, the time series data moves upward over a period of time so it can be said that the data shows a fairly strongly positive trend, so that we might expect high autocorrelation coefficients. Considering ACF, it supports this expectation because it is significantly different from zero. Autocorrelation at lag 1 is 0.45 and move downwards to 0.581 at lag 16.

Afterwards, we will choose data from 14-001 to model.

. Try a classical decomposition method on part of the data and check it against the rest of the data.

The ACF of the actual data shows that there is a trend or cycle or both, so we should look at the ACF of the first difference.

From the ACF of the first difference, we could use the 1 points moving average to smooth the data. Subsequently, we will calculate the twelve point moving average, seasonal estimate, trend-cycle estimate and forecast based on the trend-cycle and the seasonal ratios.

After calculating center moving average and getting seasonal estimate (ratio), we will find out the factors as following table

Sequence Mean Count

1 0.5 7

0.1 7

1.00 7

4 0.8 7

5 1.01 7

6 1.0 7

7 1.04 7

8 1.05 7

1.00 7

10 1.00 7

11 1.00 7

1 1.04 7

Then we can de-seasonalise the data by dividing the factors into the data to create a trend cycle column. Next, we regress the trend cycle against the time and will get a trend column.

Coefficients

Unstandardized Coefficients Standardized Coefficients t Sig.

Model B Std. Error Beta

1 (Constant) 45.164 84.847 46.48 0.000

TIME 84.57 1.51 0.85 55.56 0.000

a Dependent Variable TRENCYC

From the coefficients table above, the trend equation is

Trend = 45.164+84.57 Time

Then we can create a moving average forecasting column with the factors and trend column. The moving average forecasting equation is

Forecast = Trend Factors

Therefore, it can be implied that

Forecast = (45.164+84.57 Time) Factors

Moreover, the errors will be calculated by deducting the forecast data from the actual data in order to see how accurate the forecast is.

Errors = Data Forecast

See the figures of the result as following table

Time Data Center Moving Average Ratios Sequence Factors Trend-cycle Trend line Forecast Errors

14 Jan 1 41 0.5 446. 40.5 88.04 00.6

Feb 80 0.1 47.6 411.88 74.6 6.7

Mar 487 1.00 487.00 418.4 418.4 680.76

Apr 4 46 0.8 4485.71 48.5 416.4 1.06

May 5 4657 1.01 4610.8 466.5 4410.6 46.8

Jun 6 457 1.0 485.80 4451.1 4540. 416.67

Jul 7 464 4804.00 0.8 7 1.04 451.46 455.66 4717.0 -.0

Aug 8 551 4857.1 1.14 8 1.05 550.48 455.66 4851.0 661.8

Sep 48 407.00 1.0 1.00 550.48 4704.8 4704.8 84.6

Oct 10 4688 461.50 0.4 10 1.00 4688.00 4788.7 4788.7 -100.7

Nov 11 505 50.88 1.00 11 1.00 505.00 487.0 487.0 178.1

Dec 1 57 5107.04 1.05 1 1.04 5165.8 457.45 5155.75 16.5

15 Jan 1 481 5107.04 0. 1 0.5 5066. 5041.81 478.71 .

Feb 14 4571 56.6 0.87 1 0.1 50.08 516.16 4664.81 -.81

Mar 15 5485 585.6 1.04 1.00 5485.00 510.5 510.5 74.48

Apr 16 508 554. 0.5 4 0.8 50.04 54.88 5188.8 -0.8

May 17 56 54.46 1.05 5 1.01 50.04 57. 54.0 58.7

Jun 18 5678 547.6 1.04 6 1.0 5566.67 546.5 557.86 105.14

Jul 1 5587 55.6 1.01 7 1.04 57.1 5547.5 576.86 -18.86

Aug 0 6116 5615.71 1.0 8 1.05 584.76 56.0 51. 0.08

Sep 1 556 5664.5 0.8 1.00 556.00 5716.66 5716.66 -154.66

Oct 576 578.4 1.01 10 1.00 576.00 5801.0 5801.0 -8.0

Nov 5781 5817.67 0. 11 1.00 5781.00 5885.8 5885.8 -104.8

Dec 4 5855 586.54 1.00 1 1.04 56.81 56.7 608.5 -5.5

16 Jan 5 576 545.6 0.7 1 0.5 6065.6 6054.0 5751.8 10.6

Feb 6 5448 607.7 0.0 0.1 586.81 618.45 5751.8 -17.

Mar 7 577 6106.5 0.5 1.00 577.00 6.80 6.80 -44.80

Apr 8 650 61.58 1.0 4 0.8 647.5 607.16 6181.0 168.8

May 658 66. 1.05 5 1.01 6516.8 61.5 6455.4 16.57

Jun 0 60 65. 0.5 6 1.0 514.71 6475.87 6605. -57.

Jul 1 7066 64.00 1.10 7 1.04 674. 6560. 68.64 4.6

Aug 6841 6485. 1.05 8 1.05 6515.4 6644.5 676.8 -15.8

Sep 6480 65. 0. 1.00 6480.00 678.5 678.5 -48.4

Oct 4 617 655.00 1.05 10 1.00 617.00 681.0 681.0 10.70

Nov 5 6468 6655.7 0.7 11 1.00 6468.00 687.66 687.66 -4.66

Dec 6 7161 67.58 1.06 1 1.04 6885.58 68.0 761.0 -100.0

17 Jan 7 6 68. 0.4 1 0.5 678.4 7066.7 671.05 -1.05

Feb 8 6088 68.1 0.88 0.1 660.11 7150.7 6507.16 -41.16

Mar 646 664.58 0.0 1.00 646.00 75.0 75.0 -8.0

Apr 40 78 7050.67 1.05 4 0.8 75.65 71.44 717.06 08.4

May 41 700 711.88 0.8 5 1.01 6.60 740.80 7477.84 -468.84

Jun 4 747 76.58 1.0 6 1.0 76.47 7488.16 767. -164.

Jul 4 7 74.67 1.08 7 1.04 7618.7 757.5 7875.4 47.58

Aug 44 7516 7448.54 1.01 8 1.05 7158.10 7656.87 80.7 -5.7

Sep 45 750 7588.7 0. 1.00 750.00 7741. 7741. -1.

Oct 46 74 7710.5 1.0 10 1.00 74.00 785.5 785.5 117.41

Nov 47 71 774.5 0.5 11 1.00 71.00 70.4 70.4 -518.4

Dec 48 8751 707.6 1.11 1 1.04 8414.4 74.0 814.07 46.

18 Jan 4 7516 801.08 0.4 1 0.5 711.58 8078.66 7674.7 -158.7

Feb 50 77 8116.67 0.0 0.1 806.64 816.01 748.4 -1.4

Mar 51 86 8.8 1.0 1.00 86.00 847.7 847.7 115.6

Apr 5 8180 88. 0.8 4 0.8 846.4 81.7 8165.0 14.1

May 5 87 844.71 0.8 5 1.01 8145.54 8416.0 8500.5 -7.5

Jun 54 884 857. 1.05 6 1.0 8807.84 8500.44 8670.45 1.55

Jul 55 17 8580.88 1.06 7 1.04 8775.6 8584.80 88.1 18.81

Aug 56 8606 864.46 1.00 8 1.05 816.1 866.16 10.61 -46.61

Sep 57 4 877. 1.06 1.00 4.00 875.51 875.51 488.4

Oct 58 878 88.6 0. 10 1.00 878.00 887.87 887.87 -10.87

Nov 5 80 886.67 1.01 11 1.00 80.00 8. 8. -.

