Perceptions of African American Women Essay Example for Free

Impression of African American Women Essay I am taking a few classes that will in the end qualify me to study Astro Physics, or Chemical designing, I likewise need to work with NASA and train as a space traveler. It was stunning to realize that Dr. Mae C. Jemison who happens to be the most youthful of three kids destined to a white collar class African American family, Charlie Jemison, a support laborer and his better half, Dorothy, an instructor. Dr. Mae C. Jemison was the principal dark lady space traveler to be in space in a time loaded up with isolation and bigotry, she is a Chemical designer, researcher, doctor, educator and space traveler, she has a wide scope of involvement with innovation, building, and clinical examination. Notwithstanding her broad foundation in science, she is knowledgeable in African and African-American Studies, talks familiar Russian, Japanese, and Swahili, just as English and is prepared in move and movement. Dr. Mae C. Jamison was a motivation to me, and likely to numerous African American ladies. She was loaded with strength and assurance particularly to have reached and made progress in an abnormal field of attempt for some African American ladies, I cheer her assurance to have any kind of effect among the African American ladies and blacks in Diaspora. In the wake of moving on from Morgan Park High School in 1973 at 16 years old, Dr. Mae Jemison earned a BS in Chemical Engineering from Stanford University, while additionally satisfying the prerequisites for a BA in African-American Studies. Subsequent to acquiring these degrees in 1977, she went to Cornell University and got a Doctor of Medicine degree in 1981. During clinical school she headed out to Cuba, Kenya and Thailand, giving essential clinical consideration to individuals living there. This means that her helpful endeavors and energy to contact the less special populace. Wanting to accomplish more with her life, she joined up with graduate classes in designing and applied to NASA for admission to the space traveler program. She was turned down on her first application, possibly on the grounds that she is a dark lady, yet she continued on and in 1987 was acknowledged on her subsequent application. She got one of the fifteen up-and-comers acknowledged from more than 2,000 candidates. When Dr. Mae Jemison effectively finished her space traveler preparing program in August 1988, she turned into the fifth dark space explorer and the principal dark female space traveler in NASA history. In finishing her first space flight, Dr. Mae Jemison logged 190 hours, 30 minutes, 23 seconds in space, making her the primary African-American lady in space. She says, â€Å"I needed to learn early not to confine myself due to others’ constrained minds. I have taken in nowadays never to restrain any other person because of my constrained creative mind. † This is a motivation to different blacks all in all who typically expect a peasant and accept that they will never progress nicely or will be acknowledged in whatever they do. This is a reminder, and appearance of the colloquialism â€Å"Determination is the mother of invention†. In 1993, Dr. Mae Jemison left NASA and established the Jemison Group, Inc. to explore, create and execute trend setting innovations fit to the social, political, social and monetary setting of the individual, particularly for the creating scene. Current ventures include: Alpha, (TM) a satellite based media transmission framework to improve medicinal services in West Africa; and The Earth We Share, (TM) a global science camp for understudies ages 12 to 16, that uses an experiential educational program. Among her present ventures are a few that emphasis on improving medicinal services in Africa. She is additionally an educator of ecological investigations at Dartmouth College. Dr. Mae Jamison bacome famous and name for blacks by and large; Her enterprising soul put her in the spotlight and goes about as a lift to decided dark people in Diaspora. Ellen Johnson-Sirleaf. It was very astonishing to find out about Ellen Johnson-Sirleaf, I know basically nothing about this â€Å"giant and sovereign of present day Africa† who is by and by the current leader of Liberia. As indicated by what I have perused so far about this â€Å"queen of Africa† she was conceived In Monrovia, the capital of Liberia on October 29, 1938. During this period, Liberians did not understand that the First female leader of an African nation had been naturally introduced to their fog. Ellen Johnson-Sirleaf is a little girl to descendents of unique pilgrims of Liberia (ex-African slaves from America, who speedily on appearance set about oppressing the indigenous individuals utilizing the social arrangement of their old American bosses as a reason for their new society). These descendents are referred to in Liberia as Americo-Liberians. From what I read, I saw that Ellen Johnson-Sirleaf was really a scholarly force house, a magnetic pioneer and bound to roll out an improvement in Liberia and contribute her standard in Africa. From 1948 to 1955 Ellen Johnson contemplated records and financial aspects at the College of West Africa in Monrovia. After marriage at 17 years old to James Sirleaf, she went to America (in 1961) and proceeded with her investigations, accomplishing a degree from the University of Colorado. From 1969 to 1971 she read financial aspects at Harvard, increasing an experts degree in open organization. Ellen Johnson-Sirleaf then came back to Liberia and started working in William Tolberts (True Whig Party) government. Ellen Johnson-Sirleaf likewise filled in as Minister of Finance from 1972 to 73, yet left after a difference over open spending, this means that her reasonability and self discipline. As the 70s advanced, life under Liberias one-party state turned out to be more energized to the advantage of the Americo-Liberian world class. On 12 April 1980 Master Sergeant Samuel Kayon Doe, an individual from the indigenous Krahn ethnic gathering, held onto power in a military overthrow. With the Peoples Redemption Council presently in power, Samuel Doe started a cleanse of government. Ellen Johnson-Sirleaf barely circumvented picking banish in Kenya. From 1983 to 1985 she filled in as Director of Citibank in Nairobi. I will say that Ellen Johnson-Sirleaf had a great deal of boldness, since it was very strange for a lady to challenge a domineering occupant president in Africa without being grabbed, tormented or executed all the while, despite the fact that She was later condemned to ten years in jail. Ellen Johnson-Sirleaf spent only a brief timeframe imprisoned, before being permitted to leave the nation by and by as an outcast. During the 1980s she filled in as Vice President of both the African Regional Office of Citibank, in Nairobi, and of (HSCB) Equator Bank, in Washington. Ellen Johnson-Sirleaf assumed a functioning job in the transitional government as the nation arranged for the 2005 decisions, and in the long run represented president against her opponent the ex-global footballer, George Manneh Weah. Regardless of the decisions being called reasonable and methodical, Weah denied the outcome, which gave a greater part to Johnson-Sirleaf, Ellen Johnson-Sirleaf in the long run became Liberias initially chosen female president, just as the principal chose female president in the mainland Africa. . In 2005 She built up a Truth and Reconciliation Commission with a command to advance national harmony, security, solidarity and compromise by exploring over 20 years of common clash in the nation and in November 2007, she got the United States Presidential Medal of Freedom, the U. S. governments most elevated regular citizen grant. She is really a monster and â€Å"queen of current Africa†. References: 1. http://space. about. com/cs/formerastronauts/a/jemisonbio. htm 2. http://www. k-grayengineeringeducation. com/blog/list. php/2008/09/12/first-african-american-ladies in-space. 3. http://www. joinafrica. com/africa_of_the_week/ellenjohnsonliberia. htm.

Hypothesis Identification Article Analysis Essay - 2

Speculation Identification Article Analysis - Essay Example This speculation holds that if the European national bank loans cash to auxiliary banks now and again nobody can drove will guarantee that minimal expenditure is discharged in the European economy and subsequently the money will climb its esteem and shield the nation from swelling, .(Forest &Edward124) this measure will balance out the euro and the financial development and improvement will be acknowledged; since speculators in worldwide markets will lessen their imports and henceforth the gracefully will be low in this manner request will be high. The speculation clarifies that powers of interest and flexibly will be restrained. This speculation is reffered to as positive theory, from the article; this was dismissed to the detriment of the invalid theory. The invalid theory holded on the grounds that the exploration discoveries demonstrated that if the European national bank turns into the moneylender of the last outcome it may not defend the estimation of the euro. (Backwoods &Edward117) The suggestion on the dismissal of this theory was in light of the fact that, the bank of the last outcome doesn't guarantee the money related hazard results and that the strategy is viably a productive endowment system.(Forest

Book Riots Deals of the Day for October 17th, 2019

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Profiles of Craigslist Killers

Application Of A Mechanical Wave Sound - 1396 Words

