Roland Mba104 Pp

8/9/2019 Roland Mba104 Pp

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UNIVERSITY OF MINDANAO

ROLANDO A. DARAQUIT, JR.

MBA 104


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Making decisions is certainly the most important task of a

manager andit is often a verydifficult one. Thissite offers a

decision making procedure for solving complex problemsstep by

step.It presents the decision-analysis process for both public and

private decision-making, using different decision criteria, different

types ofinformation, andinformation of varying quality. It

describes the elementsin the analysis ofdecision alternatives and

choices, as well as the goals and objectives that guide decision-

making. The keyissues related to a decision-maker's preferencesregarding alternatives, criteria for choice, and choice modes,

together with the risk assessment tools are also presented.


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Topic

Regression:

Time Series Analysis:Smoothing Methods


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Regression Analysis

It includes any techniques for modeling

and analyzing several variables, when

the focusis on the relationshipbetween a dependent variable and

one or more independent variables.


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Continuation....

Regression analysis is widely used for

prediction (including forecasting of time-

series data). Regression analysis is alsoused to understand which among the

independent variables are related to the

dependent variable, and to explore theforms of these relationships.


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Time series analysis

It comprises methods for analyzing time series

data in order to extract meaningfulstatistics

and other characteristics of the data. Time

series forecasting is the use of a model to

forecast future events based onknown past

events: to predict data points before they are

measured. An example of time seriesforecasting in econometricsis predicting the

opening price of a stock based onits past

performance.


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INTRODUCING

*Identifying the

nature of the

phenomenonrepresented by the

sequence of

observations

* Forecasting

(predicting future

values of the timeseries variable


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Smoothing Method

It always involves some form of local averaging of data

such that the non-systematic components of individualobservations cancel each other out. The most common

technique is moving average smoothing which

replaces each element of the series by either the

simple or weighted average of n surroundingelements, where n is the width of the smoothing

"window".


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dynamic regression models

extrapolative,

decomposition,

spectral,

Box-Jenkins,

Time series analysis attempts to match the

observable patternsindata to an underlying

model or sequences of models.


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Time series analysis

Accounts for the fact that data

points taken over time may haveaninternalstructure (such as

autocorrelation, trend or

seasonal variation) that should

be accounted for.


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Exponential smoothing methods

These are typically

cheaper, easier to

use, and need less

data than the fifty or

more equally spaced

values over time

required by the Box-Jenkins techniques.

For these reasons,

smoothing methods are

often applied to

production, sales, andinventory control where

strong consideration is

given to keeping costs

down and profits up


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Smoothing Techniques:

*Simple Moving Averages:

* Weighted Moving Average

* Moving Averages with Trends

*Exponential smoothing.


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How to compare several smoothing methods:

Although there are numerical indicators for assessing

the accuracy of the forecasting technique, the most

widely approach is in using visual comparison of

several forecasts to assess their accuracy and chooseamong the various forecasting methods. In this

approach, one must plot (using, e.g., Excel) on the

same graph the original values of a time series

variable and the predicted values from severaldifferent forecasting methods, thus facilitating a

visual comparison.


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Roland Mba104 Pp