Dec 60 445 811.58 1.06 1 1.04 081.7 006.58 66.85 78.15

1 Jan 61 8107 878.88 0.0 1 0.5 85.68 00.4 866. -5.

Feb 6 84 060.08 0.1 0.1 07.6 175.0 84.5 -15.5

Mar 6 767 14.1 1.07 1.00 767.00 5.66 5.66 507.4

Apr 64 88 0.67 0.6 4 0.8 01.7 44.01 157.1 -4.1

May 65 8511 04.04 0.1 5 1.01 846.7 48.7 5.65 -1011.65

Jun 66 874 415.7 1.05 6 1.0 680. 51.7 70.8 171.0

Jul 67 85 516.6 1.04 7 1.04 47.08 57.08 80.7 -18.7

Aug 68 80 651. 1.0 8 1.05 61.0 681.44 10165.51 -5.51

Sep 6 87 770.8 1.0 1.00 87.00 765.80 765.80 1.0

Oct 70 460 86.50 0.6 10 1.00 460.00 850.15 850.15 -0.15

Nov 71 1057 67.6 1.06 11 1.00 1057.00 4.51 4.51 66.4

Dec 7 10450 10117.00 1.0 1 1.04 10048.08 10018.87 1041.6 0.8

000 Jan 7 50 1014.1 0. 1 0.5 1001.58 1010. 58.06 -68.06

Feb 74 1006 1076.4 0.8 0.1 11017.58 10187.58 70.70 755.0

Mar 75 108 1016.88 1.05 1.00 108.00 1071.4 1071.4 561.06

Apr 76 4 1071.71 0.0 4 0.8 5.67 1056.0 1014.17 -806.17

May 77 11156 1044.1 1.07 5 1.01 11045.54 10440.65 10545.06 610.4

Jun 78 10806 10465. 1.0 6 1.0 1054.1 1055.01 1075.51 70.4

Jul 7 1077 1054. 1.0 7 1.04 1058.65 1060.7 110.74 -60.74

Aug 80 1088 10610.8 1.0 8 1.05 106.81 106.7 118.41 -46.41

Sep 81 06 1061.7 0. 1.00 06.00 10778.08 10778.08 -87.08

Oct 8 10857 10707.8 1.01 10 1.00 10857.00 1086.44 1086.44 -5.44

Nov 8 108 10817.5 1.01 11 1.00 108.00 1046.80 1046.80 -54.80

Dec 84 1070 1088.1 0.8 1 1.04 107.1 1101.15 1147.40 -76.40

001 Jan 85 1114 1100.08 1.01 1 0.5 117.47 11115.51 1055.7 58.7

Feb 86 100 11170.04 0.0 0.1 11014. 111.87 1011.88 -168.88

Mar 87 1106 110.67 0.8 1.00 1106.00 1184. 1184. -.

Apr 88 117 11450.75 0.8 4 0.8 11456.1 1168.58 11141.1 85.7

May 8 1188 1161.88 1.0 5 1.01 11780.0 1145.4 11567.47 0.5

Jun 0 1171 117.67 1.00 6 1.0 1155.80 1157. 11768.04 .6

Jul 1 1 1.04 141.7 1161.65 1086.5 84.48

Aug 15 1.05 1178.10 11706.01 11.1 .6

Sep 1158 1.00 1158.00 1170.7 1170.7 -4.6

Oct 4 147 1.00 147.00 11874.7 11874.7 17.8

Nov 5 184 1.00 184.00 115.08 115.08 88.

Dec 6 165 1.04 114.04 104.44 155.17 10.8

Then, we can plot the decomposition forecasting against the actual data to illustrate what is happening.

The graph shows that the decomposition forecasting fits the actual data better at the middle. However, at the beginning and the end, there are some errors.

Comparing the forecast data with the rest of the data, we calculate them by extending the time line and see the result as following table

Rest Data Time Factors Trend Forecast Errors

00 Jan 176.00 7 0.5 117.7 1151.40 140.60

Feb 1165.00 8 0.1 11.15 1111.06 851.4

Mar 167.00 1.00 16.51 16.51 8.4

Apr 184.00 100 0.8 180.86 11.5 1850.75

May 14085.00 101 1.01 1465. 158.87 145.1

Jun 111.00 10 1.0 154.58 1800.57 -688.57

Jul 147.00 10 1.04 16.4 11. 158.71

Aug 115.00 104 1.05 1718. 154.1 -1.1

Sep 18.00 105 1.00 180.65 180.65 186.5

Oct 144.00 106 1.00 1887.01 1887.01 606.

Nov 14.00 107 1.00 171.6 171.6 -.6

Dec 1.00 108 1.04 1055.7 1577.5 44.05

00 Jan 158.00 10 0.5 1140.08 148.07 1114.

Feb 17.00 110 0.1 14.4 104. 7.77

Sum of squared errors = 1855.51

Mean-squared error (MSE) = 1108.75

Root-mean-squared error (RMSE) = 57.74

This RMSE is about 7. % of the mean for the rest of data during the forecast period

From the table above, we can notice the errors as illustrated the graph below which is plotted from rest data against forecast data of 00-00.

Although there is some significant errors, perhaps which are caused by economic uncertainty during that period, overall the plot graph below shows that the predict data relatively fit the real data.

. Try an Autocorrelation approach on the same data and discuss the differences between the results and those from the Decomposition approach. Please comment on the different from of the equation for the forecast.

After the first difference, it shows that there are seven spikes on lag1, lag, lag4, lag10, lag1, lag1, and lag14 so we will try on these lagged data to calculate correlations.

The table below shows the correlations between the actual data and lagged data

Correlations

DATA LAGS LAGS LAGS LAGS LAGS LAGS LAGS

(DATA,1) (DATA,) (DATA,4) (DATA,10) (DATA,1) (DATA,1) (DATA,14)

DATA Pearson Correlation 1 .5 .6 .5 .6 .7 .40 .54

Sig. (-tailed) . .000 .000 .000 .000 .000 .000 .000

N 6 5 86 84 8 8

LAGS(DATA,1) Pearson Correlation .5 1 .56 .6 .50 5 .7 .8

Sig. (-tailed) .000 . .000 .000 .000 .000 .000 .000

N 5 5 86 84 8 8

LAGS(DATA,) Pearson Correlation .6 .56 1 .57 .8 .5 .4 .58

Sig. (-tailed) .000 .000 . .000 .000 .000 .000 .000

N 86 84 8 8

LAGS(DATA,4) Pearson Correlation .5 .6 .57 1 .45 .51 .51 .

Sig. (-tailed) .000 .000 .000 . .000 .000 .000 .000

N 86 84 8 8

LAGS(DATA,10) Pearson Correlation .6 .50 .8 .45 1 .48 .64 .