Applications in medicine Moreover, toxins pile up in the body, especially outside of the cells due to poor bio-electric membrane voltage, which makes it possible for the toxins to stick to the membrane. This activity inhibits the natural flow of water into the cells as well as exit of nucleic waste out of the cells. As a result, the functioning of the cells is compromised until the level whereby toxins penetrate the cell membranes and thus reaching a level whereby the process is irreversible. Moreover, the body’s design has many organs that are attuned to the electromagnetic phenomena. For example, the brain produces electromagnetic fields that are both different and separate from those produced by the heart; a mechanical wave sound is vibrated by the tympanic membrane while the eyes record individual photon packets. As such, the human body is actively involved in the production and control of bioelectricity. Bioelectromagnetism is used in medicine in detoxification whereby the application of electromagnetism leads to the restoration of integrity to the actual membrane itself to return the cell to its normal functioning by being selectively permeable. This leads to proper processing of toxins for elimination whereby the kidneys and liver start to remove more toxins and thereby reducing the overall toxin load in the body. One major application happened in 1892 when Nikola Tesla met with Paul Oudin leading to the production of the â€Å"violet ray†, a device that usedShow MoreRelatedA Wave Is Repeated Oscillation That Transfers Energy Without Transferring Matter1279 Words   |  6 PagesA wave is repeated oscillation that transfers energy without transferring matter. There are few types of waves: Transverse waves Longitudinal waves Properties of the waves: †¢ Reflection – it is a change of the wave direction when there is a fixed boundary (if there’s a fixed end, crest will reflect with the trough back. If there’s an open or free end, crest will reflect with the crest back) †¢ Refraction – it is a change of the wave direction and speed when it travels from one medium toRead MoreThe Effect Of Sonic Logs On The Petroleum Industry And The Current Advances Made On Their Application1596 Words   |  7 Pagesdetermine these parameters. Full-waveform acoustic logging has advanced significantly in both theory and application in recent years, and these advances have greatly increased the capability of log analysts to measure the physical properties of formations (Paillet et al 1992). This report focuses on the basic application of sonic logs in the petroleum industry and the current advances made in their application. It is also explained how porosity of the rocks are obtained from shaly formations using sonic logsRead MoreBlowing Bottle Tops: Making Music with Glass Bottles716 Words   |  3 PagesHave you ever wondered why glass bottles made a sound, kind of like a music note? Well, this paper will explain how this works. The paper will be talkin g about sound, sound waves, standing waves, musical note names and frequencies, resonance, and closed-end air columns. Closed-end air columns will be a main focus in the paper, studying the physics behind it. Glass bottles are an example of a closed-end air column. Therefore, the more water inside the bottle, the lower the note, and less water wouldRead MoreElectrical Energy Into Mechanical Energy1335 Words   |  6 Pagesto convert electrical energy into mechanical energy. Ultrasonic waves are longitudinal waves which move as a series of compressions and rarefactions across the direction of wave propagation through the medium [37]. In addition to distance measurement, they are also utilised in ultrasonic material testing to detect; air bubbles, cracks, and other defects in products, detection of object and position, ultrasonic mouse, etc [37]. Ultrasonic sound waves are mechanical vibrations that display all of theRead MorePrinciples of Physics in Ultrasound Essay1717 Words   |  7 Pagesinfection. A sound or ultrasound wave consists of a mechanical disturbance of a medium (gas, liquid or solid) which passes through the medium at a fixed speed. Sound waves consist of a disturbance of air molecules, the vibrations which pass from molecule to molecules from the speaker to the ear of the listener. The rate at which particles in the medium vibrate in the disturbance is the frequency or pitch of the sound measured in hertz (cycles/sound). As theRead MoreYear 11 Physics: the World Communicates Dot Points2490 Words   |  10 PagesThe World Communicates 1. The wave model can be used to explain how current technologies transfer information * describe the energy transformations required in one of the following: mobile telephone, fax/ modem, radio and television Energy transmission in mobile telephone: sound wave energy (input sound) -gt; electrical (in transmitting phone) – gt; radio wave (transmit signal) -gt; electrical (in receiving phone) -gt; sound (output sound) * describe waves as a transfer of energy disturbanceRead MoreThe Effect Of Sensitivity Of Sub Surface Fatigue Cracks During Service Stage Of Gears1309 Words   |  6 PagesThe Effect Of Sensitivity Of Sub-Surface Fatigue Cracks During Service Stage Of Gears R.Vyjayanthi ,Dr.B.Venkatesh Department of mechanical engineering Vardhaman College of engineering India, Hyderabad, 500070 Vyjayanthi8@Gmail.Com Abstract—Gears are one of the most critical components in mechanical power transmission systems. Gear failures occur due to defect formation during manufacturing and service stage of gear .Mostly failures during service stage of gear show major effect on the componentRead MoreProject on Ultrasound12323 Words   |  50 Pageshear the sound waves between 20 Hz to 20 kHz. This frequency range is known as â€Å"Audio Frequency Range†. The sound waves having frequencies above this audible range is known as â€Å"Ultrasonic Waves† or â€Å"Supersonic Waves†. Supersonic waves have the velocities higher than the velocity of sound i.e. more than 1200 km / hour. Ultrasonic waves can not be heard by a human being but a cat or dog may hear them. The wavelengths of ultrasonic waves are very small as compared to audible sound. The sound waves whichRead MoreProject on Ultrasound12332 Words   |  50 Pageshear the sound waves between 20 Hz to 20 kHz. This frequency ran ge is known as â€Å"Audio Frequency Range†. The sound waves having frequencies above this audible range is known as â€Å"Ultrasonic Waves† or â€Å"Supersonic Waves†. Supersonic waves have the velocities higher than the velocity of sound i.e. more than 1200 km / hour. Ultrasonic waves can not be heard by a human being but a cat or dog may hear them. The wavelengths of ultrasonic waves are very small as compared to audible sound. The sound waves whichRead MoreHistory of the Ultrasonic Technology Essay719 Words   |  3 Pagesconducted research by Pierre Curie 1880. Pierre Curie he discover asymmetrical crystals like Rochelle salt and quartz can generate electricity charge once mechanical pressure is applied. So it is obtained mechanical vibrations from applying electrical oscillations to the crystals. The frequency of Ultrasonic wave should be higher than 20,000 Hz. (Sound waves). After of all of the research of ultrasonic technology the first ULTRA SONIC MACHINING (USM) built 1950 s. United States develop Ultrasonic machining

Om Heizer Om10 Ism 04 Free Essays

Chapter FORECASTING Discussion Questions 1.? Qualitative models incorporate subjective factors into the forecasting model. Qualitative models are useful when subjective factors are important. We will write a custom essay sample on Om Heizer Om10 Ism 04 or any similar topic only for you Order Now When quantitative data are difficult to obtain, qualitative models may be appropriate. 2.? Approaches are qualitative and quantitative. Qualitative is relatively subjective; quantitative uses numeric models. 3.? Short-range (under 3 months), medium-range (3 months to 3 years), and long-range (over 3 years). 4.? The steps that should be used to develop a forecasting system are: (a)? Determine the purpose and use of the forecast (b)? Select the item or quantities that are to be forecasted (c)? Determine the time horizon of the forecast (d)? Select the type of forecasting model to be used (e)? Gather the necessary data (f)? Validate the forecasting model (g)? Make the forecast (h)? Implement and evaluate the results 5.? Any three of: sales planning, production planning and budgeting, cash budgeting, analyzing various operating plans. 6.? There is no mechanism for growth in these models; they are built exclusively from historical demand values. Such methods will always lag trends. .? Exponential smoothing is a weighted moving average where all previous values are weighted with a set of weights that decline exponentially. 8.? MAD, MSE, and MAPE are common measures of forecast accuracy. To find the more accurate forecasting model, forecast with each tool for several periods where the demand outcome is known, and calculate MSE, MAPE, or MAD for each. The smaller error indicates the better forecast. 9.? The Delphi technique involves: (a)? Assembling a group of experts in such a manner as to preclude direct communication between identifiable members of the group (b)? Assembling the responses of each expert to the questions or problems of interest (c)? Summarizing these responses (d)? Providing each expert with the summary of all responses (e)? Asking each expert to study the summary of the responses and respond again to the questions or problems of interest. (f)? Repeating steps (b) through (e) several times as necessary to obtain convergence in responses. If convergence has not been obtained by the end of the fourth cycle, the responses at that time should probably be accepted and the process terminated—little additional convergence is likely if the process is continued. 