The document discusses various forecasting techniques including exponential smoothing, linear regression, and simulation. It provides examples of how to calculate forecasts using simple and double exponential smoothing as well as linear, parabolic, and multiple regression models. It also presents a simulation example to compare the costs of different maintenance policies for vacuum tubes in a machine.
Text Book: An Introduction to Mechanics by Kleppner and Kolenkow
Chapter 1: Vectors and Kinematics
-Explain the concept of vectors.
-Explain the concepts of position, velocity and acceleration for different kinds of motion.
References:
Halliday, Resnick and Walker
Berkley Physics Volume-1
This lesson begin with explaining the simple exponential smoothing method characteristics, and uses. Simple exponential smoothing method attempts to best fit a smoothing constant to past data. Using an example and the forecasting process, we apply the simple exponential smoothing method to create a model and forecast based upon it.
Text Book: An Introduction to Mechanics by Kleppner and Kolenkow
Chapter 1: Vectors and Kinematics
-Explain the concept of vectors.
-Explain the concepts of position, velocity and acceleration for different kinds of motion.
References:
Halliday, Resnick and Walker
Berkley Physics Volume-1
This lesson begin with explaining the simple exponential smoothing method characteristics, and uses. Simple exponential smoothing method attempts to best fit a smoothing constant to past data. Using an example and the forecasting process, we apply the simple exponential smoothing method to create a model and forecast based upon it.
A method that uses measurable, historical data observations, to make forecasts by calculating the weighted average of the current period’s actual value and forecast, with a trend adjustment added in
[Q1~12]Aclothingstoreisconsideringtwomethodstoreducetheselosses:1)tohireasecurityguardvs.2)toinstallcameras.Aftercollectingdatafor5-monthperiodeachrespectively,themonthlylosses(in100)wererecordedinthetable.Themanagerwouldinstallthecamerasonlyiftherewasenoughevidencethattheguardwasbetter.
̅ s2 n
Guard (x1)
27
20
32
23
38
Cameras (x2)
48
31
29
38
44
)
2
a) Compute the average, variance in the above table. (Show ( − ̅ calculation!)
b) Test whether you use equal variance or not.
(b-1) Setup hypotheses.
(b-2) F-stat =
(b-3) Fcrit region:
(b-4) Conclusion:
Assumetheequal-variancet-statisticfortheabovetwopopulations
c) T-test about μ1 – μ2.
(c-1) Setup hypotheses.
Conclusion must include
- Whether you can reject H0 or not - Explain in the problem context.
(c-2) Compute SD & d.f. d.f. =
1 1
1 2
+ ?
( )
��
SD= ? ?𝑛 𝑛 (c-3) t-stat = � = �
(c-4) rejection region:
(c-5) Conclusion:
Becausehiringtheguardismoreexpensive,themanagerrequiresthatthereducinglosses(in100)whenhiringtheguardmustbeatleast2
d) 2nd t-test
(d-1) Setup hypotheses. H1
)
��
(d-2) t-stat = � = �
(d-3) Conclusion: Since t-stat
1 2
(BA 2606 MID-2) 2
[Q13~19]Hopingtoimprovesales,onecompanydecidedtointroducemoreattractivepackaging.Totesttheeffectonsales,themanagerdistributesthenewdesigntoSupermarket1(MKT1),whilesendingtheolddesigntoSupermarket2(MKT2).Thebarcodedatawerereceivedafteracertainperiod.Thecodeforthisproductwas9077inbothsupermarkets.Sincethecostfornewpackageismoreexpensive,themanagerwantstoknowtheeffectivenessofthisnewdesign.Thecollecteddataforthetotaltransactions(n)andthenumberof9077(x)isasfollows:
a) Set up the alternative hypothesis.
b) Fill up the table. (Use 3 decimal point calculation.) What is the pooled proportion for (p1 – p2)?