Sig. (-tailed) .000 .000 .000 .000 . .000 .000 .000

N 86 86 86 86 86 84 8 8

LAGS(DATA,1) Pearson Correlation .7 .5 .5 .51 .48 1 .48 .45

Sig. (-tailed) 0.000 .000 .000 .000 .000 . .000 .000

N 84 84 84 84 84 84 8 8

LAGS(DATA,1) Pearson Correlation .40 .7 .4 .51 .64 .48 1 .47

Sig. (-tailed) .000 .000 .000 .000 .000 .000 . .000

N 8 8 8 8 8 8 8 8

LAGS(DATA,14) Pearson Correlation .54 .8 .58 . . .45 .47 1

Sig. (-tailed) .000 .000 .000 .000 .000 .000 .000 .

N 8 8 8 8 8 8 8 8

Correlation is significant at the 0.01 level (-tailed).

From the correlation between the data and lagged data, these are all high and then we need to use regression on the chosen columns to find the relationship.

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 LAGS(DATA,14), LAGS(DATA,4), LAGS(DATA,10), LAGS(DATA,1), LAGS(DATA,), LAGS(DATA,1), LAGS(DATA,1) . Enter

a All requested variables entered.

b Dependent Variable DATA

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 0.80 0.61 0.57 448.111

a Predictors (Constant), LAGS(DATA,14), LAGS(DATA,4), LAGS(DATA,10), LAGS(DATA,1), LAGS(DATA,), LAGS(DATA,1), LAGS(DATA,1)

This explains 5.7% of the variability and so will be reasonable fit to the data

Coefficients

Unstandardized Coefficients Standardized Coefficients t Sig.

Model B Std. Error Beta

1 (Constant) 1.580 10.57 1.85 .067

LAGS(DATA,1) 1.658E-0 .1 .017 .16 .8

LAGS(DATA,) .10 .10 .01 .04 .00

LAGS(DATA,4) 1.70E-0 .0 .00 .017 .86

LAGS(DATA,10) -.11 .10 -.11 -1.17 .4

LAGS(DATA,1) .5 .105 .547 5.66 .000

LAGS(DATA,1) 6.E-0 .14 .05 .471 .6

LAGS(DATA,14) .16 .104 .181 1.88 .064

a Dependent Variable DATA

From the coefficients table, the lag1, lag4, lag10, lag1, and lag14 data are not significant as well as the constant so the regression should be repeated only with lag and lag1 data because their significant is less than .05

Variables Entered/Removed

Model Variables Entered Variables Removed Method

1 LAGS(DATA,1), LAGS(DATA,) . Enter

a All requested variables entered.

b Dependent Variable DATA

c Linear Regression through the Origin

Model Summary

Model R R Square Adjusted R Square Std. Error of the Estimate

1 . .7 .7 44.446

a For regression through the origin (the no-intercept model), R Square measures the proportion of the variability in the dependent variable about the origin explained by regression. This CANNOT be compared to R Square for models which include an intercept.

b Predictors LAGS(DATA,1), LAGS(DATA,)

This fit has increased slightly from .7% to 6% by removing the lag1, lag4, lag10, lag1, and lag14 data as well as the constant.

Coefficients

Unstandardized Coefficients Standardized Coefficients

Model B Std. Error Beta t Sig.

1 LAGS(DATA,) .4 .07 .44 6.08 .000

LAGS(DATA,1) .651 .07 .575 8.70 .000

a Dependent Variable DATA

b Linear Regression through the Origin

All lag data are significant, so

Forecast = 0.4 (data lagged) + 0.651 (data lagged1)

See the figures of the Autocorrelation forecasting as following table

Time Data lag lag1 Forecast Error

14 Jan 1 41

Feb 80

Mar 487

Apr 4 46 41.00

May 5 4657 80.00

Jun 6 457 487.00

Jul 7 464 46.00

Aug 8 551 4657.00

Sep 48 457.00

Oct 10 4688 464.00

Nov 11 505 551.00

Dec 1 57 48.00

15 Jan 1 481 4688.00 41.00 4746.01 66.

Feb 14 4571 505.00 80.00 4808.81 -7.81

Mar 15 5485 57.00 487.00 554.54 -4.54

Apr 16 508 481.00 46.00 474.70 1.0

May 17 56 4571.00 4657.00 508.8 65.6

Jun 18 5678 5485.00 457.00 564. 4.08

Jul 1 5587 508.00 464.00 5.8 .18

Aug 0 6116 56.00 551.00 6087.75 8.5

Sep 1 556 5678.00 48.00 5740.48 -178.48

Oct 576 5587.00 4688.00 5504.58 58.4

Nov 5781 6116.00 505.00 57.78 -1.78

Dec 4 5855 556.00 57.00 58.8 -8.8

16 Jan 5 576 576.00 481.00 566. 8.78

Feb 6 5448 5781.00 4571.00 551.58 -65.58

Mar 7 577 5855.00 5485.00 6141.08 -68.08

Apr 8 650 576.00 508.00 5848. 501.68

May 658 5448.00 56.00 607.16 484.84

Jun 0 60 577.00 5678.00 60.7 -17.7

Jul 1 7066 650.00 5587.00 644.7 641.1

Aug 6841 658.00 6116.00 6871.01 -0.01

Sep 6480 60.00 556.00 66.5 10.65

Oct 4 617 7066.00 576.00 685.6 6.1

Nov 5 6468 6841.00 5781.00 6766.6 -8.6

Dec 6 7161 6480.00 5855.00 6656. 504.68

17 Jan 7 6 617.00 576.00 6787.6 -5.6

Feb 8 6088 6468.00 5448.00 686.10 -8.10

Mar 646 7161.00 577.00 601.0 -655.0

Apr 40 78 6.00 650.00 6.4 44.06

May 41 700 6088.00 658.00 657.51 51.4

Jun 4 747 646.00 60.00 666.48 80.5

Jul 4 7 78.00 7066.00 7840.66 8.4

Aug 44 7516 700.00 6841.00 750.44 -14.44

Sep 45 750 747.00 6480.00 74.1 0.87

Oct 46 74 7.00 617.00 781.16 -8.16

Nov 47 71 7516.00 6468.00 7510.1 -11.1

Dec 48 8751 750.00 7161.00 76.0 787.1

18 Jan 4 7516 74.00 6.00 7648.17 -1.17

Feb 50 77 71.00 6088.00 707.4 1.06

Mar 51 86 8751.00 646.00 707.84 455.17

Apr 5 8180 7516.00 78.00 8105.1 74.7

May 5 87 77.00 700.00 778.80 44.0

Jun 54 884 86.00 747.00 856.8 447.7

Jul 55 17 8180.00 7.00 8748.8 78.11

Aug 56 8606 87.00 7516.00 8504.57 101.4

Sep 57 4 884.00 750.00 88.50 40.50

Oct 58 878 17.00 74.00 177.65 -44.65

Nov 5 80 8606.00 71.00 858.58 0.4

Dec 60 445 4.00 8751.00 754.14 -0.14

1 Jan 61 8107 878.00 7516.00 874.51 -617.51

Feb 6 84 80.00 77.00 86.7 -468.7

Mar 6 767 445.00 86.00 50.67 176.

Apr 64 88 8107.00 8180.00 8884.15 -51.15

May 65 8511 84.00 87.00 866.11 -455.11

Jun 66 874 767.00 884.00 1016.0 -6.0

Jul 67 85 88.00 17.00 81.6 .64

Aug 68 80 8511.00 8606.00 8.84 41.16

Sep 6 87 874.00 4.00 1051. -64.

Oct 70 460 85.00 878.00 10006.6 -546.6

Nov 71 1057 80.00 80.00 101. 474.71

Dec 7 10450 87.00 445.00 105. -8.