0.? A time series model predicts on the basis of the assumption that the future is a function of the past, whereas an associative model incorporates into the model the variables of factors that might influence the quantity being forecast. 11.? A time series is a sequence of evenly spaced data points with the four components of trend, seasonality, cyclical, and random vari ation. 12.? When the smoothing constant, (, is large (close to 1. 0), more weight is given to recent data; when ( is low (close to 0. 0), more weight is given to past data. 13.? Seasonal patterns are of fixed duration and repeat regularly. Cycles vary in length and regularity. Seasonal indices allow â€Å"generic† forecasts to be made specific to the month, week, etc. , of the application. 14.? Exponential smoothing weighs all previous values with a set of weights that decline exponentially. It can place a full weight on the most recent period (with an alpha of 1. 0). This, in effect, is the naive approach, which places all its emphasis on last period’s actual demand. 15.? Adaptive forecasting refers to computer monitoring of tracking signals and self-adjustment if a signal passes its present limit. 16.? Tracking signals alert the user of a forecasting tool to periods in which the forecast was in significant error. 17.? The correlation coefficient measures the degree to which the independent and dependent variables move together. A negative value would mean that as X increases, Y tends to fall. The variables move together, but move in opposite directions. 18.? Independent variable (x) is said to explain variations in the dependent variable (y). 19.? Nearly every industry has seasonality. The seasonality must be filtered out for good medium-range planning (of production and inventory) and performance evaluation. 20.? There are many examples. Demand for raw materials and component parts such as steel or tires is a function of demand for goods such as automobiles. 21.? Obviously, as we go farther into the future, it becomes more difficult to make forecasts, and we must diminish our reliance on the forecasts. Ethical Dilemma This exercise, derived from an actual situation, deals as much with ethics as with forecasting. Here are a few points to consider:  ¦ No one likes a system they don’t understand, and most college presidents would feel uncomfortable with this one. It does offer the advantage of depoliticizing the funds al- location if used wisely and fairly. But to do so means all parties must have input to the process (such as smoothing constants) and all data need to be open to everyone.  ¦ The smoothing constants could be selected by an agreed-upon criteria (such as lowest MAD) or could be based on input from experts on the board as well as the college.  ¦ Abuse of the system is tied to assigning alphas based on what results they yield, rather than what alphas make the most sense.  ¦ Regression is open to abuse as well. Models can use many years of data yielding one result or few years yielding a totally different forecast. Selection of associative variables can have a major impact on results as well. Active Model Exercises* ACTIVE MODEL 4. 1: Moving Averages 1.? What does the graph look like when n = 1? The forecast graph mirrors the data graph but one period later. 2.? What happens to the graph as the number of periods in the moving average increases? The forecast graph becomes shorter and smoother. 3.? What value for n minimizes the MAD for this data? n = 1 (a naive forecast) ACTIVE MODEL 4. 2: Exponential Smoothing 1.? What happens to the graph when alpha equals zero? The graph is a straight line. The forecast is the same in each period. 2.? What happens to the graph when alpha equals one? The forecast follows the same pattern as the demand (except for the first forecast) but is offset by one period. This is a naive forecast. 3.? Generalize what happens to a forecast as alpha increases. As alpha increases the forecast is more sensitive to changes in demand. *Active Models 4. 1, 4. 2, 4. 3, and 4. 4 appear on our Web site, www. pearsonhighered. com/heizer. 4.? At what level of alpha is the mean absolute deviation (MAD) minimized? alpha = . 16 ACTIVE MODEL 4. 3: Exponential Smoothing with Trend Adjustment .? Scroll through different values for alpha and beta. Which smoothing constant appears to have the greater effect on the graph? alpha 2.? With beta set to zero, find the best alpha and observe the MAD. Now find the best beta. Observe the MAD. Does the addition of a trend improve the forecast? alpha = . 11, MAD = 2. 59; beta above . 6 changes the MAD (by a little) to 2. 54. ACT IVE MODEL 4. 4: Trend Projections 1.? What is the annual trend in the data? 10. 54 2.? Use the scrollbars for the slope and intercept to determine the values that minimize the MAD. Are these the same values that regression yields? No, they are not the same values. For example, an intercept of 57. 81 with a slope of 9. 44 yields a MAD of 7. 17. End-of-Chapter Problems [pic] (b) | | |Weighted | |Week of |Pints Used |Moving Average | |August 31 |360 | | |September 7 |389 |381 ( . 1 = ? 38. 1 | |September 14 |410 |368 ( . 3 = 110. 4 | |September 21 |381 |374 ( . 6 = 224. 4 | |September 28 |368 |372. | |October 5 |374 | | | |Forecast 372. 9 | | (c) | | | |Forecasting | Error | | |Week of |Pints |Forecast |Error |( . 20 |Forecast| |August 31 |360 |360 |0 |0 |360 | |September 7 |389 |360 |29 |5. 8 |365. 8 | |September 14 |410 |365. 8 |44. 2 |8. 84 |374. 64 | |September 21 |381 |374. 64 |6. 36 |1. 272 |375. 12 | |September 28 |368 |375. 912 |–7. 912 |–1. 5824 |374. 3296| |October 5 |374 |374. 3296 |–. 3296 |–. 06592 |374. 2636| The forecast is 374. 26. (d)? The three-year moving average appears to give better results. [pic] [pic] Naive tracks the ups and downs best but lags the data by one period. Exponential smoothing is probably better because it smoothes the data and does not have as much variation. TEACHING NOTE: Notice how well exponential smoothing forecasts the naive. [pic] (c)? The banking industry has a great deal of seasonality in its processing requirements [pic] b) | | |Two-Year | | | |Year |Mileage |Moving Average |Error ||Error| | |1 |3,000 | | | | | |2 |4,000 | | | | | |3 |3,400 |3,500 |–100 | |100 | |4 |3,800 |3,700 |100 | |100 | |5 |3,700 |3,600 |100 | |100 | | | |Totals| |100 | | |300 | | [pic] 4. 5? (c)? Weighted 2 year M. A. ith . 6 weight for most recent year. |Year |Mileage |Forecast |Error ||Error| | |1 |3,000 | | | | |2 |4,000 | | | | |3 |3,400 |3,600 |–200 |200 | |4 |3,800 |3,640 |160 |160 | |5 |3,700 |3,640 |60 |60 | | | | | | | 420 | | Forecast for year 6 is 3,740 miles. [pic] 4. 5? (d) | | |Forecast |Error ( |New | |Year |Mileage |Forecast |Error |( = . 50 |Forecast | |1 |3,000 |3,000 | ?0 | 0 |3,000 | |2 |4,000 |3,000 |1,000 |500 |3,500 | |3 |3,400 |3,500 | –100 |–50 |3,450 | |4 |3,800 |3,450 | 350 |175 |3,625 | |5 |3,700 |3,625 | 75 |? 38 |3,663 | | | |Total |1,325| | | | The forecast is 3,663 miles. 4. 6 |Y Sales |X Period |X2 |XY | |January |20 |1 |1 |20 | |February |21 |2 |4 |42 | |March |15 |3 |9 |45 | |April |14 |4 |16 |56 | |May |13 |5 |25 |65 | |June |16 |6 |36 |96 | |July |17 |7 |49 |119 | |August |18 |8 |64 |144 | |September |20 |9 |81 |180 | |October |20 |10 |100 |200 | |November |21 |11 |121 |231 | |December |23 |12 |144 |276 | |Sum | 18 |78 |650 |1,474 | |Average |? 18. 2 | 6. 5 | | | (a) [pic] (b)? [i]? NaiveThe coming January = December = 23 [ii]? 3-month moving (20 + 21 + 23)/3 = 21. 33 [iii]? 6-month weighted [(0. 1 ( 17) + (. 1 ( 18) + (0. 1 ( 20) + (0. 2 ( 20) + (0. 2 ( 21) + (0. 3 ( 23)]/1. 0 = 20. 6 [iv]? Exponential smoothing with alpha = 0. 3 [pic] [v]? Trend? [pic] [pic] Forecast = 15. 73? +?. 38(13) = 20. 67, where next January is the 13th month. (c)? Only trend provides an equation that can extend beyond one month 4. 7? Present = Period (week) 6. a) So: where [pic] )If the weights are 20, 15, 15, and 10, there will be no change in the forecast because these are the same relative weights as in part (a), i. e. , 20/60, 15/60, 15/60, and 10/60. c)If the weights are 0. 4, 0. 3, 0. 2, and 0. 1, then the forecast becomes 56. 3, or 56 patients. [pic] [pic] |Temperature |2 day M. A. | |Error||(Error)2| Absolute |% Error | |93 |— | — |— |— | |94 |— | — |— |— | |93 |93. 5 | 0. 5 |? 0. 25| 100(. 5/93) | = 0. 54% | |95 |93. 5 | 1. 5 | ? 2. 25| 100(1. 5/95) | = 1. 58% | |96 |94. 0 | 2. 0 |? 4. 00| 100(2/96) | = 2. 08% | |88 |95. 5 | 7. | 56. 25| 100(7. 5/88) | = 8. 52% | |90 |92. 0 | 2. 0 |? 4. 00| 100(2/90) | = 2. 22% | | | | |13. 5| | | 66. 75 | | |14. 94% | MAD = 13. 5/5 = 2. 7 (d)? MSE = 66. 75/5 = 13. 35 (e)? MAPE = 14. 94%/5 = 2. 99% 4. 9? (a, b) The computations for both the two- and three- month averages appear in the table; the results appear in the figure below. [pic] (c)? MAD (two-month moving average) = . 750/10 = . 075 MAD (three-month moving average) = . 793/9 = . 088 Therefore, the two-month moving average seems to have performed better. [pic] (c)? The forecasts are about the same. [pic] 4. 12? t |Day |Actual |Forecast | | | | |Demand |Demand | | |1 |Monday |88 |88 | | |2 |Tuesday |72 |88 | | |3 |Wednesday |68 |84 | | |4 |Thursday |48 |80 | | |5 |Friday | |72 |( Answer | Ft = Ft–1 + ((At–1 – Ft–1) Let ( = . 25. Let Monday forecast demand = 88 F2 = 88 + . 25(88 – 88) = 88 + 0 = 88 F3 = 88 + . 25(72 – 88) = 88 – 4 = 84 F4 = 84 + . 25(68 – 84) = 84 – 4 = 80 F5 = 80 + . 