1 1
+ ?
c) Compute the standard error for (p1 – p2). ? ?𝑛 𝑛
d) What distribution does (p1 – p2) follow?
MKT1 MKT2 Total n 904 1038
x 180 155 p
)
Why not t-distribution? .
𝒑
−𝒑
?
𝟏 𝟏
e) Compute z-statistic: ( ? 𝑺𝑫
f) What is zcrit at α = 0.05? Explain where you get the number. P(Z
g) Conclusion: Since
Becausethenewdesignismoreexpensive,themanagementrequiresthenewdesignoutsellstheoldonebyatleast2%.Inthisassumption,pleaseanswerforthefollowingquestions.
? ?
1 1
𝑛
?
h) Compute the standard error for (p1 – p2). ? ?𝑝(1−𝑝) 1
?
?
)
� − 𝑝
𝟏 𝟏 2
𝑺𝑫
i) Compute z-statistic: ( �−𝒑 ( 1−𝑝)
Speeds(km/h)
20
30
40
50
60
ABS(x1)
36
48
60
67
70
non-ABS(x2)
34
51
64
69
73
d = x1 – x2
[Q22~27]TofindtheeffectivenessofABS,acarbuyerorganizedanexperiment.HehitthebrakesatsomespeedandrecordedthetimetostopanABS-equippedcarandanotheridenticalcarwithoutABS.Thespeedsandthetime(in0.1seconds)tostopondrypavementarelistedhere.CanweinferthatABSisbetter(thatis,thestoppingtimeisshorter)with95%confidence?
a) What kind of comparison is this question about?
b) Set up the alternative hypothesis, using d = x1 – x2.
c) Compute d in the table and the average and standard deviation
MATHEMATICAL MODELING OF COMPLEX REDUNDANT SYSTEM UNDER HEAD-OF-LINE REPAIREditor IJMTER
Suppose a composite system consisting of two subsystems designated as ‘P’ and
‘Q’ connected in series. Subsystem ‘P’ consists of N non-identical units in series, while the
subsystem ‘Q’ consists of three identical components in parallel redundancy.
ATT00001
ATT00002
ATT00003
ATT00004
ATT00005
CARD.DTA
Card_1995_geo_var_schooling.pdf
Exam2_2014.pdf
ADVANCED ECONOMETRICS
Midterm 2 (Take Home)
Due: Dec.25, 2014
Answer all questions. You should not discuss solutions with your peers but me. Good luck!
Prof. Dr. H. Taştan ,
First Name:...................................................
Last Name:................................................
No:...................................................
1 (20) In class we have shown that when the number of instrumental variables is larger than the number
of endogenous variables the generalized IV estimator (or 2SLS) can be written as
β̂IV =
(
X>PzX
)−1
X>Pzy
where Pz = Z(Z
>Z)−1Z>. In this formulation X is n×k and Z is n× l, l > k.
(a) Show that β̂IV can be obtained as a solution to the following minimization problem
min
β
Q(β) = (y−Xβ)>Pz (y−Xβ)
(b) Show that when k = l the generalized IV estimator reduces to the simple IV estimator:
β̂IV =
(
Z>X
)−1
Z>y
2 (20) Consider the following simple consumption model as a function of permanent income
ci = β1 + β2y
∗
i + ui, ui ∼ iid (0,σ
2
u)
where ci is the logarithm of consumption by household i, and y
∗
i is the permanent income of household
i which is not observed. Instead we observe current income, yi
yi = y
∗
i + vi, vi ∼ iid (0,σ
2
v)
where vi is assumed to be uncorrelated with y
∗
i and ηi. We run the following regression
ci = β1 + β2yi + ηi
(a) Show that yi is negatively correlated with ηi. You can assume β2 > 0.
(b) Evaluate the plim of the OLS estimator β̂2:
β̂2 =
∑n
i=1(yi − ȳ)ci∑n
i=1(yi − ȳ)2
In particular, show that this plim is less than the true β2.