000 Jan 7 50 460.00 8107.00 40.60 .40

Feb 74 1006 1057.00 84.00 10005.1 0.0

Mar 75 108 10450.00 767.00 1045.87 -11.87

Apr 76 4 50.00 88.00 .5 -50.5

May 77 11156 1006.00 8511.00 4.08 11.

Jun 78 10806 108.00 874.00 1118.66 -77.66

Jul 7 1077 4.00 85.00 10515. 57.77

Aug 80 1088 11156.00 80.00 116.81 -414.81

Sep 81 06 10806.00 87.00 1145.7 -1.7

Oct 8 10857 1077.00 460.00 10887.81 -0.81

Nov 8 108 1088.00 1057.00 11675.85 -78.85

Dec 84 1070 06.00 10450.00 11151.68 -44.68

001 Jan 85 1114 10857.00 50.00 1070.5 17.75

Feb 86 100 108.00 1006.00 1108.51 -185.51

Mar 87 1106 1070.00 108.00 1175.5 -61.5

Apr 88 117 1114.00 4.00 1074.07 5.

May 8 1188 100.00 11156.00 1166.65 5.5

Jun 0 1171 1106.00 10806.00 1180. -.

Jul 1 1 117 1077 1141.88 87.1

Aug 15 1188 1088 107.40 17.60

Sep 1158 1171 06 1165.06 -67.06

Oct 4 147 1 10857 174.74 50.6

Nov 5 184 15 108 1501.7 47.6

Dec 6 165 1158 1070 1157.7 677.8

Then, we can plot the autocorrelation forecasting against the actual data to illustrate what is happening.

The graph shows that the Autocorrelation forecasting fits the actual data better at the middle. However, at the end, there are some errors.

Comparing the forecast data with the rest of the data, we calculate them by extending the time line and see the result as following table

Time Data lag lag1 Forecast Error

00 Jan 7 176 147 1114 106.5 -07.5

Feb 8 1165 184 100 1165.68 -00.68

Mar 167 165 1106 1748.1 -6.1

Apr 100 184 176 117 111. 107.71

May 101 14085 1165 1188 18. 1086.77

Jun 10 111 167 1171 14.0 -110.0

Jul 10 147 184 1 14555.76 167.4

Aug 104 115 14085 15 1406.8 -1.8

Sep 105 18 111 1158 1711. 77.77

Oct 106 144 147 147 15087.1 -15.1

Nov 107 14 115 184 14166.08 -14.08

Dec 108 1 18 165 17.56 -5.56

00 Jan 10 158 144 176 141. -6.

Feb 110 17 14 1165 1470.75 -118.75

Sum of squared errors = 1071844.

Mean-squared error (MSE) = 7650.16

Root-mean-squared error (RMSE) = 874.76

This RMSE is about 6.6 % of the mean for the rest of data during the forecast period

From the table above, we can notice the errors as illustrated the graph below which is plotted from rest data against forecast data of 00-00.

However, there are some errors at the end, overall the plot graph below (During 14-00) shows that the predict data relatively fit the real data.

From the graph above, it illustrated the actual data (red line), Decomposition forecasting (blue line), and Autocorrelation forecasting (green line). Some periods Decomposition model is closer and fitter than Autocorrelation model, but some periods do contrast. Therefore, it is relatively difficult to say which model is better because both models are quite close to the real data.

However, it can be seen the error of these two models as following graph

Decomposition Model

Forecast = (45.164 + 84.57 Time) Factors

Autocorrelation Model

Forecast = 0.4 (data lagged) + 0.651 (data lagged1)

From these equations, it demonstrates that decomposition model depends on the trend running following time and some seasonal effects showing by the factors. And that means it can forecast more than one period ahead. Considering Autocorrelation model, it depends on the lag and lag1 data. It means it can forecast third periods ahead because from its equation has to forecast following the lag data.

4. Try a Box-Jenkins ARIMA approach on the same data and compare your forecasts with the rest of the data as before.

The trendcycle data from the question two will be used to analyse on the Box-Jenkins models as the seasonal effect was removed and the trendcycle data is shown at the chart below.

See the ACF and PACF of the trend-cycle data

From ACF, it shows a trend, so we will do the first difference to remove the trend

From Partial ACF, there is one strong spike on lag one and it dies away after one spike so if a model is fitted to the data, it should be an AR(1).

After first different, ACF does not die away so try the Partial ACF.

This PACF does not show any evidence of pattern repeat and does not die away so try the second difference.

The ACF and PACF of the second difference do not give anything helpful.

Therefore, AR(1) model is suggested by the Partial ACF of the trendcycle data. So we will try ARIMA(1,0,0) trendcycle data.

Model Description

Variable TRENCYC

Regressors NONE

Non-seasonal differencing 0

No seasonal component in model.

Parameters

AR1 ________ value originating from estimation

CONSTANT ________ value originating from estimation

5.00 percent confidence intervals will be generated.

Split group number 1 Series length 6

No missing data.

Melards algorithm will be used for estimation.

Termination criteria

Parameter epsilon .001

Maximum Marquardt constant 1.00E+0

SSQ Percentage .001

Maximum number of iterations 10

Initial values

AR1 .416

CONSTANT 808.40

Conclusion of estimation phase.

Estimation terminated at iteration number because

Sum of squares decreased by less than .001 percent.

FINAL PARAMETERS

Number of residuals 6

Standard error 5.65186

Log likelihood -74.7657

AIC 150.551

SBC 1508.658

Variables in the Model

B SEB T-RATIO APPROX. PROB.

AR1 .8057 .0185 4.8788 .00000000

CONSTANT 8144.71650 17.450 .7476 .00008

The constant term is significant so we can fit an AR(1) to the data with the constant.

The following new variables are being created

Name Label

FIT_1 Fit for TRENCYC from ARIMA, MOD_1 CON

ERR_1 Error for TRENCYC from ARIMA, MOD_1 CON

LCL_1 5% LCL for TRENCYC from ARIMA, MOD_1 CON

UCL_1 5% UCL for TRENCYC from ARIMA, MOD_1 CON

SEP_1 SE of fit for TRENCYC from ARIMA, MOD_1 CON

From the box of variables in the model above, T-ratio of the AR1 and constant are far away from 0 and bigger than and also the all probabilities of which are significant.

So, the ARIMA Model will be

Z t = 8144.71650 + 0.8057 Z t-1

Because of using the trendcycle data with ARIMA, the ARIMA forecast data need to be multiplied by factors in order to get the real forecasts and compare to the actual data

Forecast = (8144.71650 + 0.8057 Z t-1) factors

After multiplying the forecast data by factors and plotting the forecast data against the actual data, it can be seen that it is very close to the data all over the period of time so this ARIMA model fits to the actual data.

Then check this model by considering the residuals by plotting the error. So, the graph shows removing trend and it is stationary.

So, the graph shows removing trend and it is relatively stationary. Then check with ACF and PACF which do not suggest anything.

All figures of ARIMA forecasting are shown as following table.