25(48 – 80) = 80 – 8 = 72 4. 13? (a)? Exponential smoothing, ( = 0. 6: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 6(45–41) = 43. 4 |6. 6 | |3 |52 | 43. 4 + 0. 6(50–43. 4) = 47. 4 |4. 6 | |4 |56 |47. 4 + 0. 6(52–47. 4) = 50. 2 |5. 8 | |5 |58 |50. 2 + 0. 6(56–50. 2) = 53. 7 |4. 3 | |6 |? |53. 7 + 0. 6(58–53. 7) = 56. 3 | | ( = 25. 3 MAD = 5. 06 Exponential smoothing, ( = 0. 9: | | |Exponential |Absolute | |Year |Demand |Smoothing ( = 0. |Deviation | |1 |45 |41 |4. 0 | |2 |50 |41. 0 + 0. 9(45–41) = 44. 6 |5. 4 | |3 |52 |44. 6 + 0. 9(50–44. 6 ) = 49. 5 |2. 5 | |4 |56 |49. 5 + 0. 9(52–49. 5) = 51. 8 |4. 2 | |5 |58 |51. 8 + 0. 9(56–51. 8) = 55. 6 |2. 4 | |6 |? |55. 6 + 0. 9(58–55. 6) = 57. 8 | | ( = 18. 5 MAD = 3. 7 (b)? 3-year moving average: | | |Three-Year |Absolute | |Year |Demand |Moving Average |Deviation | |1 45 | | | |2 |50 | | | |3 |52 | | | |4 |56 |(45 + 50 + 52)/3 = 49 |7 | |5 |58 | (50 + 52 + 56)/3 = 52. 7 |5. 3 | |6 |? | (52 + 56 + 58)/3 = 55. 3 | | ( = 12. 3 MAD = 6. 2 (c)? Trend projection: | | | |Absolute | |Year |Demand |Trend Projection |Deviation | |1 | 45 |42. 6 + 3. 2 ( 1 = 45. 8 |0. 8 | |2 |50 |42. 6 + 3. 2 ( 2 = 49. 0 |1. 0 | |3 |52 |42. 6 + 3. 2 ( 3 = 52. 2 |0. 2 | |4 |56 |42. 6 + 3. 2 ( 4 = 55. 4 |0. | |5 |58 |42. 6 + 3. 2 ( 5 = 58. 6 |0. 6 | |6 |? |42. 6 + 3. 2 ( 6 = 61. 8 | | ( = 3. 2 MAD = 0. 64 [pic] | X |Y |XY |X2 | | 1 |45 | 45 | 1 | | 2 |50 |100 | 4 | | 3 |52 |156 | 9 | | 4 |56 |224 |16 | | 5 |58 |290 |25 | Then: (X = 15, (Y = 261, (XY = 815, (X2 = 55, [pic]= 3, [pic]= 52. 2 Therefore: [pic] (d)? Comparing the results of the forecasting methodologies for parts (a), (b), and (c). |Forecast Methodology |MAD | |Exponential smoothing, ( = 0. |5. 06 | |Exponential smoothing, ( = 0. 9 |3. 7 | |3-year moving average |6. 2 | |Trend projection |0. 64 | Based on a mean absolute deviation criterion, the trend projection is to be preferred over the exponential smoothing with ( = 0. 6, exponential smoothing with ( = 0. 9, or the 3-year moving average forecast methodologies. 4. 14 Method 1:MAD: (0. 20 + 0. 05 + 0. 05 + 0. 20)/4 = . 125 ( better MSE : (0. 04 + 0. 0025 + 0. 0025 + 0. 04)/4 = . 021 Method 2:MAD: (0. 1 + 0. 20 + 0. 10 + 0. 11) / 4 = . 1275 MSE : (0. 01 + 0. 04 + 0. 01 + 0. 0121) / 4 = . 018 ( better 4. 15 | |Forecast Three-Year |Absolute | |Year |Sales |Moving Average |Deviation | |2005 |450 | | | |2006 |495 | | | |2007 |518 | | | |2008 |563 |(450 + 495 + 518)/3 = 487. 7 |75. 3 | |2009 |584 |(495 + 518 + 563)/3 = 525. 3 |58. 7 | |2010 | |(518 + 563 + 584)/3 = 555. 0 | | | | | ( = 134 | | | | MAD = 67 | 4. 16 Year |Time Period X |Sales Y |X2 |XY | |2005 |1 |450 | 1 |450 | |2006 |2 |495 | 4 |990 | |2007 |3 |518 | 9 |1554 | |2008 |4 |563 |16 |2252 | |2009 |5 |584 |25 |2920 | | | | ( = 2610| |( = 55 | |( = 8166 | [pic] [pic] |Year |Sales |Forecast Trend |Absolute Deviation | |2005 |450 |454. 8 |4. 8 | |2006 |495 |488. 4 |6. | |2007 |518 |522. 0 |4. 0 | |2008 |563 |555. 6 |7. 4 | |2009 |584 |589. 2 |5. 2 | |2010 | |622. 8 | | | | | | ( = 28 | | | | | MAD = 5. 6 | 4. 17 | | |Forecast Exponential |Absolu te | |Year |Sales |Smoothing ( = 0. 6 |Deviation | |2005 |450 |410. 0 |40. | |2006 |495 |410 + 0. 6(450 – 410) = 434. 0 |61. 0 | |2007 |518 |434 + 0. 6(495 – 434) = 470. 6 |47. 4 | |2008 |563 |470. 6 + 0. 6(518 – 470. 6) = 499. 0 |64. 0 | |2009 |584 |499 + 0. 6(563 – 499) = 537. 4 |46. 6 | |2010 | |537. 4 + 0. 6(584 – 537. 4) = 565. 6 | | | | | ( = 259 | | | | MAD = 51. 8 | | | |Forecast Exponential |Absolute | |Year |Sales |Smoothing ( = 0. |Deviation | |2005 |450 |410. 0 |40. 0 | |2006 |495 |410 + 0. 9(450 – 410) = 446. 0 |49. 0 | |2007 |518 |446 + 0. 9(495 – 446) = 490. 1 |27. 9 | |2008 |563 |490. 1 + 0. 9(518 – 490. 1) = 515. 2 |47. 8 | |2009 |584 |515. 2 + 0. 9(563 – 515. 2) = 558. 2 |25. 8 | |2010 | |558. 2 + 0. 9(584 – 558. 2) = 581. 4 | | | | |( = 190. 5 | | | |MAD = 38. 1 | (Refer to Solved Problem 4. 1) For ( = 0. 3, absolute deviations for 2005–2009 are 40. 0, 73. 0, 74. 1, 96. 9, 88. 8, respectively. So the MAD = 372. 8/5 = 74. 6. [pic] Because it gives the lowest MAD, the smoothing constant of ( = 0. 9 gives the most accurate forecast. 4. 18? We need to find the smoothing constant (. We know in general that Ft = Ft–1 + ((At–1 – Ft–1); t = 2, 3, 4. Choose either t = 3 or t = 4 (t = 2 won’t let us find ( because F2 = 50 = 50 + ((50 – 50) holds for any (). Let’s pick t = 3. Then F3 = 48 = 50 + ((42 – 50) or 48 = 50 + 42( – 50( or –2 = –8( So, . 25 = ( Now we can find F5 : F5 = 50 + ((46 – 50) F5 = 50 + 46( – 50( = 50 – 4( For ( = . 25, F5 = 50 – 4(. 25) = 49 The forecast for time period 5 = 49 units. 4. 19? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 2 | | |Unadjusted | |Adjusted | | | |Month |Income |Forecast |Trend |Forecast ||Error||Error2 | |February |70. 0 | 65. 0 | 0. 0 | 65 |? 5. 0 |? 25. 0 | |March |68. 5 | 65. 5 | 0. 1 | 65. 6 |? 2. 9 |? 8. 4 | |April |64. 8 | 65. 9 | 0. 16 |66. 05 |? 1. 2 |? 1. 6 | |May |71. 7 | 65. 92 | 0. 13 |66. 06 |? 5. 6 |? 31. 9 | |June |71. | 66. 62 | 0. 25 |66. 87 |? 4. 4 |? 19. 7 | |July |72. 8 | 67. 31 | 0. 33 |67. 64 |? 5. 2 |? 26. 6 | |August | | 68. 16 | |68. 60 | |24. 3| | |113. 2| | MAD = 24. 3/6 = 4. 05, MSE = 113. 2/6 = 18. 87. Note that all numbers are rounded. Note: To use POM for Windows to solve this problem, a period 0, which contains the initial forecast and initial trend, must be added. 4. 20? Trend adjusted exponential smoothing: ( = 0. 1, ( = 0. 8 [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] [pic] 4. 23? Students must determine the naive forecast for the four months. The naive forecast for March is the February actual of 83, etc. |(a) | |Actual |Forecast ||Error| ||% Error| | | |March |101 |120 |19 |100 (19/101) = 18. 81% | | |April |? 96 |114 |18 |100 (18/96) ? = 18. 75% | | |May |? 89 |110 |21 |100 (21/89) ? = 23. 60% | | |June |108 |108 |? 0 |100 (0/108) ? = 0% | | | | | | |58 | | | 61. 16% | [pic] |(b)| |Actual |Naive ||Error| ||% Error| | | |March |101 |? 83 |18 |100 (18/101) = 17. 82% | | |April |? 96 |101 |? |100 (5/96) ? = 5. 21% | | |May |? 89 |? 96 |? 7 |100 (7/89) ? =? 7. 87% | | |June |108 |? 89 |19 |100 (19/108) = 17. 59% | | | | | | |49| | |48. 49% | | [pic] Naive outperforms management. (c)? MAD for the manager’s technique is 14. 5, while MAD for the naive forecast is only 12. 25. MAPEs are 15. 29% and 12. 12%, respectively. So the naive method is better. 4. 24? (a)? Graph of demand The observations obviously do not form a straight line but do tend to cluster about a straight line over the range shown. (b)? Least-squares re gression: [pic] Assume Appearances X |Demand Y |X2 |Y2 |XY | |3 | 3 | 9 | 9 | 9 | |4 | 6 |16 | 36 |24 | |7 | 7 |49 | 49 |49 | |6 | 5 |36 | 25 |30 | |8 |10 |64 |100 |80 | |5 | 7 |25 | 49 |35 | |9 | ? | | | | (X = 33, (Y = 38, (XY = 227, (X2 = 199, [pic]= 5. 5, [pic]= 6. 33. Therefore: [pic] The following figure shows both the data and the resulting equation: [pic] (c) If there are nine performances by Stone Temple Pilots, the estimated sales are: (d) R = . 82 is the correlation coefficient, and R2 = . 68 means 68% of the variation in sales can be explained by TV appearances. 4. 25? |Number of | | | | | |Accidents | | | | |Month |(y) |x |xy |x2 | |January | 30 | 1 | 30 | 1 | |February | 40 | 2 | 80 | 4 | |March | 60 | 3 |180 | 9 | |April | 90 | 4 |360 |16 | |? Totals | |220 | | | [pic] The regression line is y = 5 + 20x. The forecast for May (x = 5) is y = 5 + 20(5) = 105. 4. 26 |Season |Year1 |Year2 |Average |Average |Seasonal |Year3 | | |Demand |Demand |Year1(Year2 |Season |Index |D emand | | | | |Demand |Demand | | | |Fall |200 |250 |225. 0 |250 |0. 90 |270 | |Winter |350 |300 |325. |250 |1. 30 |390 | |Spring |150 |165 |157. 5 |250 |0. 63 |189 | |Summer |300 |285 |292. 5 |250 |1. 17 |351 | 4. 27 | | Winter |Spring |Summer |Fall | |2006 |1,400 |1,500 |1,000 |600 | |2007 |1,200 |1,400 |2,100 |750 | |2008 |1,000 |1,600 |2,000 |650 | |2009 | 900 |1,500 |1,900 | 500 | | |4,500 |6,000 |7,000 |2,500 | 4. 28 | | | | |Average | | | | | | |Average |Quarterly |Seasonal | |Quarter |2007 |2008 |2009 |Demand |Demand |Index | |Winter | 73 | 65 | 89 | 75. 67 |106. 67 |0. 709 | |Spring |104 | 82 |146 |110. 67 |106. 67 |1. 037 | |Summer |168 |124 |205 |165. 67 |106. 67 |1. 553 | |Fall | 74 | 52 | 98 | 74. 67 |106. 67 |0. 700 | 4. 29? 2011 is 25 years beyond 1986. Therefore, the 2011 quarter numbers are 101 through 104. | | | | |(5) | | |(2) |(3) |(4) |Adjusted | |(1) |Quarter |Forecast |Seasonal |Forecast | |Quarter |Number |(77 + . 3Q) |Factor |[(3) ( (4)] | |Winter |101 |120. 43 | . 8 | 96. 344 | |Spring |102 |120. 86 |1. 1 |132. 946 | |Summer |103 |121. 29 |1. 4 |169. 806 | |Fall |104 |121. 72 | . 7 | 85. 204 | 4. 30? Given Y = 36 + 4. 3X (a) Y = 36 + 4. 3(70) = 337 (b) Y = 36 + 4. 3(80) = 380 (c) Y = 36 + 4. 3(90) = 423 4. 31 4. 33? (a)? See the table below. For next year (x = 6), the number of transistors (in millions) is forecasted as y = 126 + 18(6) = 126 + 108 = 234. Then y = a + bx, where y = number sold, x = price, and |4. 32? a) | x |y |xy |x2 | | | 16 | 330 | 5,280 |256 | | | 12 | 270 | 3,240 |144 | | | 18 | 380 | 6,840 |324 | | | 14 | 300 | 4,200 |196 | | | 60 |1,280 |19,560 |920 | So at x = 2. 80, y = 1,454. 6 – 277. 6($2. 80) = 677. 32. Now round to the nearest integer: Answer: 677 lattes. [pic] (b)? If the forecast is for 20 guests, the bar sales forecast is 50 + 18(20) = $410. Each guest accounts for an additional $18 in bar sales. |Table for Problem 4. 33 | | | | | |Year |Transistors | | | | | | | |(x) |(y) |xy |x2 |126 + 18x |Err or |Error2 ||% Error| | | |? 1 |140 |? 140 |? 1 |144 |–4 |? 16 |100 (4/140)? = 2. 86% | | |? 