1
3 (30) Use card.dta to answer the following questions. Also read Card (1993), “Using Geographic
Variation in College Proximity to Estimate the Return to Schooling”, NBER Working Paper.
(a) Run the OLS regression of log(wage) on educ, exper, exper2, black, smsa, south, smsa66, reg662
to reg669. Comment on the coefficient estimate of educ.
(b) Estimate the same model by 2SLS using nearc4 as an instrument for educ. Compare the OLS
and IV coefficient estimates on educ. (Note that we partly did this in class). Carry out the
Hausman test.
(c) Use both nearc2 and nearc4 as instruments for educ. Run the reduced form model for educ.
Compare 2SLS estimates to the results obtained in the previous section. Carry out the OID
test.
(d) Discuss the plausibility of Card (1993)’s econometric methodology and empirical findings. Do
you agree with his conclusions?
4 (30) A continuous time model for short term interest rates may be written as a stochastic differential
equation
dr = (α + βr)dt + σrγ�
√
dt
where r is the short term interest rate, � is standard normal random variable, dt is a short time
interval and α,β,γ,σ are parameters. Discrete time approximation is given as
rt+1 − rt = α + βrt + �t+1
with
E(�t+1) = 0, E(�
2
t+1) = σ
2 ...
Economics
Curve Fitting
macroeconomics
Curve fitting helps in capturing the trend in the data by assigning a single function
across the entire range.
If the functional relationship between the two quantities being graphed is known to be
within additive or multiplicative constants, it is common practice to transform the data in
such a way that the resulting line is a straight line.(by plotting) A process of quantitatively
estimating the trend of the outcomes, also known as regression or curve fitting, therefore
becomes necessary.
For a series of data, curve fitting is used to find the best fit curve. The produced equation is
used to find points anywhere along the curve. It also uses interpolation (exact fit to the data)
and smoothing.
Some people also refer it as regression analysis instead of curve fitting. The curve fitting
process fits equations of approximating curves to the raw field data. Nevertheless, for a
given set of data, the fitting curves of a given type are generally NOT unique.
Smoothing of the curve eliminates components like seasonal, cyclical and random
variations. Thus, a curve with a minimal deviation from all data points is desired. This
best-fitting curve can be obtained by the method of least squares.
What is curve fitting Curve fitting?
Curve fitting is the process of constructing a curve, or mathematical functions, which possess closest
proximity to the series of data. By the curve fitting we can mathematically construct the functional
relationship between the observed fact and parameter values, etc. It is highly effective in mathematical
modelling some natural processes.
What is a fitting model?
A fit model (sometimes fitting model) is a person who is used by a fashion designer or
clothing manufacturer to check the fit, drape and visual appearance of a design on a
'real' human being, effectively acting as a live mannequin.
What is a model fit statistics?
The goodness of fit of a statistical model describes how well it fits a set of
observations. Measures of goodness of fit typically summarize the discrepancy
between observed values and the values expected under the model in question.
What is a commercial model?
Commercial modeling is a more generalized type of modeling. There are high
fashion models, and then there are commercial models. ... They can model for
television, commercials, websites, magazines, newspapers, billboards and any other
type of advertisement. Most people who tell you they are models are “commercial”
models.
What is the exponential growth curve?
Growth of a system in which the amount being added to the system is proportional to the
amount already present: the bigger the system is, the greater the increase. ( See geometric
progression.) Note : In everyday speech, exponential growth means runaway expansion, such
as in population growth.
Why is population exponential?
Exponential population growth: When resources are unlimited, populations
exhibit exponential growth, resulting in a J-shaped curve.
Identification of the Mathematical Models of Complex Relaxation Processes in ...Vladimir Bakhrushin
The approach to solving the problem of complex relaxation spectra is presented.
Presentation for the XI International Conference on Defect interaction and anelastic phenomena in solids. Tula, 2007.