Time Data Forecast Error LCL UCL

14 Jan 1 41 777.48 -78.40 755.1 1554.1

Feb 80 40.1 -46.50 40.41 55.8

Mar 487 4446.0 4.10 67.1 566.61

Apr 4 46 484.61 -456.74 76.75 61.16

May 5 4657 460.8 54.08 77.10 576.5

Jun 6 457 477.15 180.5 4.85 585.6

Jul 7 464 510.58 -410.17 74. 610.4

Aug 8 551 481. 666.46 404.1 576.7

Sep 48 506.71 -17.71 417.01 6486.4

Oct 10 4688 5050. -6. 870.61 60.0

Nov 11 505 4755.17 6.8 575.46 54.88

Dec 1 57 516.58 5. . 61.80

15 Jan 1 481 46.11 -156.6 404.57 640.8

Feb 14 4571 4664.78 -10.06 46.4 605.84

Mar 15 5485 508.7 401.7 04.0 66.44

Apr 16 508 545.5 -4.64 456.7 6716.

May 17 56 511.81 76.4 407.51 648.

Jun 18 5678 578.0 -117.7 4504.6 6864.11

Jul 1 5587 5841.4 -44.65 447.05 676.47

Aug 0 6116 567. 8.77 446.8 6605.70

Sep 1 556 586.84 -07.84 460.1 704.55

Oct 576 561.18 150.8 44.48 671.8

Nov 5781 580.8 -8.8 46.57 688.

Dec 4 5855 6060.01 -17.1 4647. 7006.64

16 Jan 5 576 54.74 86.5 448.7 6858.8

Feb 6 5448 5556.16 -118.86 45.6 785.8

Mar 7 577 608.74 -55.74 484.04 708.45

Apr 8 650 570.70 660.51 46.8 68.7

May 658 6577.07 4.88 5.4 761.65

Jun 0 60 667.4 -6.76 568.76 778.17

Jul 1 7066 616.6 86.1 4778. 717.75

Aug 6841 7161.50 -05. 5640.76 8000.18

Sep 6480 6546.0 -66.0 567.1 776.61

Oct 4 617 651.5 404.65 5.64 76.06

Nov 5 6468 640.86 -47.86 5761.15 810.56

Dec 6 7161 6760.60 85.00 50.87 7680.

17 Jan 7 6 6564.54 -181.6 570.4 808.75

Feb 8 6088 6147.1 -65.8 5576. 75.65

Mar 646 6718.7 -47.7 558.67 788.08

Apr 40 78 6157.4 14.76 510.1 746.60

May 41 700 761. -604.4 664.84 874.5

Jun 4 747 710.8 6.45 578.1 814.7

Jul 4 7 766.06 75.0 616.66 85.08

Aug 44 7516 800. -470.40 6448.7 8808.1

Sep 45 750 7177.7 4.7 57.56 856.7

Oct 46 74 75.14 410.86 65.4 8711.85

Nov 47 71 746. -555. 6767.1 16.6

Dec 48 8751 7701.87 1008.78 65.4 8585.5

18 Jan 4 7516 788.7 -47.60 7.47 588.8

Feb 50 77 70.66 146.5 676.40 05.8

Mar 51 86 8064. 8.77 6884.5 4.4

Apr 5 8180 811.58 -11.8 717.05 58.47

May 5 87 846.44 -17.46 716.0 5.7

Jun 54 884 808.44 66.1 665.8 5.4

Jul 55 17 146.76 -1.00 7615.5 74.67

Aug 56 8606 01.88 -567.51 758. 4.40

Sep 57 4 815.1 1046.81 7015.48 74.0

Oct 58 878 0.68 -4.68 8040.7 10400.

Nov 5 80 8716.67 0. 756.6 86.7

Dec 60 445 61.1 176.80 775. 10084.64

1 Jan 61 8107 8610.5 -5.84 788.8 104.

Feb 6 84 7758.77 511.4 746.4 705.8

Mar 6 767 00.0 746.8 7840.1 101.7

Apr 64 88 540.77 -7.1 8555.77 1015.1

May 65 8511 086.5 -56.66 7816.68 10176.10

Jun 66 874 858.68 15.14 741.54 600.6

Jul 67 85 1006.57 -177.48 8470.84 1080.6

Aug 68 80 1.6 -85.6 867.56 1066.7

Sep 6 87 8.5 648.75 8158.55 10517.6

Oct 70 460 51.0 -41.0 8771.4 1110.1

Nov 71 1057 44.44 116.56 854.7 10614.15

Dec 7 10450 1071. -501.7 6.64 117.06

000 Jan 7 50 510.54 0.4 881.8 1110.80

Feb 74 1006 05.7 10.67 8815.1 11174.6

Mar 75 108 1061.76 -18.76 78.05 1141.47

Apr 76 4 10565.15 -147.0 601.06 1160.47

May 77 11156 601.75 158.86 86.8 10686.

Jun 78 10806 1108.6 -5.06 80.47 1168.8

Jul 7 1077 1068.8 -187.87 66.8 1176.

Aug 80 1088 1081.4 48.17 15. 1145.4

Sep 81 06 100.6 -414.6 140.8 11500.40

Oct 8 10857 871.78 85. 86.07 11051.48

Nov 8 108 10804.0 87.70 64.5 1184.01

Dec 84 1070 117.16 -541.50 658.1 1018.

001 Jan 85 1114 74.5 1474.18 075.58 1145.00

Feb 86 100 10610.4 -645.5 10480.11 18.5

Mar 87 1106 1058.5 10.47 778.8 118.4

Apr 88 117 10785.1 450.81 85.61 1185.0

May 8 1188 11505.70 88.4 101.07 1571.4

Jun 0 1171 114.75 -14.75 105.85 188.7

Jul 1 1 115.18 8. 101.74 167.15

Aug 15 165.85 -610. 11168.7 158.14

Sep 1158 11668.7 -10.7 10488.56 1847.8

Oct 4 147 115.56 151.44 10115.85 1475.7

Nov 5 184 1147.86 -8.86 1168.15 147.57

Dec 6 165 167.8 -608.55 11577.88 17.0

Comparing the forecast data with the rest of the data, we calculate them by extending the time line and see the result as following table

Time Data Forecast Error LCL UCL

00 Jan 7 176 1664.74 10.8 117.4046 14.04

Feb 8 1165 10.5 -147.8 11764.648 1488.006

Mar 167 14.08 -715.08 1186.16 145.887

Apr 100 184 184.01 714.8 10.184 15087.04

May 101 14085 11.54 6.10 11477.54 14541.5

Jun 10 111 18.7 -146.05 11588.6446 1465.4704

Jul 10 147 1484.8 5.60 1.05 156.046

Aug 104 115 1700.5 -46.8 11516.186 14580.01

Sep 105 18 1587.1 -58.1 1055.04 1511.007

Oct 106 144 1448.75 45.5 1116.868 1480.666

Nov 107 14 175.48 -.48 1174.5704 14807.6

Dec 108 1 1400.47 -78.4 11.66 146.7884

00 Jan 10 158 1646.74 1001. 11780.4406 14844.664

Feb 110 17 1184.77 41.47 1146.5 1456.161

Sum of squared errors = 55.1

Mean-squared error (MSE) = 4566.7

Root-mean-squared error (RMSE) = 68.15

This RMSE is about 4.76 % of the mean for the rest of data during the forecast period

However, overall the plot graph below (During 14-00) shows that the predict data relatively fits the real data.

5. Discuss the differences between the Box-Jenkins results and the other methods you have used. In particular comment on the different mathematical forms of the models selected.