2 |160 |? 320 |? 4 |162 |–2 | 4 |100 (2/160)? = 1. 25% | | |? 3 |190 |? 570 |? 9 |180 |10 |100 |100 (10/190) = 5. 26% | | |? 4 |200 |? 800 |16 |198 |? 2 | 4 |100 (2/200) = 1. 00% | | |? |210 |1,050 |25 |216 |–6 |? 36 |100 (6/210)? = 2. 86% | |Totals |15 | | |900 | | |2,800 | | (b)? MSE = 160/5 = 32 (c)? MAPE = 13. 23%/5 = 2. 65% 4. 34? Y = 7. 5 + 3. 5X1 + 4. 5X2 + 2. 5X3 (a)? 28 (b)? 43 (c)? 58 4. 35? (a)? [pic] = 13,473 + 37. 65(1860) = 83,502 (b)? The predicted selling price is $83,502, but this is the average price for a house of this size. There are other factors besides square footage that will impact the selling price of a house. If such a house sold for $95,000, then these other factors could be contributing to the additional value. (c)? Some other quantitative variables would be age of the house, number of bedrooms, size of the lot, and size of the garage, etc. (d)? Coefficient of determination = (0. 63)2 = 0. 397. This means that only about 39. 7% of the variability in the sales price of a house is explained by this regression model that only includes square footage as the explanatory variable. 4. 36? (a)? Given: Y = 90 + 48. 5X1 + 0. 4X2 where: [pic] If: Number of days on the road ( X1 = 5 and distance traveled ( X2 = 300 then: Y = 90 + 48. 5 ( 5 + 0. 4 ( 300 = 90 + 242. 5 + 120 = 452. 5 Therefore, the expected cost of the trip is $452. 50. (b)? The reimbursement request is much higher than predicted by the model. This request should probably be questioned by the accountant. (c)? A number of other variables should be included, such as: 1.? the type of travel (air or car) 2.? conference fees, if any 3.? costs of entertaining customers 4.? other transportation costs—cab, limousine, special tolls, or parking In addition, the correlation coefficient of 0. 68 is not exceptionally high. It indicates that the model explains approximately 46% of the overall variation in trip cost. This correlation coefficient would suggest that the model is not a particularly good one. 4. 37? (a, b) |Period |Demand |Forecast |Error |Running sum ||error| | | 1 |20 |20 |0. 00 |0. 00 |0. 00 | | 2 |21 |20 |1. 00 |1. 0 |1. 00 | | 3 |28 |20. 5 |7. 50 |8. 50 |7. 50 | | 4 |37 |24. 25 |12. 75 |21. 25 |12. 75 | | 5 |25 |30. 63 |–5. 63 |15. 63 |5. 63 | | 6 |29 |27. 81 |1. 19 |16. 82 |1. 19 | | 7 |36 |28. 41 |7. 59 |24. 41 |7. 59 | | 8 |22 |32. 20 |–10. 20 |14. 21 |10. 20 | | 9 |25 |27. 11 |–2. 10 |12. 10 |2. 10 | |10 |28 |26. 05 | 1. 95 |14. 05 | | | | | | |1. 95 | | | | | | | | | | | | | | | |MAD[pic]5. 00 | Cumulative error = 14. 05; MAD = 5? Tracking = 14. 05/5 ( 2. 82 4. 38? (a)? least squares equation: Y = –0. 158 + 0. 1308X (b)? Y = –0. 158 + 0. 1308(22) = 2. 719 million (c)? coefficient of correlation = r = 0. 966 coefficient of determination = r2 = 0. 934 4. 39 |Year X |Patients Y |X2 |Y2 |XY | |? 1 |? 36 | 1 |? 1,296 | 36 | |? 2 |? 33 | |? 1,089 | 66 | |? 3 |? 40 | 9 |? 1,600 |? 120 | |? 4 |? 41 |? 16 |? 1,681 |? 164 | |? 5 |? 40 |? 25 |? 1,600 |? 200 | |? 6 |? 55 |? 36 |? 3,025 |? 330 | |? 7 |? 60 |? 49 |? 3,600 |? 420 | |? 8 |? 54 |? 64 |? 2,916 |? 432 | |? 9 |? 58 |? 81 |? 3,364 |? 522 | |10 |? 61 |100 |? 3,721 |? 10 | |55 | | |478 | | |X |Y |Forecast |Deviation |Deviation | |? 1 |36 |29. 8 + 3. 28 ( ? 1 = 33. 1 |? 2. 9 |2. 9 | |? 2 |33 |29. 8 + 3. 28 ( ? 2 = 36. 3 |–3. 3 |3. 3 | |? 3 |40 |29. 8 + 3. 28 ( ? 3 = 39. 6 |? 0. 4 |0. 4 | |? 4 |41 |29. 8 + 3. 28 ( ? 4 = 42. 9 |–1. 9 |1. 9 | |? 5 |40 |29. 8 + 3. 28 ( ? 5 = 46. 2 |–6. 2 |6. 2 | |? 6 |55 |29. 8 + 3. 28 ( ? 6 = 49. 4 |? 5. 6 |5. 6 | |? 7 |60 |29. 8 + 3. 28 ( ? 7 = 52. 7 |? 7. 3 |7. 3 | |? |54 |29. 8 + 3. 28 ( ? 8 = 56. 1 |–2. 1 |2. 1 | |? 9 |58 |29. 8 + 3. 28 ( ? 9 = 59. 3 |–1. 3 |1. 3 | |10 |61 |29. 8 + 3. 28 ( 10 = 62. 6 |–1. 6 |1. 6 | | | | | | ( = | | | | | |32. 6 | | | | | |MAD = 3. 26 | The MAD is 3. 26—this is approximately 7% of the average number of patients and 10% of the minimum number of patients. We also see absolute deviations, for years 5, 6, and 7 in the range 5. 6–7. 3. The comparison of the MAD with the average and minimum number of patients and the comparatively large deviations during the middle years indicate that the forecast model is not exceptionally accurate. It is more useful for predicting general trends than the actual number of patients to be seen in a specific year. 4. 40 | |Crime |Patients | | | | |Year |Rate X |Y |X2 |Y2 |XY | |? 1 |? 58. 3 |? 36 |? 3,398. 9 |? 1,296 |? 2,098. 8 | |? 2 |? 61. 1 |? 33 |? 3,733. 2 |? 1,089 |? 2,016. 3 | |? 3 |? 73. |? 40 |? 5,387. 6 |? 1,600 |? 2,936. 0 | |? 4 |? 75. 7 |? 41 |? 5,730. 5 |? 1,681 |? 3,103. 7 | |? 5 |? 81. 1 |? 40 |? 6,577. 2 |? 1,600 |? 3,244. 0 | |? 6 |? 89. 0 |? 55 |? 7,921. 0 |? 3,025 |? 4,895. 0 | |? 7 |101. 1 |? 60 |10,221. 2 |? 3,600 |? 6,066. 0 | |? 8 |? 94. 8 |? 54 |? 8,987. 0 |? 2,916 |? 5,119. 2 | |? 9 |103. 3 |? 58 |10,670. 9 |? 3,364 |? 5,991. 4 | |10 |116. 2 |? 61 |13,502. 4 |? 3,721 |? 7,088. 2 | |Column | |854. | | |478 | |Totals | | | | | | |months) |(Millions) |(1,000,00 0s) | | | | |Year |(X) |(Y) |X2 |Y2 |XY | |? 1 |? 7 |1. 5 |? 49 |? 2. 25 |10. 5 | |? 2 |? 2 |1. 0 | 4 |? 1. 00 |? 2. 0 | |? 3 |? 6 |1. 3 |? 36 |? 1. 69 |? 7. 8 | |? 4 |? 4 |1. 5 |? 16 |? 2. 25 |? 6. 0 | |? 5 |14 |2. 5 |196 |? 6. 25 |35. 0 | |? 6 |15 |2. 7 |225 |? 7. 9 |40. 5 | |? 7 |16 |2. 4 |256 |? 5. 76 |38. 4 | |? 8 |12 |2. 0 |144 |? 4. 00 |24. 0 | |? 9 |14 |2. 7 |196 |? 7. 29 |37. 8 | |10 |20 |4. 4 |400 |19. 36 |88. 0 | |11 |15 |3. 4 |225 |11. 56 |51. 0 | |12 |? 7 |1. 7 |? 49 |? 2. 89 |11. 9 | Given: Y = a + bX where: [pic] and (X = 132, (Y = 27. 1, (XY = 352. 9, (X2 = 1796, (Y2 = 71. 59, [pic] = 11, [pic]= 2. 26. Then: [pic] andY = 0. 511 + 0. 159X (c)? Given a tourist population of 10,000,000, the model predicts a ridership of: Y = 0. 511 + 0. 159 ( 10 = 2. 101, or 2,101,000 persons. (d)? If there are no tourists at all, the model predicts a ridership of 0. 511, or 511,000 persons. One would not place much confidence in this forecast, however, because the number of tourists (zero) is outside the range of data used to develop the model. (e)? The standard error of the estimate is given by: (f)? The correlation coefficient and the coefficient of determination are given by: [pic] 4. 42? (a)? This problem gives students a chance to tackle a realistic problem in business, i. e. , not enough data to make a good forecast. As can be seen in the accompanying figure, the data contains both seasonal and trend factors. [pic] Averaging methods are not appropriate with trend, seasonal, or other patterns in the data. Moving averages smooth out seasonality. Exponential smoothing can forecast January next year, but not farther. Because seasonality is strong, a naive model that students create on their own might be best. (b) One model might be: Ft+1 = At–11 That is forecastnext period = actualone year earlier to account for seasonality. But this ignores the trend. One very good approach would be to calculate the increase from each month last year to each month this year, sum all 12 increases, and divide by 12. The forecast for next year would equal the value for the same month this year plus the average increase over the 12 months of last year. (c) Using this model, the January forecast for next year becomes: [pic] where 148 = total monthly increases from last year to this year. The forecasts for each of the months of next year then become: |Jan. |29 | |July. |56 | |Feb. |26 | |Aug. |53 | |Mar. |32 | |Sep. |45 | |Apr. |35 | |Oct. |35 | |May. |42 | |Nov. |38 | |Jun. |50 | |Dec. |29 | Both history and forecast for the next year are shown in the accompanying figure: [pic] 4. 3? (a) and (b) See the following table: | |Actual |Smoothed | |Smoothed | | |Week |Value |Value |Forecast |Value |Forecast | |t |A(t) |Ft (( = 0. 2) |Error |Ft (( = 0. 6)|Error | | 1 |50 |+50. 0 |? +0. 0 |+50. 0 |? +0. 0 | | 2 |35 |+50. 0 |–15. 0 |+50. 0 |–15. 0 | | 3 |25 |+47. 0 |–22. 0 |+41. 0 |–16. 0 | | 4 |40 |+42. 6 |? –2. 6 |+31. 4 |? +8. 6 | | 5 |45 |+42. 1 |? –2. 9 |+36. 6 |? +8. | | 6 |35 |+42. 7 |? –7. 7 |+41. 6 |? –6. 6 | | 7 |20 |+41. 1 |–21. 1 |+37. 6 |–17. 6 | | 8 |30 |+36. 9 |? –6. 9 |+27. 1 |? +2. 9 | | 9 |35 |+35. 5 |? –0. 5 |+28. 8 |? +6. 2 | |10 |20 |+35. 4 |–15. 4 |+32. 5 |–12. 5 | |11 |15 |+32. 3 |–17. 3 |+25. 0 |–10. 0 | |12 |40 |+28. 9 |+11. 1 |+19. 0 |+21. 0 | |13 |55 |+31. 1 |+23. 9 |+31. 6 |+23. 4 | |14 |35 |+35. 9 |? 0. 9 |+45. 6 |–10. 6 | |15 |25 |+36. 7 |–10. 7 |+39. 3 |–14. 3 | |16 |55 |+33. 6 |+21. 4 |+30. 7 |+24. 3 | |17 |55 |+37. 8 |+17. 2 |+45. 3 |? +9. 7 | |18 |40 |+41. 3 |? –1. 3 |+51. 1 |–11. 1 | |19 |35 |+41. 0 |? –6. 0 |+44. 4 |? –9. 4 | |20 |60 |+39. 8 |+20. 2 |+38. 8 |+21. 2 | |21 |75 |+43. 9 |+31. 1 |+51. 5 |+23. 5 | |22 |50 |+50. 1 |? –0. 1 |+65. 6 |–15. | |23 |40 |+50. 1 |–10. 1 |+56. 2 |–16. 2 | |24 |65 |+48. 1 |+16. 9 |+46. 5 |+18. 5 | |25 | |+51. 4 | |+57. 6 | | | | | MAD = 11. 8 |MAD = 13. 45 | (c)? Students should note how stable the smoothed values are for ( = 0. 2. When compared to actual week 25 calls of 85, the smoothing constant, ( = 0. 6, appears to do a slightly better job. On the basis of the standard error of the estimate and the MAD, the 0. 2 constant is better. However, other smoothing constants need to be examined. |4. 4 | | | | | | |Week |Actual Value |Smoothed Value |Trend Estimate |Forecast |Forecast | |t |At |Ft (( = 0. 3) |Tt (( = 0. 2) |FITt |Error | |? 1 |50. 000 |50. 000 |? 0. 000 |50. 000 | 0. 000 | |? 2 |35. 000 |50. 000 |? 0. 000 |50. 000 |–15. 000 | |? 3 |25. 000 |45. 500 |–0. 900 |44. 600 |–19. 600 | |? 4 |40. 000 |38. 720 |–2. 076 |36. 644 | 3. 56 | |? 5 |45. 000 |37. 651 |–1. 875 |35. 776 | 9. 224 | |? 6 |35. 000 |38. 543 |–1. 321 |37. 222 |? –2. 