An Exponential Observer Design for a Class of Chaotic Systems with Exponentia...ijtsrd
In this paper, a class of generalized chaotic systems with exponential nonlinearity is studied and the state observation problem of such systems is explored. Using differential inequality with time domain analysis, a practical state observer for such generalized chaotic systems is constructed to ensure the global exponential stability of the resulting error system. Besides, the guaranteed exponential decay rate can be correctly estimated. Finally, several numerical simulations are given to demonstrate the validity, effectiveness, and correctness of the obtained result. Yeong-Jeu Sun "An Exponential Observer Design for a Class of Chaotic Systems with Exponential Nonlinearity" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-1 , December 2020, URL: https://www.ijtsrd.com/papers/ijtsrd38233.pdf Paper URL : https://www.ijtsrd.com/engineering/electrical-engineering/38233/an-exponential-observer-design-for-a-class-of-chaotic-systems-with-exponential-nonlinearity/yeongjeu-sun
Continuous models of economy adopted to either define evolutions of economic
systems or investigate economic dynamics, amongst its other areas of applications, are
known to be related to differential equations. The considered model in this article takes
the form of a second order non-linear ordinary differential equation (ODE) which is
conventional solved by reducing to the system of first order. This approach is
computationally tasking, unlike block methods which bypasses reduction by directly
solving the model. A new approach is introduced named Modified Taylor Series
Approach (MTSA). Hence, the resultant MTSA-derived block method is implemented to
solve the second order non-linear model of economy under consideration.
[Note: This is a partial preview. To download this presentation, visit:
https://www.oeconsulting.com.sg/training-presentations]
Sustainability has become an increasingly critical topic as the world recognizes the need to protect our planet and its resources for future generations. Sustainability means meeting our current needs without compromising the ability of future generations to meet theirs. It involves long-term planning and consideration of the consequences of our actions. The goal is to create strategies that ensure the long-term viability of People, Planet, and Profit.
Leading companies such as Nike, Toyota, and Siemens are prioritizing sustainable innovation in their business models, setting an example for others to follow. In this Sustainability training presentation, you will learn key concepts, principles, and practices of sustainability applicable across industries. This training aims to create awareness and educate employees, senior executives, consultants, and other key stakeholders, including investors, policymakers, and supply chain partners, on the importance and implementation of sustainability.
LEARNING OBJECTIVES
1. Develop a comprehensive understanding of the fundamental principles and concepts that form the foundation of sustainability within corporate environments.
2. Explore the sustainability implementation model, focusing on effective measures and reporting strategies to track and communicate sustainability efforts.
3. Identify and define best practices and critical success factors essential for achieving sustainability goals within organizations.
CONTENTS
1. Introduction and Key Concepts of Sustainability
2. Principles and Practices of Sustainability
3. Measures and Reporting in Sustainability
4. Sustainability Implementation & Best Practices
To download the complete presentation, visit: https://www.oeconsulting.com.sg/training-presentations
RMD24 | Retail media: hoe zet je dit in als je geen AH of Unilever bent? Heid...BBPMedia1
Grote partijen zijn al een tijdje onderweg met retail media. Ondertussen worden in dit domein ook de kansen zichtbaar voor andere spelers in de markt. Maar met die kansen ontstaan ook vragen: Zelf retail media worden of erop adverteren? In welke fase van de funnel past het en hoe integreer je het in een mediaplan? Wat is nu precies het verschil met marketplaces en Programmatic ads? In dit half uur beslechten we de dilemma's en krijg je antwoorden op wanneer het voor jou tijd is om de volgende stap te zetten.
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A personal brand exploration presentation summarizes an individual's unique qualities and goals, covering strengths, values, passions, and target audience. It helps individuals understand what makes them stand out, their desired image, and how they aim to achieve it.