Decomposition Model

Forecast = Trend Factors

= (45.164 + 84.57 Time) Factors

Autocorrelation Model

Forecast = 0.4 (Data lagged) + 0.651 (Data lagged1)

ARIMA Model

Forecast (Zt) = (8144.71650 + 0.8057 Z t-1) Factors

First of all, decomposition model is a linear model which depends on the trendcycle, runs following time, and seasonal effects showing by the factors. So it can forecast more than one period ahead.

Second, autocorrelation model is based on two explanatory variables which are data lagged and data lagged1. So it can forecast third periods ahead because from its equation has to forecast following the lag data.

Finally, Box-Jenkins model or ARIMA model shows the form of an ARIMA(1,0,0) or AR(1) model which depends on the constant term and Zt-1 as well as factors.

The graph below illustrates the comparisons of actual data and three forecasting data which are generated from decomposition model, autocorrelation model and ARIMA model.

It is very difficult to say which model is better because most forecast data are relatively close to real data and some periods one is fitter to the actual data more than others but some periods do contrast. Therefore, we can not tell much different among them.

Next, we will focus on the most appropriate of these three models by considering the error.

Compare the errors of the data for model fit.

From the graph above, it can be seen that the autocorrelation model is more appropriate than other two because of more accuracy. Moreover, it involves only the lag data and does not have any influences from the factors like other two models. If you look at the actual data graph below, it shows a trend pattern but does not show a seasonal pattern. It is emphasised to use autocorrelation model. On the other hand, if the graph shows obviously seasonal pattern, decomposition model should be better. And because of using the trendcycle data to use for ARIMA forecasting, it involves seasonal effects (factors); so make ARIMA model not a proper method as well.

However, there has some lost data at the beginning and at the end by using autocorrelation model to forecast because its data is need to be lagged and it can not forecast a bit more future like Box-Jenkins or ARIMA model.

6. Comment on the impact of your choice of where to split the data in order to use some data to fit the model and other data to check it.

The data at the end (00-00) has been used to check the forecast. From the graph below, we can see that at the beginning and middle of the rest data, the forecast data from autocorrelation model and ARIMA model is close to data but other periods, significant errors will occur. However, there are considerable errors occurred.

Then, trying new data model, we choose data model from 15-00 and data in 1-14 is used for checking. The graph below shows errors from old data (split at the end) against errors from new data (split at the beginning)

The magnitudes of errors from two ways to split data all over the period are similar but they do not overlap at the same time so it does some impact on where to split the data. However, it should be consider the pattern of actual data and choose the appropriate method to forecast rather than think about where to split the data.

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Shakespeare's Othello-the emotions of othello

If you order your custom term paper from our custom writing service you will receive a perfectly written assignment on Shakespeare's Othello-the emotions of othello. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality Shakespeare's Othello-the emotions of othello paper right on time. Out staff of freelance writers includes over 120 experts proficient in Shakespeare's Othello-the emotions of othello, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your Shakespeare's Othello-the emotions of othello paper at affordable prices! The Emotions of Othello

The motion of this play was based on the relationship or the friendships between Othello, Iago, and Cassio. Although the women in this book played a large role in adding to the vengeful, powerful feel in this play, it all ceased from the relationship between the three men. This play was filled with many different emotions, given by each of the characters when they interacted with each other. The emotions that mainly evolved in this play were Power, jealousy, revenge and very questionable love between the characters. This story shows how power is such a strong desire of people, that some will stop at nothing to get it and hurting people is an option if they have to do it.

This all began with Othello, who is the main character in this book. Othello was a Moor and a General in the Venetian army. He was a very powerful man who made large decisions, one decision which he made changed his life forever. Othello was very trustworthy and trusted. He had so much trust in the people that he was close to, that it affected his life and he believed many things that he should not have. Because of an overabundance of trust and belief in others, this caused him to become a rageful, jealous man. All of his better characteristics were pushed aside deep down in his soul and allowed the worst to get the best of him. All of this jealous rage in Othello came from the lies that the manipulative, evil Iago had told Othello of his wife Desdemona. Iago is Othellos standard-bearer and he came to hate him, which led Iago to set up Othello and make him pay by destroying his love for his wife Desdemona. Iago had become jealous towards Cassio and vengeful against Othello. He had became this way when Othello appointed Cassio as Lieutenant in the army and not him. Iago felt that he deserved that position as Lieutenant more than Cassio did. Iago also felt that Othello chose Cassio over him for that position because of the friendship between the two.

Cassio is a dear friend of Othello and Desdemona. Cassio was friends with Othello before he came to meet Desdemona. Cassio is the person who got Othello and Desdemona together. Othello and Cassio can be looked at as best friends. Since them being friends for so long, the relationship between Cassio and Othello would be expected to be very close and trusting towards on another. Although, what happens in this play questions the friendship between Cassio and Othello. In theory, Cassio not only received that position because he was a close friend of Othello, but because he was more educated, better looking and more skilled than Iago was. The whole downfall of Othello and Iago is caused when the evil and manipulative one goes and tells Othello a lie about Desdemona. Iago feels that the only way to destroy Othello is to destroy the relationship between him and his wife and the friendship between Cassio and Othello. It is known that Desdemona is Othellos strength and she makes him what he is and he is nothing without her. Iago thinks that if he can destroy their relationship that Othello will automatically become weak and feel that he has nothing to live for and just give up. He tries to destroy the friendship between Cassio and Othello, so that he can take the title of the Lieutenant away from him, and probably be left with no choice but to give that title to Iago. Basically, that is two strikes in one with Othello and will most likely have him to fall and loose his power. What Iago does to complete this mission is that he tells Othello that his loving innocent wife is having an affair with his appointed lieutenant and close friend Cassio. He tried to lead Othello to believe the two were having an affair in Act . sc. . Iago says to Othello to make him suspect they are having an affair Look to your wife; obsevere her well with Cassio,..

At first, Othello does not believe that the love of his life Desdemona would commit such an unfaithful act, and he asks Iago for proof. In Act . Sc. , Iago says to Othello I speak not yet of proof. At that time, Iago had no proof. Iago went on and on about how Desdemona is flirtatious and how she was not what Othello thought she was, but yet Othello still did not believe him. Of course the idea never left his mind about what Iago lied to him, but it was not a fact to him. The thing that sparked the situation between Othello and Desdemona was when he caught Cassio with the handkerchief. This handkerchief had sentimental meanings between the two of them. Othello gave Desdemona this handkerchief when they first meant to express his love for her, or just his feelings. His mother gave it to him and he gave it to her. When he saw Cassio with that same handkerchief, he though that Desdemona gave it to Cassio, which made him become enraged with jealousy and suspicion. What really happened was that Desdemona misplaced that handkerchief, and Emilia recovered it and she was not going to return it. She gave it to Iago, and Iago gave it to Cassio. That is why Othello saw him with the handkerchief. After that Cassio gave it to Bianca and told her to make a copy of it, and that someone would be back to claim it. Othello spoke to Desdemona while lying on her deathbed about the handkerchief. He mentions to her in Act 5. sc That handkerchief Which I so loved, and gave thee, thou gavst to Cassio. He immediatley accused her of giving it to Cassio, by jumping to conclusions. She told him it was not so and he asked her where it was and she could not tell him, and this made him even more enraged, and he though she was lying.