222 | |? 7 |20. 000 |36. 555 |–1. 455 |35. 101 |–15. 101 | |? 8 |30. 000 |30. 571 |–2. 361 |28. 210 | 1. 790 | |? 9 |35. 000 |28. 747 |–2. 253 |26. 494 | 8. 506 | |10 |20. 000 |29. 046 |–1. 743 |27. 03 |? –7. 303 | |11 |15. 000 |25. 112 |–2. 181 |22. 931 |? –7. 931 | |12 |40. 000 |20. 552 |–2. 657 |17. 895 |? 22. 105 | |13 |55. 000 |24. 526 |–1. 331 |23. 196 |? 31. 804 | |14 |35. 000 |32. 737 |? 0. 578 |33. 315 | 1. 685 | |15 |25. 000 |33. 820 |? 0. 679 |34. 499 |? –9. 499 | |16 |55. 000 |31. 649 |? 0. 109 |31. 58 |? 23. 242 | |17 |55. 000 |38. 731 |? 1. 503 |40. 234 |? 14. 766 | |18 |40. 000 |44. 664 |? 2. 389 |47. 053 |? –7. 053 | |19 |35. 000 |44. 937 |? 1. 966 |46. 903 |–11. 903 | |20 |60. 000 |43. 332 |? 1. 252 |44. 584 |? 15. 416 | |21 |75. 000 |49. 209 |? 2. 177 |51. 386 |? 23. 614 | |22 |50. 000 |58. 470 |? 3. 94 |62. 064 |–12. 064 | |23 |40. 000 |58. 445 |? 2. 870 |61. 315 |–21. 315 | |24 |65. 000 |54. 920 |? 1. 591 |56. 511 | 8. 489 | |25 | |59. 058 |? 2. 100 |61. 158 | | To evaluate the tren d adjusted exponential smoothing model, actual week 25 calls are compared to the forecasted value. The model appears to be producing a forecast approximately mid-range between that given by simple exponential smoothing using ( = 0. 2 and ( = 0. 6. Trend adjustment does not appear to give any significant improvement. 4. 45 |Month |At |Ft ||At – Ft | |(At – Ft) | |May |100 |100 | 0 | 0 | |June | 80 |104 |24 |–24 | |July |110 | 99 |11 |11 | |August |115 |101 |14 |14 | |September |105 |104 | 1 | 1 | |October |110 |104 |6 |6 | |November |125 |105 |20 |20 | December |120 |109 |11 |11 | | | | |Sum: 87 |Sum: 39 | |4. 46 (a) | |X |Y |X2 |Y2 |XY | | |? 421 |? 2. 90 |? 177241 | 8. 41 |? 1220. 9 | | |? 377 |? 2. 93 |? 142129 | 8. 58 |? 1104. 6 | | |? 585 |? 3. 00 |? 342225 | 9. 00 |? 1755. 0 | | |? 690 |? 3. 45 |? 476100 |? 11. 90 |? 2380. 5 | | |? 608 |? 3. 66 |? 369664 |? 13. 40 |? 2225. 3 | | |? 390 |? 2. 88 |? 52100 | 8. 29 |? 1123. 2 | | |? 415 |? 2. 15 |? 172225 | 4. 62 | 892. 3 | | |? 481 |? 2. 53 |? 231361 | 6. 40 |? 1216. 9 | | |? 729 |? 3. 22 |? 531441 |? 10. 37 |? 2347. 4 | | |? 501 |? 1. 99 |? 251001 | 3. 96 | 997. 0 | | |? 613 |? 2. 75 |? 375769 | 7. 56 |? 1685. 8 | | |? 709 |? 3. 90 |? 502681 |? 15. 21 | ? 2765. 1 | | |? 366 |? 1. 60 |? 133956 | 2. 56 | 585. 6 | | |Column |6885 | |36. 6 | | | |totals | | | | | |January |400 |— |— | — |— | |February |380 |400 |— |20. 0 |— | |March |410 |398 |— |12. 0 |— | |April |375 | 399. 2 |396. 67 |24. 2 |21. 67 | |May |405 | 396. 8 |388. 33 |8. 22 |16. 67 | | | | |MAD = | |16. 11| | |19. 17| | (d)Note that Amit has more forecast observations, while Barbara’s moving average does not start until month 4. Also note that the MAD for Amit is an average of 4 numbers, while Barbara’s is only 2. Amit’s MAD for exponential smoothing (16. 1) is lower than that of Barbara’s moving average (19. 17). So his forecast seems to be better. 4. 48? (a) |Quarter |Contracts X |Sales Y |X2 |Y2 |XY | |1 |? 153 |? 8 |? 23,409 |? 64 |? 1,224 | |2 |? 172 |10 |? 29,584 |100 |? 1,720 | |3 |? 197 |15 |? 38,809 |225 |? 2,955 | |4 |? 178 |? 9 |? 31,684 |? 81 |? 1,602 | |5 |? 185 |12 |? 34,225 |144 |? 2,220 | |6 |? 199 |13 |? 39,601 |169 |? 2,587 | |7 |? 205 |12 |? 42,025 |144 |? ,460 | |8 |? 226 |16 |? 51,076 |256 |? 3,616 | |Totals | | 1,515 | | |95 | b = (18384 – 8 ( 189. 375 ( 11. 875)/(290,413 – 8 ( 189. 375 ( 189. 375) = 0. 1121 a = 11. 875 – 0. 1121 ( 189. 375 = –9. 3495 Sales ( y) = –9. 349 + 0. 1121 (Contracts) (b) [pic] 4. 49? (a) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||Error| |Error2 | | 1 |? 0. 25 |0. 25 |0. 00 |? 0. 00 | | 2 |? . 24 |0. 25 |0. 01 |? 0. 0001 | | 3 |? 0. 24 |0. 244 |0. 004 |? 0. 0000 | | 4 |? 0. 26 |0. 241 |0. 018 |? 0. 0003 | | 5 |? 0. 25 |0. 252 |0. 002 |? 0. 00 | | 6 |? 0. 30 |0. 251 |0. 048 |? 0. 0023 | | 7 |? 0. 31 |0. 280 |0. 029 |? 0. 0008 | | 8 |? 0. 32 |0. 298 |0. 021 |? 0. 0004 | | 9 |? 0. 24 |0. 311 |0. 071 |? 0. 0051 | |10 |? 0. 26 |0. 68 |0. 008 |? 0. 0000 | |11 |? 0. 25 |0. 263 |0. 013 |? 0. 0002 | |12 |? 0. 33 |0. 255 |0. 074 |? 0. 0055 | |13 |? 0. 50 |0. 300 |0. 199 |? 0. 0399 | |14 |? 0. 95 |0. 420 |0. 529 |? 0. 2808 | |15 |? 1. 70 |0. 738 |0. 961 |? 0. 925 | |16 |? 2. 30 |1. 315 |0. 984 |? 0. 9698 | |17 |? 2. 80 |1. 906 |0. 893 |? 0. 7990 | |18 |? 2. 80 |2. 442 |0. 357 |? 0. 278 | |19 |? 2. 70 |2. 656 |0. 043 |? 0. 0018 | |20 |? 3. 90 |2. 682 |1. 217 |? 1. 4816 | |21 |? 4. 90 |3. 413 |1. 486 |? 2. 2108 | |22 |? 5. 30 |4. 305 |0. 994 |? 0. 9895 | |23 |? 6. 20 |4. 90 |1. 297 |? 1. 6845 | |24 |? 4. 10 |5. 680 |1. 580 |? 2. 499 | |25 |? 4. 50 |4. 732 |0. 232 |? 0. 0540 | |26 |? 6. 10 |4. 592 |1. 507 |? 2. 2712 | |27 |? 7. 0 |5. 497 |2. 202 |? 4. 8524 | |28 |10. 10 |6. 818 |3. 281 |10. 7658 | |29 |15. 20 |8. 787 |6. 412 |41. 1195 | (Continued) 4. 49? (a)? (Continued) |Method ( Exponential Smoothing | | | |0. 6 = ( | | | |Year |Deposits (Y) |Forecast ||Error| |Error2 | |30 |? 18. 10 |12. 6350 | 5. 46498 |29. 8660 | |31 |? 24. 10 |15. 9140 |8. 19 |67. 01 | |32 |? 25. 0 |20. 8256 |4. 774 |22. 7949 | |33 |? 30. 30 |23. 69 | 6. 6097 6 |43. 69 | |34 |? 36. 00 |27. 6561 | 8. 34390 |69. 62 | |35 |? 31. 10 |32. 6624 | 1. 56244 | 2. 44121 | |36 |? 31. 70 |31. 72 | 0. 024975 | 0. 000624 | |37 |? 38. 50 |31. 71 |6. 79 |? 46. 1042 | |38 |? 47. 90 |35. 784 |12. 116 |146. 798 | |39 |? 49. 10 |43. 0536 |6. 046 |36. 56 | |40 |? 55. 80 |46. 814 | 9. 11856 | 83. 1481 | |41 |? 70. 10 |52. 1526 |17. 9474 |322. 11 | |42 |? 70. 90 |62. 9210 | 7. 97897 |63. 66 | |43 |? 79. 10 |67. 7084 |11. 3916 |129. 768 | |44 |? 94. 00 |74. 5434 | 19. 4566 | 378. 561 | |TOTALS | |787. 30 | | | |150. 3 | | |1,513. 22 | |AVERAGE | 17. 8932 | | 3. 416 | 34. 39 | | | | |(MAD) |(MSE) | |Next period forecast = 86. 2173 |Standard error = 6. 07519 | Method ( Linear Regression (Trend Analysis) | |Year |Period (X) |Deposits (Y) |Forecast |Error2 | |? 1 |? 1 |0. 25 |–17. 330 |309. 061 | |? 2 |? 2 |0. 24 |–15. 692 |253. 823 | |? 3 |? 3 |0. 24 |–14. 054 |204. 31 | |? 4 |? 4 |0. 26 |–12. 415 |160. 662 | |? 5 |? 5 |0. 25 |–1 0. 777 |121. 594 | |? 6 |? 6 |0. 30 |? –9. 1387 |89. 0883 | |? 7 |? 7 |0. 31 |? –7. 50 |61. 0019 | |? 8 |? 8 |0. 32 |? –5. 8621 |38. 2181 | |? |? 9 |0. 24 |? –4. 2238 |19. 9254 | |10 |10 |0. 26 |? –2. 5855 |8. 09681 | |11 |11 |0. 25 |? –0. 947 |1. 43328 | |12 |12 |0. 33 |? 0. 691098 |0. 130392 | |13 |13 |0. 50 |? 2. 329 |3. 34667 | |14 |14 |0. 95 |? 3. 96769 |9. 10642 | |15 |15 |1. 70 |? 5. 60598 |15. 2567 | |16 |16 |2. 30 |? 7. 24427 |24. 4458 | |17 |17 |2. 0 |? 8. 88257 |36. 9976 | |18 |18 |2. 80 |? 10. 52 |59. 6117 | |19 |19 |2. 70 |? 12. 1592 |89. 4756 | |20 |20 |3. 90 |? 13. 7974 |97. 9594 | |21 |21 |4. 90 |? 15. 4357 |111. 0 | |22 |22 |5. 30 |? 17. 0740 |138. 628 | |23 |23 |6. 20 |? 18. 7123 |156. 558 | |24 |24 |4. 10 |? 20. 35 |264. 083 | |25 |25 |4. 50 |? 21. 99 |305. 62 | |26 |26 |6. 10 |? 23. 6272 |307. 203 | |27 |27 |7. 70 |? 25. 2655 |308. 547 | |28 |28 |10. 10 |? 26. 9038 |282. 367 | |29 |29 |15. 20 |? 28. 5421 |178. 011 | |30 | 30 |18. 10 |? 30. 18 |145. 936 | |31 |31 |24. 10 |? 31. 8187 |59. 58 | |32 |32 |25. 60 |? 33. 46 |61. 73 | |33 |33 |30. 30 |? 35. 0953 |22. 9945 | |34 |34 |36. 0 |? 36. 7336 |0. 5381 | |35 |35 |31. 10 |? 38. 3718 |52. 8798 | |36 |36 |31. 70 |? 40. 01 |69. 0585 | |37 |37 |38. 50 |? 41. 6484 |9. 91266 | |38 |38 | 47. 90 |? 43. 2867 |21. 2823 | |39 | 39 |49. 10 |? 44. 9250 |17. 43 | |40 | 40 |55. 80 |? 46. 5633 |? ? 85. 3163 | |41 | 41 |70. 10 |? 48. 2016 |? 479. 54 | |42 | 42 |70. 90 |? 49. 84 |? 443. 28 | |43 | 43 |79. 10 |? 51. 4782 |? 762. 964 | |44 | 44 |94. 00 |? 53. 1165 | 1,671. 46 | |TOTALS | |990. 00 | | |787. 30 | | | | | | | | | | | | | |7,559. 95 | | |AVERAGE |22. 50 | 17. 893 | |171. 817 | | | | | |(MSE) | |Method ( Least squares–Simple Regression on GSP | | |a |b | | | | |–17. 636 |13. 936 | | | | |Coefficients: |GSP |Deposits | | | | |Year |(X) |(Y) |Forecast ||Error| |Error2 | |? 1 |0. 40 |? 0. 25 |–12. 198 |? 12. 4482 |? 154. 957 | |? 2 |0. 40 |? 0. 24 |–12. 198 |? 12. 4382 |? 154. 71 | |? 3 |0. 50 |? 0. 24 |–10. 839 |? 11. 0788 |? 122. 740 | |? 4 |0. 70 |? 0. 26 |–8. 12 | 8. 38 | 70. 226 | |? 5 |0. 90 |? 0. 25 |–5. 4014 | 5. 65137 | 31. 94 | |? 6 |1. 00 |? 0. 30 |–4. 0420 | 4. 342 | 18. 8530 | |? 7 |1. 40 |? 0. 31 |? 1. 39545 | 1. 08545 | 1. 17820 | |? 8 |1. 70 |? 0. 32 |? 5. 47354 | 5. 5354 | 26. 56 | |? 9 |1. 30 |? 0. 24 |? 0. 036086 | 0. 203914 | 0. 041581 | |10 |1. 20 |? 0. 26 |–1. 3233 | 1. 58328 | 2. 50676 | |11 |1. 10 |? 0. 25 |–2. 6826 | 2. 93264 | 8. 60038 | |12 |0. 90 |? 0. 33 |–5. 4014 | 5. 73137 | 32. 8486 | |13 |1. 20 |? 0. 50 |–1. 3233 | 1. 82328 | 3. 32434 | |14 |1. 20 |? 0. 95 |–1. 3233 | 2. 27328 | 5. 16779 | |15 |1. 20 |? 1. 70 |–1. 3233 | 3. 02328 | 9. 14020 | |16 |1. 60 |? 2. 30 |? 4. 11418 | 1. 81418 | 3. 9124 | |17 |1. 50 |? 2. 80 |? 2. 75481 | 0. 045186 | 0. 002042 | |18 |1. 60 |? 2. 80 |? 4. 11418 | 1. 31418 | 1. 727 | |19 | 1. 70 |? 2. 70 |? 5. 47354 | 2. 77354 | 7. 69253 | |20 |1. 90 |? 3. 90 |? 8. 19227 | 4. 29227 | 18. 4236 | |21 |1. 90 |? 4. 90 |? 8. 19227 | 3. 29227 | 10. 8390 | |22 |2. 30 |? 5. 30 |13. 6297 | 8. 32972 | 69. 3843 | |23 |2. 50 |? 6. 20 |16. 3484 |? 10. 1484 |? 102. 991 | |24 |2. 80 |? 4. 10 |20. 4265 |? 16. 3265 |? 266. 56 | |25 |2. 90 |? 4. 50 |21. 79 |? 17. 29 |? 298. 80 | |26 |3. 40 |? 6. 10 |28. 5827 |? 22. 4827 |? 505. 473 | |27 |3. 80 |? 7. 70 |34. 02 |? 26. 32 |? 692. 752 | |28 |4. 10 |10. 10 |38. 0983 |? 27. 9983 |? 783. 90 | |29 |4. 00 |15. 20 |36. 74 |? 21. 54 |? 463. 924 | |30 |4. 00 |18. 10 |36. 74 |? 18. 64 |? 347. 41 | |31 |3. 90 |24. 10 |35. 3795 |? 11. 2795 |? 127. 228 | |32 |3. 80 |25. 60 |34. 02 | 8. 42018 | 70. 8994 | |33 |3. 0 |30. 