Stats Chapter 12

Front 1

incorporate the judgment of those participating

Back 1

qualitative approaches

Front 2

employ statistical techniques

Back 2

quantitative approaches

Front 3

depends on historical data

Back 3

time series forecasting

Front 4

Three Qualitative Approaches

Back 4

Delphi
Scenario Writing
Brinstorming

Front 5

Opinions are gathered from a panel of experts

Back 5

Delphi Approach

Front 6

In this approach, a moderator will develop a questionnaire based on upper managements identification of issues

Back 6

Delphi Approach

Front 7

In this approach, a questionnaire is sent to pre selected managers

Back 7

Delphi Approach

Front 8

In this approach, you re-pose questions an additional time giving respondents the opportunity to change thier answers

Back 8

Delphi Approach

Front 9

The biggest drawback to this approach is the expertise of the experts

Back 9

Delphi Approach

Front 10

This apporach can be conducted via email

Back 10

Delphi Approach

Front 11

In this approach, a set of well defined assumptions is developed by a companys upper management

Back 11

Scenario Writing Approach

Front 12

In this approach, a panel of experts is asked to develop a conceptual scenario of the future based on each set of well defined assumptions

Back 12

Scenario Writing Approach

Front 13

This process is often restricted to just a few experts and involves more time to properly develop

Back 13

Scenario Writing Approach

Front 14

This approach should be done as a very positive approach

Back 14

Brainstorming Approach

Front 15

In this approach, a group of managers will meet o discuss issues within the company

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Brainstorming Approach

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In this apporach, solutions will be developed and alternatives will be prioritized and thos suggestions that are less favorable will fall to the bottom,

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Brainstorming Apporach

Front 17

This apporach will encourage participation from all

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Brainstorming Approach

Front 18

This approach is similar to simple regression

Back 18

Time Series Analysis

Front 19

In ____, the X-variable is time (horizontal axis) rather than a specific variable

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Time Series Analysis

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Four important factors that influence all movements of time-related data sets

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Secular Trend
Seasonal Variation
Cyclical Variation
Random (Irregular) Movements

Front 21

long term movement over a period of time (over one year); rate of change is relatively constant

Back 21

Linear Trend (T)

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pattern tends to occur at the same time each year; movement is short term and is complete in less than one year

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Seasonality (s)

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wavelike movement, which in general follows the business activity over a relatively long period of time (3 years or longer); these movements take longer than one year to develop

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Cyclical (C)

Front 24

Four phases to cyclical variation

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upswing/expansion
downturn/contraction
peak
trough (lowest point)

Front 25

fluctuations caused by unusual occurences and produce no discernible pattern; considered to be long term movements

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Irregular (R or I)

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number of future periods covered by the forecast

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Forecasting horizon

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less than one month

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immediate-term

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one to three months

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Short-term

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three months to two years

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medium-term

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two years or more

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long-term

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model which is helpful in isolating seasonal movement

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multiplicative model

Front 32

Yt

Back 32

entire time series data set (the unadjusted actual value)

Front 33

Three Smoothing Techniques

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Moving Average
Weighted Moving Average
Exponential Smoothing

Front 34

purpose of this technique is to eliminate wil variations in the data set

Back 34

Smoothing

Front 35

series of arithmetic averages over a given number of time periods; estimate of the long run average of the variables

Back 35

Moving averages

Front 36

multiply the actual sales by the weights assigned to that week; then sum the weighted values

Back 36

Weighted Moving Average

Front 37

weighted average of current and past time period values

Back 37

Exponential Smoothing

Front 38

In the exponential smoothing approach, alpha must be selected to: ______

Back 38

minimize the mean square error

Front 39

WIth this techniques, a trend line is fitted to the data set using the method of least squares; the line is a best fit line which minimizes the variation

Back 39

Trend Projections

Front 40

t

Back 40

period of time

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bo

Back 41

intercept of the trend line

Front 42

b1

Back 42

slope of the trend line

Front 43

Tt

Back 43

forecast value of the time series in period t

Front 44

Any time series data set contain ______ elements; _____ is/are long term and _____ is/are short term

Back 44

four
three
one

Front 45

process that isolates the short term movements, fits a trend line to the long term movements, then re-inserts the short term movements

Back 45

Decomposition

Front 46

If my data set has no descernible seasonality, then i will use the ________ data set

Back 46

original

Front 47

If my data set has a discernable seasonality, then i will use the _________ approach

Back 47

Decomposition

Front 48

long term movement(s)

Back 48

Linear (T)
Cyclical (C)
Irregular (R)

Front 49

Short term movement(s)

Back 49

Seasonal (S)