Othello from this point on continually blamed his innocent wife for committing an unfaithful act, but he never came out and asked her if she had or was having an affair with Cassio. Othello had called Desdemona a whore in Act 5. sc. Out strumpet. This point here shows that Othellos trust was not as strong as readers may have thought it was. There was a lack of communication between the two at this time, because if there really was communication between them, then he would have asked her about it, and Cassio as well. There was also a lack of communication between Cassio and Othello. If they had a good friendship, they should have had a good communication level, which they could not of have or Othello would have asked him about the accused affair between Desdemona and Cassio. He never confronted Desdemona about the rumor, or Cassio. If their friendship was really that tight, ten he should have confronted him about it as well. Then again, maybe Othello was just so upset and hurt that any solution to this problem may have been blocked out of his mind. Although he did jump to conclusions about this whole situation, but I guess at that time everything was coming together about that thought, but it was all occurring just coincidentally. At this time, Othellos characteristics towards these characters had become questionable. It was like was her really the things that he was supposed to be to them. Did he really trust Desdemona enough to believe that she would cheat on him? Were him and Cassio such good friends for him to think that Cassio would do such a thing to him? Was Iago really not a trustworthy person, because Othello did not believe him in the beginning, but the though never left his mind. For Othello to believe Iago, before even questioning Cassio or Desdemona shows that Othello allowed Iago to manipulate him. Othello became weak and allowed Iago turn him into what he became, which lead to the death of his wife and himself. Not to mention the death of Emilia, wife of Iago and Desdemonas mistress.

Iago started all of this chaos with no good intentions at all. All of this was one great big set up and all planned out. Although Iago was looked upon as the evil one in this story, I think he was cool. I think that because he in a way he had the power to hypnotize people and made them believe everything that he said. Not only that, but most of the things that he set up went the way he wanted them too, making his whole plan successful. If they were dumb enough to let him do that to them all, then I dont see why he would have stopped. I guess that is the way that he though of the situation. If they believe every lie that I tell them, to get what I want I am going to continue to lie to them. That was the though of Iago, but it only ended up affecting him at the end of the play. He did get the satisfaction of manipulating Othello, Cassio and other characters, and causing chaos throughout the book. He at one point became the center of attraction, but at the end he seemed to pay for it all, not physically but mentally. Taking the lives of innocent people, just for power and out of revenge and jealousy. He was so desperate for power that he stopped at nothing to get it. He was satisfied at the end of the play, but he was forever going to live miserably, because of all the confusion that he cause, all the hearts he hadoke, and all the lies that he had told. Othello before he killed himself tells Iago in other words that he will have to live with what he did, and Iagos response to that was Demand me nothing. What you know, you know. From this time forth, I will never speak word (Act5. sc.). What he meant by this was that whoever was involved with what went on are the only people who will ever know about it, because from that time on, he will act as if nothing ever happened.

The relationship between these characters have played an important role in the motion of this story. There was lack of several factors in these relationships, which caused the actions that took place. Many of these actions which were very dangerous and life threatening. Due to the fact that these relationships were so questionable, they caused these characters to act in different ways creating the endless motions of revenge, jealousy and hatred between them all. These feelings which never were discovered until the climax of the play, had to have always been there deep inside these characters towards one another. Although there is still a question about that. The question that stil remains is were these feelings really rooted in these characters towards one another, or did certain actions taken by them cause these feelings to come out against them? The relationships between them have changed over the course of time, and this was only caused due to the actions that these characters have taken. Friendships can seriously be dangerous if there is a struggle for power between each of the friends. A person will do anything for power, even if it means hurting others.

Please note that this sample paper on Shakespeare's Othello-the emotions of othello is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on Shakespeare's Othello-the emotions of othello, we are here to assist you. Your cheap custom college paper on Shakespeare's Othello-the emotions of othello will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

Humanism

If you order your custom term paper from our custom writing service you will receive a perfectly written assignment on Humanism. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality Humanism paper right on time. Out staff of freelance writers includes over 120 experts proficient in Humanism, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your Humanism paper at affordable prices! Essay Paper

Psychology in Secondary Education

Marcella King

September 5, 00 Humanistic education is made up of loosely related assumptions and techniques. The movement emerged as a reaction against prevailing approaches in education. It happened during a time in American history when there was dissatisfaction with many aspects of our society as well as a distrust of authority. The view stressed that humans control much of their own behavior, and parents and teachers should allow freedom for children to explore and develop. This view was proposed by three eminent psychologists Aaham Maslow, Carl Rogers and Arthur Combs.

In the humanistic approach to learning, teachers are encouraged to create a classroom environment that address students needs and helps them develop a positive attitude about themselves and others. The non-cognitive values of emotions, values and self-perception are stressed. Techniques used in teaching this approach are the use of role-playing, values clarification and abolishing the traditional grading system.

Teachers are encouraged to allow students to make choices and to manage their own learning. It is also important to establish a warm and positive atmosphere in the classroom. The teacher should also serve as a facilitator and helper and use object lessons in the classroom as well as setting a good example for her or his students.

I believe that there are many ways that I could facilitate the humanistic approach in my classroom. One way is to collaborate with students in designing classroom rules at the beginning of the year. Another possibility is for students to give suggestions on various topics they would like to research either as an individual or in a group.

As a teacher I believe it is important to make the students feel valuable and cared for. Being able to call a student by his or her name is an important way to build a relationship. One way to learn students names quickly would be to take pictures of each student at the beginning of the year as a quick referral point. Greeting each student at the door at the beginning of the period with a smile and positive comment helps build a positive relationship with students. Letting students know your office hours at school if they need to talk to you about a situation can also be helpful. Giving a variety of choices to do for a project lets students help with decision- making. Placing a suggestion box in the classroom for ideas or concerns without students signing their name can create a safe and caring environment.

In the local school that I have worked with, the chorus participates in a number of civic activities such as singing at care facilities or at the hospital Christmas party. I believe these activities help students become sensitive to the needs of others as well as becoming a role model for other students.

I know there are other ways that I can encourage the humanistic approach in my classroom. By doing so, I will instill some of the best values my students will need as they become valuable assets to society.

Please note that this sample paper on Humanism is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on Humanism, we are here to assist you. Your cheap custom college paper on Humanism will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

Anne Frank

If you order your cheap custom essays from our custom writing service you will receive a perfectly written assignment on Anne Frank. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality Anne Frank paper right on time. Out staff of freelance writers includes over 120 experts proficient in Anne Frank, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your Anne Frank paper at affordable prices! On Friday morning, August 4, 144, a German police officer accompanied by four men in civilian clothes entered the building. The men had been told of the families in hiding And they knew exactly where to go to find them. No one knows who the informer was.

The police emptied the satchel in which Anne kept her diaries, notebooks and photographs so they could use it to carry away food and valuables. The papers and photos were recovered later by Miep Gies and Bep Voskuijl, two of the Dutch Christians who courageously kept the occupants alive (the others were Victor Kugler, Johannes Kleiman, and Jan Gies).

Anne and her group were first sent to the Westerbork transit camp. In September they were transported to the extermination camp in Auschwitz-Birkenau where Annes mother and Herman van Pels died very soon. Peter van Pels perished in Mauthausen. Auguste van Pels died somewhere in or near Theresienstadt. Anne and Margo were sent to Bergen-Belsen camp, where in March 145, they died of typhus and starvation. Anne was just short of her 16th birthday. Of the group, Otto Frank was the only survivor. He was freed when the Russians liberated Auschwitz in January 145.