30 |34. 02 | 3. 72018 | 13. 8397 | |34 |3. 70 |36. 00 |32. 66 | 3. 33918 | 11. 15 | |35 |4. 10 |31. 10 |38. 0983 | 6. 99827 | 48. 9757 | |36 |4. 10 |31. 70 |38. 0983 | 6. 39827 |? 40. 9378 | |37 |4. 00 |38. 50 |36. 74 | 1. 76 | 3. 101 46 | |38 |4. 50 |47. 90 |43. 5357 | 4. 36428 | 19. 05 | |39 |4. 60 |49. 10 |44. 8951 | 4. 20491 | 17. 6813 | |40 |4. 50 |55. 80 |43. 5357 |? 12. 2643 |? 150. 412 | |41 |4. 60 |70. 10 |44. 951 |? 25. 20 |? 635. 288 | |42 |4. 60 |70. 90 |44. 8951 |? 26. 00 |? 676. 256 | |43 |4. 70 |79. 10 |46. 2544 |? 32. 8456 |1,078. 83 | |44 |5. 00 |94. 00 |50. 3325 |? 43. 6675 |1,906. 85 | |TOTALS | | | |451. 223 |9,016. 45 | |AVERAGE | | | |? 10. 2551 |? 204. 92 | | | | | |? (MAD) |? (MSE) | Given that one wishes to develop a five-year forecast, trend analysis is the appropriate choice. Measures of error and goodness-of-fit are really irrelevant. Exponential smoothing provides a forecast only of deposits for the next year—and thus does not address the five-year forecast problem. In order to use the regression model based upon GSP, one must first develop a model to forecast GSP, and then use the forecast of GSP in the model to forecast deposits. This requires the development of two models—one of which (the model for GSP) must be based solely on time as the independent variable (time is the only other variable we are given). (b)? One could make a case for exclusion of the older data. Were we to exclude data from roughly the first 25 years, the forecasts for the later year How to cite Om Heizer Om10 Ism 04, Essay examples

Alcoholism Among Teenagers Essay Sample free essay sample

This survey is focus on alcohol addiction among adolescents. This alcohol addiction phenomenon is something that is going an progressively big concern to some parents. Alternatively of worrying about coffin nails. or drug dependence. they fear their kids to going addicted in intoxicant. Adolescents can easy purchase intoxicants by adding their excess pocket money. and by that manner they can imbibe all along. Statisticss shows that 62 % of all 15 to 18 twelvemonth old adolescents are imbibing intoxicant and the worst is most of them are misss. The adolescents become addicted in intoxicant because it is more advantages for them to less their depressed. if their alone. debatable or sometimes it’s merely a trip. It can do them loosen up and active. they say. but how it can be? Alcohols are non solution for that. To forestall adolescents for being addicted in intoxicant is non easy but there are many ways to forestall them in imbibing intoxicant. like recreational activities like fall ining in a athletics nines that they will certainly bask and that will deflect them from intoxicant. and the benefits of this is they can get down a new life without believing about intoxicant or other job that can deflect their heads. This survey does non cover the subject about inordinate poisoning that can do harmful things like sex. public violences and drug usage which can do many more Torahs against it and hurts. Related LITERATURE AND STUDYHow can you state if person has a imbibing job?Signs of a imbibing job include behaviors like imbibing for the intent of acquiring rummy ; imbibing entirely or maintaining it secret ; imbibing to get away jobs ; concealing intoxicant in uneven topographic points ; acquiring irritated when you are unable to obtain intoxicant to imbibe ; and holding jobs at work. school. place. or lawfully as a consequence of your imbibing. Other warning marks of intoxicant maltreatment include losing involvement in activities you used to bask. holding blackouts because of heavy imbibing. and acquiring annoyed when loved 1s say you may hold a imbibing job. Behaviors that may bespeak that a individual is enduring from intoxicant dependance include being able to imbibe more and more intoxicant. problem halting one time you start imbibing. powerful impulses to imbibe. and holding backdown symptoms like jitteriness. sickness. shaking. or holding cold workout suits when you donâ €™t hold a drink. Can an alcoholic merely cut back or halt imbibing? While some people with alcohol dependance can cut back or halt imbibing without aid. most are merely able to make so temporarily unless they get intervention. Persons who consume intoxicant in lower sums and tend to get by with jobs more straight are more likely to be successful in their attempts to cut back or halt imbibing without the benefit of intervention. Is at that place a safe degree of imbibing? Recent research describes possible wellness benefits of devouring intoxicant. including reduced hazard of bosom disease. shot. and dementedness. Given that. it is just to state that low consumption. along the lines of 4-8 ounces of wine per twenty-four hours. is likely safe. Statistics Teen Alcohol Abuse Research by the National Institute on Alcohol Abuse and Alcoholism Research surveies demonstrate that adolescent intoxicant maltreatment starts at a really early age. More exactly. the mean age when adolescents foremost seek intoxicant is 13 old ages old for misss and 11 old ages old for male child. The mean age at which Americans begin imbibing on a regular basis. harmonizing to these surveies is 15. 9 old ages old. Harmonizing to a research survey undertaken by the National Institute on Alcohol Abuse and Alcoholism ( NIAAA ) . teens who begin imbibing before the age of 15 are four times more likely to endure from unsafe intoxicants side effects like dependence on intoxicant than those who begin imbibing at 21 old ages of age or older. In fact. harmonizing to Joseph A. Califano. Chairman and President of The National Center on Addiction and Substance Abuse at Columbia University. â€Å"a kid who reaches age 21 without smoking. mistreating intoxicant or utilizing drugs is virtually certain neer to make so. † Are the above underage imbibing and teenage intoxicant abuse statistics of import? See this: in 1998. United States intoxicant dependence and intoxicant maltreatment research workers embarked upon a survey to determine the entire cost associated with to the negative effects of minor imbibing such as teenage intoxicant maltreatment and teenage alcohol addiction. The cost was more than $ 58 billion per twelvemonth! ALCOHOL STATISTICS IN THE PHILIPPINES MANILA. Philippines – The Geneva-based World Health Organization. in its study released early this month. warned of inordinate intoxicant usage. stating its toll in footings of homo lives has become progressively dismaying. Some 2. 5 million people die yearly due to harmful intoxicant usage. the study said. The planetary position study on intoxicant and wellness is based on figures gathered in 134 states worldwide on the impact of inordinate and risky usage of intoxicant. Alarmingly. in Southeast Asia. Filipinos were found to be the 2nd highest consumers of intoxicant. 2nd merely to the Indonesians. In the survey. beer appears to be the favorite drink of Filipinos due. chiefly to its affordability compared to difficult drinks. Based on the latest available information. nevertheless. Filipinos are seen as the No. 1 wine drinkers in the whole of the Asiatic part. The prevailing rate of intoxicant ingestion is estimated at 5 million Filipino drinkers in a population of 90 million . Some of the harmful effects attributed to excessive intoxicant usage include being the direct cause of about 4 per centum of all deceases worldwide – more deceases than those caused by HIV-AIDS and TB and. globally. 6. 2 per centum of deceases of immature work forces. compared to 1. 1 per centum of female deceases. Besides. globally. 320. 000 immature people aged 15-29 old ages old die yearly from alcohol-related causes. ensuing in 9 per centum of all deceases in such age group. The World Health Organization considers alcohol as an evidentiary subscriber to the load of disease in most states. although it says its impact is non the same in all states in the parts. Harmonizing to the study. the highest ingestion rates are found in largely developed states such as in Western and Eastern Europe. although non the highest rate of bad imbibing. In the Americas. intoxicant ingestion is higher than the planetary norm. and the typical ingestion form is undependable. Harmonizing to the study. abstinence rates are high in North African and South Asiatic states with big. typically non-drinking populations while pronounced additions in intoxicant ingestion were found in South Africa and Southeast Asia during the clip the study was conducted. The Global Status Report on Alcohol and Health is the footing of the recommendation for states to implement steps that have shown to cut down alcohol-related injury. including raising intoxicant revenue enhancements. curtailing gross revenues to cut down handiness. raising the age bound for buying intoxicant. and go throughing and implementing effectual anti-drunk-driving Torahs. In the interim. on top of all that is drastic reform in the advertizements of alcoholic drinks. Ads present a direct nexus between intoxicant and felicity. sexual conquering. success and exhilaration: Alcohol drinkers are frequently portrayed as heroic. attractive. athletic. and good. macho. PREVENTIVE MEASUREPreventive MeasuresThe stating ‘an ounce of bar is worth a lb of cure’ is applicable in this state of affairs. With earlier bar and information. many striplings will non be involved in imbibing at an early age. With this. parents and defenders of the striplings must take duty over them to inform them of the effects of being an alky. It has been reported that one manner to forestall adolescent imbibing is to remain involved and