Regression analysis is a family of statistical tools that can help
sociologists better understand and predict the way that people act and
interact. Regression analysis is used to build mathematical models to
predict the value of one variable from knowledge of another. Although
statistical methods of correlation offer researchers techniques to help
them better understand the degree to which two variables are
consistently related, such knowledge alone is typically insufficient
to predict behavior. Simple linear regression allows the value of one
dependent variable to be predicted from the knowledge of one
independent variable. Multiple linear regression can be used to develop
models to predict the value of a dependent variable from the knowledge
of the value of more than one independent variable.
For example, a sociologist interested in the behavior of small groups
might want to determine whether or not the efficacy of the decisions
made in small groups could be predicted from the number of people in
the group. Although larger group size could mean that there are more
ideas, more contribution to the thinking process, and a larger
potential for synergistic thinking, a larger group could also mean that
more time would be required to reach a decision, the competition of
ideas could lead to confusion, and coalitions could form within the
group and make it harder to resolve disagreements. A predictive model
for group size versus efficacy of decision making could be developed
by setting up an experiment that compared the efficacy of decision
making on the same problem for groups of various sizes. The slope of
the line of best fit passing through the data points on the scatter
plot could be mathematically calculated, using these data points to
determine the equation of the simple regression line. This equation
could then be used by the sociologist to recommend optimal group size
for similar types of decisions or projects based on the single variable
of number of group members.
The problem with drawing a line of best fit through a scatter plot, of
course, is that unless all the pairs of data fall on one straight line,
it is possible to draw multiple lines through a data set. The question
faced by the researcher is how to determine which of these possible
lines will yield the best predictions of the dependent variable from
the independent variable. This can be accomplished mathematically
through residual analysis.
In regression analysis, a residual is defined as the difference between
the actual y values and the predicted y values, or y - y^. To find the
line of best fit, it is important to reduce the distance between the
points on the scatter plot and the line. This is done by minimizing
the sum of the squares of the residuals in order to find the line of
best fit. By looking at the residuals, a researcher can better
understand how well the regression line fits past data in order to
estimate how well it will predict future data.
Forecasting involves using past data to generate a number, set of
numbers, or scenario that corresponds to a future occurrence. It is
absolutely essential to short-range and long-range planning.
Forecasting provides information about the potential future events and
their consequences for the organization. It may not reduce the
complications and uncertainty of the future. However, it increases the
confidence of the management to make important decisions. Forecasting
is the basis of premising.
Quantitative forecasting techniques are generally more objective than
their qualitative counterparts. Quantitative forecasts can be timeseries forecasts or forecasts based on associative models. Time-series
data may have underlying behaviors that need to be identified by the
forecaster. In addition, the forecast may need to identify the causes
of the behavior. Some of these behaviors may be patterns or simply
random variations. Among the patterns are:
Trends, are long-term movements (up or down) in the data.
Seasonal, it produces short-term variations that are usually
related to the time of year, month, or even a particular day, as
witnessed by retail sales at Christmas or the spikes in banking
activity on the first of the month and on Fridays.
Cyclical, are wavelike variations lasting more than a year that
are usually tied to economic or political conditions.
Random (Irregular) variations that do not reflect typical
behavior, such as a period of extreme weather or a union strike.
Among the time-series models, the simplest is the naïve forecast. A
naive forecast simply uses the actual demand for the past period as
the forecasted demand for the next period. This, of course, makes the
assumption that the past will repeat. It also assumes that any trends,
seasonality, or cycles are either reflected in the previous period's
demand or do not exist.
Foresight is the capacity to think systematically about the future to
inform decision making today. It is a cognitive capacity that we need
to develop as individuals, as organizations and as a society. In
individuals, it is usually an unconscious capacity and needs to be
surfaced to be used in any meaningful way to inform decision making,
either as individuals or in organizations. It’s a capacity we use
Foresight is first and foremost a state of mind that determines how
you think about the future. It underpins how you design foresight
approaches and how you implement them in your organizations. It needs
ways of thinking and doing that are unlike those required for
conventional strategic planning processes. Foresight is therefore a
strategic thinking capacity. Done well, it expands perceptions of
future options available to the organization and enhances the
operational context in which strategy is developed. Its use allows new
strategic options to emerge and proactive responses to change to be
developed. Done less well, it usually generates an interesting
experience but there is little change to how strategy is developed or
the understanding of the scope of change shaping the organization’s
IV. Agile Strategy
Agile strategy is an approach to project management. Agile strategy
break tasks into small increments with no direct long term planning.
The idea behind the Agile strategy is that it can be adapted and
adjusted as a project develops to reflect the changes that are
happening all-around the organization. Since things like outside market
forces and consumer interests can’t necessarily be forecast at the
start of the project, using the Agile strategy will give you the
opportunity to change course as needed to make sure the final result
of the project is something that will be of value to the organization.
Traditional facet of project management that most leaders are familiar
with is the idea of setting project requirements right from the start.
Usually, the scope of the project is established clearly before any
work begins, and all work going forward is guided by the goals that
have been laid out. The Agile methodology ignores this approach and
instead establishes the requirements for the project on an ongoing
basis during the project. That means that what ends up being
accomplished within the project could be very different than what was
expected at the start. The advantage to this kind of management is
that the project is flexible enough to develop over time until it
precisely suits the needs of the market. You don’t run the risk of
creating a product that has no market at the end of the project because
the project teams have the ability to adapt the final deliverables all
throughout the process. Being responsive and flexible is typically a
good thing for businesses because they can avoid falling out of touch
with what the market is asking for.
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