Memorandum Of Association Constitution of Company.pptseri bangash
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A Memorandum of Association (MOA) is a legal document that outlines the fundamental principles and objectives upon which a company operates. It serves as the company's charter or constitution and defines the scope of its activities. Here's a detailed note on the MOA:
Contents of Memorandum of Association:
Name Clause: This clause states the name of the company, which should end with words like "Limited" or "Ltd." for a public limited company and "Private Limited" or "Pvt. Ltd." for a private limited company.
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Registered Office Clause: It specifies the location where the company's registered office is situated. This office is where all official communications and notices are sent.
Objective Clause: This clause delineates the main objectives for which the company is formed. It's important to define these objectives clearly, as the company cannot undertake activities beyond those mentioned in this clause.
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Liability Clause: It outlines the extent of liability of the company's members. In the case of companies limited by shares, the liability of members is limited to the amount unpaid on their shares. For companies limited by guarantee, members' liability is limited to the amount they undertake to contribute if the company is wound up.
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Capital Clause: This clause specifies the authorized capital of the company, i.e., the maximum amount of share capital the company is authorized to issue. It also mentions the division of this capital into shares and their respective nominal value.
Association Clause: It simply states that the subscribers wish to form a company and agree to become members of it, in accordance with the terms of the MOA.
Importance of Memorandum of Association:
Legal Requirement: The MOA is a legal requirement for the formation of a company. It must be filed with the Registrar of Companies during the incorporation process.
Constitutional Document: It serves as the company's constitutional document, defining its scope, powers, and limitations.
Protection of Members: It protects the interests of the company's members by clearly defining the objectives and limiting their liability.
External Communication: It provides clarity to external parties, such as investors, creditors, and regulatory authorities, regarding the company's objectives and powers.
https://seribangash.com/difference-public-and-private-company-law/
Binding Authority: The company and its members are bound by the provisions of the MOA. Any action taken beyond its scope may be considered ultra vires (beyond the powers) of the company and therefore void.
Amendment of MOA:
While the MOA lays down the company's fundamental principles, it is not entirely immutable. It can be amended, but only under specific circumstances and in compliance with legal procedures. Amendments typically require shareholder
"𝑩𝑬𝑮𝑼𝑵 𝑾𝑰𝑻𝑯 𝑻𝑱 𝑰𝑺 𝑯𝑨𝑳𝑭 𝑫𝑶𝑵𝑬"
𝐓𝐉 𝐂𝐨𝐦𝐬 (𝐓𝐉 𝐂𝐨𝐦𝐦𝐮𝐧𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬) is a professional event agency that includes experts in the event-organizing market in Vietnam, Korea, and ASEAN countries. We provide unlimited types of events from Music concerts, Fan meetings, and Culture festivals to Corporate events, Internal company events, Golf tournaments, MICE events, and Exhibitions.
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"𝐄𝐯𝐞𝐫𝐲 𝐞𝐯𝐞𝐧𝐭 𝐢𝐬 𝐚 𝐬𝐭𝐨𝐫𝐲, 𝐚 𝐬𝐩𝐞𝐜𝐢𝐚𝐥 𝐣𝐨𝐮𝐫𝐧𝐞𝐲. 𝐖𝐞 𝐚𝐥𝐰𝐚𝐲𝐬 𝐛𝐞𝐥𝐢𝐞𝐯𝐞 𝐭𝐡𝐚𝐭 𝐬𝐡𝐨𝐫𝐭𝐥𝐲 𝐲𝐨𝐮 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐚 𝐩𝐚𝐫𝐭 𝐨𝐟 𝐨𝐮𝐫 𝐬𝐭𝐨𝐫𝐢𝐞𝐬."