interested in the teenager’s life ( ‘Alcoholism’ 2004 ) . In this manner. the parents know the activities of their kids. including their equals and their agenda in school. Another manner is to speak openly to your kids. particularly to pre-teens and teens. about the widespread presence and dangers of intoxicant and drugs ( ‘Alcoholism’ 2004 ) . This will function as a warning for them and information to forestall the incidence of alcohol addiction. Act as a function theoretical account and do non imbibe overly or utilize other drugs or fume ( ‘Alcoholism’ 2004 ) . The striplings will recognize that their parents are good illustration of moral values. and non indulge themselves to going alkies. With these. the parents can interact decently with their kids and someway supervise their behavior. The cardinal construct in this issue is communicating. With good and changeless communicating. parents and kids can hold a good and permanent relationship. This can assist both parties to educate one another sing life and go involved with the activities of one another. Good communicating can assist each other create a good environment for one another. Chapter 2 Method of Study* Survey research* Surfboarding the net Instruments and Techniques* Questionnaires Name: Gender: Age: Interview sheet: 1. Make you imbibe alcohol? Yes______ No________ 2. If yes. do you experience you are a normal drinker? Yes______ No________ 3. What do you believe the jobs or factors that affect teens like you to take excessively much intoxicant? Family______ Friends______ Study______ Other answer______ ______ 4. As a adolescent. what do you believe is the most affected when you’re imbibing intoxicant? Study______ Health______ Family relationship______ 5. Are you cognizant of the possible bad effects of imbibing excessively much intoxicant? Yes______ No______ 6. Are you willing to halt imbibing intoxicant? Yes______ No______ ** THANK YOU **| Treatment of Datas Consequence of the Survey Respondents| 1| 2| 3| 4| 5| 6|Female – 63| Yes= 33| Yes= 51| A= 23| A= 8| Yes= 59| Yes= 61| | No=30| No= 12| B= 20| B= 45| No= 4| No= 2| | | | | | | || | | C= 15| C= 10| | || | | | | | || | | D= 5| | | || Yes= 29| Yes= 22| A= 12| A= 29| Yes= 36| Yes= 36| | | | | | | |Male- 37| No= 8| No= 15| B= 13| B= 6| No= 1| No= 1| | | | | | | || | | C= 9| C= 2| | || | | | | | || | | D= 3| | | || | | | | | |Sum: 100 Teenagers| T = 100| T = 100| T = 100| T = 100| T = 100| T = 100| This tabular array shows that 62 % of the adolescents are imbibing intoxicant and 27 % of it is considered as addicted to it. But there are 95 % are still cognizant of possible bad effects of intoxicant to their lives and 97 % of the adolescents are willing to halt imbibing intoxicant. The chief factor that influenced adolescents is household with 35 % . 33 % of the adolescents are influenced by their friends. 24 % are influenced by their survey. And 8 % have their ain ground why they are being addicted in imbibing intoxicant. And this tabular array besides shows the things that are affected when adolescents drink excessively much intoxicant. Based on the graph. the most affected is our wellness with 51 % . Adolescents are cognizant that intoxicant is really unsafe to our wellness. While 37 % forfeits their survey and 12 % forfeits their household relationship because of imbibing intoxicant excessively much. Factors that affect Teenagers to be addicted in imbibing Alcohol The saloon graph presents the factors that influenced adolescents to imbibe intoxicant. It shows that the chief factor that influenced adolescents is household with 35 % . 33 % of the adolescents are influenced by their friends. 24 % are influenced by their survey. And 8 % have their ain ground why they are being addicted in imbibing intoxicant. Thingss that affect when Teenagers imbibe excessively much Alcohol The pie graph shows the things that are affected when adolescents drink excessively much intoxicant. Based on the graph. the most affected is our wellness with 51 % . Adolescents are cognizant that intoxicant is really unsafe to our wellness. While 37 % forfeits their survey and 12 % forfeits their household relationship because of imbibing intoxicant excessively much. Chapter 3 Presentation and reading Alcoholism Number of Teenagers | figure of adolescents who are imbibing intoxicant | adolescents who are addicted in alcohol| adolescents who are cognizant to the bad consequence of imbibing excessively much of intoxicant | adolescents who are willing to halt imbibing intoxicant | Female ( 63 ) | 33| 12| 59| 61| Male ( 37 ) | 29| 15| 36| 36|T OTAL = 100 Teenagers| 62 % | 27 % | 95 % | 97 % | This tabular array shows that 62 % of the adolescents are imbibing intoxicant and 27 % of it is considered as addicted to it. But there are 95 % are still cognizant of possible bad effects of intoxicant to their lives and 97 % of the adolescents are willing to halt imbibing intoxicant. Chapter 4 Summary of FindingssAlcohol is one of the increasing job of our state. specially it is become a job of some parents with their boy / girl who are addicted in intoxicant. They can purchase intoxicant in easy manner. by adding their excess money. in by that manner they can purchase intoxicants and can imbibe all along. Co adolescents are one who influenced them to imbibe intoxicant. Sometimes adolescents are imbibing intoxicant by nonsensical explination. like holding a bonding with friends or schoolmates. holding a job. down and etc. Most of the adolescents. 95 % are cognizant what is the bad effects of intoxicant in their wellness. and 97 % of them are willing to halt imbibing intoxicant. they besides know what are the things they can give in imbibing intoxicant and that are their survey. wellness and household dealingss. And to forestall adolescents for being addicted in intoxicant is non easy but there are many ways to forestall them from it like a recreational activity like fall i ning in athleticss nines that they will bask and can pull out them from intoxicants. RecommendationFor a adolescent who is analyzing. this intoxicant can deflect our concentration in household relationship and the most is our concentration in survey. It can do a more attempts or attending in Drinking intoxicant that can do a distraction to their wellness and surveies. and it can be happened if they continue imbibing excessively much intoxicant which can take them to a disease and forfeits in their hereafter. Few would differ that alcohol addiction is dearly-won for those identified as alkies and for those close to them. every bit good as for the civilization that has identified alcohol addiction as a societal job. Even fewer. nevertheless. seem to hold on the best manner of reacting to it. Evidence that intoxicant dependance is destructive. is balanced by obliging grounds that the methods brought to bear on get the better ofing such dependance are so variable and fraught with unforeseen effects. whether in the clinic or the courtroom. that sensible people might be fo rgiven for proposing that alcohol addiction is a job that can non be solved. What is required foremost of all if the job of alcohol addiction is to be efficaciously solved is clear believing about it. Decision For a adolescent who is analyzing in school. this destroys any concentration that they may necessitate to larn. Changeless texts mean that attending can no longer be devoted to the undertaking at manus. and the job stretches outside school every bit good. and it can be seen many provinces that have passed Torahs against utilizing cell phones while driving. which causes many more hurts than you would believe. For most texting nuts. the hook comes with a cost and it’s non merely on their phone measure. Teenage encephalons are still developing and they haven’t to the full developed the country that processes effects and regulates urges. which can be a job particularly while driving and texting. There are several other upsets that are caused by insistent texting. Here are some studies of turning â€Å"monster thumbs† because of inordinate texting. Insistent Thumb Syndrome. depression. low ego regard and anxiousness are some of the other sick effects of text dependenc e. Surprisingly. there are people who believe that there is no such thing as text dependence. Did anyone cognize that cocaine or marihuana were habit-forming until people started acquiring addicted to them? Similarly text dependence is in the initial phases now. The Oklahoman we start discontinuing this dependence the better. Bibliography [ 01 ] Alcohol Maltreatment: Drinking Facts.hypertext transfer protocol: //www. cwru. edu/orgs/peerhelp/AlcoholAbuse_info. hypertext markup language[ 02 ] Alcohol and mental wellness. InfoScotland. com.hypertext transfer protocol: //www. infoscotland. com/alcohol/displaypage. jsp[ 03 ] Drug Rehab Information. Alcoholism and Alcohol Treatment. 2007 hypertext transfer protocol: //www. info-drug-rehab. com/alcohol. hypertext markup language[ 04 ] Michael bolyn. Factore influence adolescents alcohol abuse hypertext transfer protocol: //www. livestrong. com/article/73128-factors-influence-teenagers-alcohol-abuse/ # ixzz2CCcZUSdD [ 05 ] Roxanne Dryden-Edwards. Alcohol and Teens. MedicineNet. hypertext transfer protocol: //www. medicinenet. com/alcohol_and_teens/article. htm # tocb [ 05 ] Scientific consensus â€Å"Global Status Report on Alcohol 2004† . Green facts hypertext transfer protocol: //www. greenfacts. org/en/alcohol/index. htm # 6 [ 06 ] WebMD. LLC. September 13. 2006hypertext transfer protocol: //www. webmd. com/mental-health/alcohol-abuse/teen-alcohol-and-drug-abuse-topic-overview