When Otto Frank returned to Amsterdam after the war, Miep Gies gave him Annes diaries and exercise books. When he knew that Anne was dead he began copying whole sections out of the diaries to send to other surviving family and friends. Since parts of the diary had been rewritten and revised by Anne herself, he edited the text also, omitting parts he deemed too personal to be included in a document to be read by others. Those who read the excerpts recognized the value of such a document and urged him to seek a publisher. The manuscript was corrected and edited by several people in addition to Otto Frank. Several publishers rejected the manuscript before it was at last accepted in 147 by a Dutch publisher who printed only a small number of copies. The edition was well received and in 150, there was a German and a French edition. In 15, an edition of the diaries was published in the United States where it was received with great acclaim. A dramatic version of the story of Annes ordeal was presented on the stage. Cheap essay writing services offer help on Anne Frank In the 150s, people who hated Jews and wanted to discredit the Holocaust and anything connected with it, began to publish articles stating that the diary was a hoax. When Otto Frank died in 180, he gave the diary to the Netherlands State Institute for War Documentation. There was so much controversy connected with the authenticity of the diary, the N.S.I.W.D. felt obliged to subject every part of the diary to scientific testing in order to determine its authenticity once and for all. They tested the paper, the ink, the glue that bound the book together, the handwriting, the postage stamps and censorship stamps on postcards and letters that Anne and her family sent during their time in hiding. The forensic experts produced a highly technical, 50-page report on their findings. It proved that the diaries were written by one person during the period in question and the changes made to the diaries were of a very limited nature. It proved beyond any doubt that the diaries were authentic.

Anne Franks diary is a testament to her keen powers of observation and the growth of her maturity and insight. It has had an emotional impact on all who have read it. The diary makes us aware of what it is like to live each day in fear of being ripped from ones home and loved ones, the fear of losing ones very life only because of having been born a Jew in a land and at a time when barbaric Nazi ideology ruled.

Please note that this sample paper on Anne Frank is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on Anne Frank, we are here to assist you. Your cheap research papers on Anne Frank will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!

The Gorgeous America

If you order your custom term paper from our custom writing service you will receive a perfectly written assignment on The Gorgeous America. What we need from you is to provide us with your detailed paper instructions for our experienced writers to follow all of your specific writing requirements. Specify your order details, state the exact number of pages required and our custom writing professionals will deliver the best quality The Gorgeous America paper right on time. Out staff of freelance writers includes over 120 experts proficient in The Gorgeous America, therefore you can rest assured that your assignment will be handled by only top rated specialists. Order your The Gorgeous America paper at affordable prices! Few political documents have affected the world quite like the American Declaration of Independence or the French Declaration of the Rights of Man and Citizen. The repercussions of each have had a profound effect on world history up to this point. But why did these documents have such an effect? The answer lies in the common philosophical backgrounds of the two. The writings of Rousseau, Locke and Montesquieu all contained ideas that were later used by Thomas Jefferson and the National Assembly to compose the two documents.

Rousseaus ideas of a social contract, which states that the general will and the people were sovereign, and if a king abuses the liberty of the people they have a right and a duty to dissolve the current government and create a new one (McKay, 581), were central to both documents. Jefferson had Rousseaus ideas in mind when he wrote the Declaration of Independence. The history of the present King of Greatitain [George III] is a history of repeated injuries and usurpations, all having in direct object the establishment of an absolute tyranny over these states...a prince, whose character is thus marked by every act which may define a tyrant, is unfit to be the ruler of a free people...we therefore...solemnly publish and declare, that these United Colonies are...independent states... (Jefferson, 1-). The reasons, such as suspension of colonial legislatures, impressment of American sailors and the importation of mercenaries (Jefferson, ), given for the dissolution of the political connections that the American anditish people have held for over 100 years all relate to the Kings tyrannical tendencies and the peoples right to choose a different government. The edict also states that although petitions of grievances were issued, the King turned a deaf ear.

The Declaration of the Rights of Man is not only built on the social contract, but also on Rousseaus idea of general will of the people. He defines the general will as being, Sacred and absolute, reflecting the common interests of the people, who have displaced the monarch as the holder of the sovereign powers. (McKay, 581) Passing and enforcing arbitrary laws are considered to be an act of tyranny and a substantial reason, according to Rousseau, to declare the current government void and establish a new one. Article VII clearly states that arbitrary laws and orders cannot exist.(Sherman, 100) The fact that this is distinctly stated implies that arbitrary laws were being passed and enforced under Louis XVI. Article VI states that law is the expression of the general will every citizen has the right to participate personally or through his represenative.... (Sherman, 100)

Lockes ideas of natural rights, the rights of human beings to the pursuit of life, liberty, and property (McKay, 54), is clearly stated in both declarations. In the Declaration of Independence, Jefferson used the exact words in the preamble - life, liberty, and the pursuit of happiness - in which he uses happiness to mean property.(1) He also cites examples of the arbitrary suspension of liberties by George III such as the right to peaceably assemble, taxation without the consent of the colonists, maintenance of a peacetime standing army, and the right to a trial by jury.(1-) A reference to natural rights also appear in the preamble of the Declaration of the Rights of Man. Article II of the proclamation directly states, The aim of all political associations is the preservation of the natural ... rights of man (which are)... liberty, property, security and resistance to oppression. (Sherman, -100) Article IV defines liberty as

The freedom to do everything which injures no one else hence the exercise of the natural rights of each man has no limits except those which assures to the other members of society the enjoyment of the same rights. These limits can only be determined by law.(Sherman, 100)

The rights of freedom from arbitrary imprisonment and the idea of someone accused of a crime is innocent until proven guilty (Sherman, 100) were all laid out by the national Assembly and run parallel to Lockes ideas about human rights.

Montesquieus ideas of the courts being the foremost protector of liberties (577) is used as a reason for theeak with Greatitain. The justices of the admiralty or naval courts that existed in colonial America served at Kings Pleasure rather than Good Behavior, ensuring that the decisions of the courts would be biased in favor of the King. The right to a trial by jury was also suspended for those whooke the laws laid down by the Navigation acts. The colonials expressed these concerns in the Declaration of Independence. The Declaration of the Rights of Man also hold Montesquieus interpretation of the courts. It provides for the right to a trial and freedom from punishments that are not strictly and obviously necessary.(Sherman 100) It also holds that all men are equal in the eyes of the law.

Both the Declaration of Independence and the Declaration of the Rights of Man and Citizen have common roots in the arguments of the Enlightenment, and in the Enlightened philosophies Rousseau, Locke and Montesquieu. Rousseaus idea of a social contract, Lockes natural rights, and Montesquieus idea of the courts being the defenders of liberties all came into play when the two documents were written, and in being written, the culmination of the Enlightened thinkers came to their peak.

Please note that this sample paper on The Gorgeous America is for your review only. In order to eliminate any of the plagiarism issues, it is highly recommended that you do not use it for you own writing purposes. In case you experience difficulties with writing a well structured and accurately composed paper on The Gorgeous America, we are here to assist you. Your cheap custom college paper on The Gorgeous America will be written from scratch, so you do not have to worry about its originality. Order your authentic assignment and you will be amazed at how easy it is to complete a quality custom paper within the shortest time possible!