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1. Simple Exponential Smoothing New forecast = Old forecast + (Actual – Old forecast) F t = F t-1 + (A t-1 – F t-1 ) Double Exponential Smoothing = S’ t + ( S’ t – S’’ t ) + b t S’ t = S’ t-1 + ’ (A t – S’ t-1 ) S’’ t = S’’ t-1 + ’’ (S’ t – S’’ t-1 ) b t = S’’ t – S’’ t-1 Where F t+1 = Single smoothed Single smoothed Double smoothed trend adjustment + + -
2. Linear trend Y t = + t n ty - t y = n t 2 - ( t) 2 y - t n n t t 2 1 1 1 2 3 5 3 6 14 4 10 30 5 15 55 6 21 91 7 28 140 8 36 204 9 45 285 10 55 385 11 66 506 12 78 650 13 91 819 14 105 1,015 15 120 1,240 16 136 1,496 17 153 1,785 18 171 2,109 19 190 2,470 20 210 2,870
3. Parabolic trend Y t = + t + t 2 with t=0 at the center of the data ty = t 2 y - t 2 n = n t 4 - ( t 2 ) 2 n t 2 y - t 2 y
4. Simple linear regression Y = a + bx a y - x n b = n ( x 2 ) - ( x) 2 n xy - ( x) ( y)
5. a Σ n + b Σ x 2 = Σ xy The transposed formulae : r 2 is the ratio Explained variation Total variation an + b Σ x = Σ y = a = Σ y – b Σ x n b = n Σ xy – Σ x Σ y n Σ x 2 - ( Σ x) 2 Σ ( YE – Y ) 2 Σ ( y - Y ) 2 where YE = estimate of Y given the regression equation for each value of x Y = mean off actual values of y y = individual actual value of y
6. r 2 = (n Σ xy - Σ x Σ y) 2 {n Σ x 2 - ( Σ x) 2 } x {n Σ y 2 - (y) 2 } Σ x 2 n Σ x 2 - ( Σ x) 2 S a = S e = S e S b = S e Σ x 2 - ( Σ x) 2 n An alternative formula for r 2 is : Standard error of regression = Standard errors of the intercept (a) and the gradient (b) The intercept The gradient Σ y 2 - a Σ y - b Σ xy n - 2
7. a ± t x S a b ± t x S b The confidence intervals for α and β may be established as follows : For the intercept For the gradient For the intercept For the gradient A test of significance for α and β The value of t is based upon n-2 degrees of freedom, and the chosen confidence level α β = = t = a - α S a t = b - β S b
8. Multiple Regression Y = a + b x 1 + b 2 x 2 x 1 y = a x 1 + b 1 x 1 2 + b 2 x 1 x 2 x 2 y = a x 2 + b 1 x 1 x 2 + b 2 x 2 2 a y + b 1 x 1 y + b 2 x 2 y - Y 2 - R 2 = y = an + b x 1 + b 2 x 2 ( y) 2 n ( y) 2 n
10. Assuming that the number of personnel does not change, a. What percentage of the volunteers will be working for each division in December 2007? b. Predict what percentage will be at the equilibrium state. An organization dependent upon volunteer help is divided into three divisions and allows free movement of personnel between any two. Between January and April 2007, personnel movement was as indicated in the following table. Exercise Gains Division Jan. From 1 From 2 From 3 April 1 30 27 3 4 34 2 60 2 57 4 63 3 40 1 0 32 33
11. A Production Planning Department is considering the production schedules for Period 9. In particular they wish to calculate the time to be allocated for the man- ufacture of a batch of 100 of a computer controlled machine tool called ROBO XI. The first ROBO XI took 80 hours to make and it is known from past experience that there is a learning effect. From past records the following information is avail- able. They calculate that the cumulative production at the beginning of Period 9 will be 3,000 units. Required : a. What type of learning curve model do the records suggest? b. What value of learning curve do the records show? c. Calculate the learning coefficient. d. Calculate the time allowance necessary for the batch of 100 in Period 9. ROBO XI Cumulative Cumulative Time per production (units) time taken (hours) unit (hours) 600 18,153.6 30.256 1,200 32,676 27.23