Statistical forecasting: Estimating the likelihood
of an event taking place in the future, based on available data.
Statistical forecasting concentrates on using the past
to predict the future by identifying trends, patterns and business drives
within the data to develop a forecast. This forecast is referred to as
a statistical forecast because it uses mathematical formulas to identify
the patterns and trends while testing the results for mathematical reasonableness
Here below are information on topics related
to the field of forecasting and statistics. You will also find topics
related to forecasting in business.
Topic for the Day:
What is Statistical Data Analysis?
To determine what statistical data analysis is, one
must first define statistics. Statistics is a set of
techniques that are used in collecting, analyzing, presenting, and interpreting
methods are used in a wide variety of occupations and help people identify,
study, and solve many complex problems. Statistics is also widely used
in the business and economic world. Statistics makes complex data more
understandable to decision makers and managers, who are then able to
make better informed decisions.
There is a lot of information available
in today's environment because of continual improvements
in computer technology. To compete successfully and on a global scale,
managers and decision makers need to be able to understand the information
collected and use it effectively. Statistical data analysis provides
hands on experience to promote the use of statistical thinking and techniques
to apply in order to make educated decisions in the business world.
Computers play a very important role in statistical
data analysis. Studying a problem through the use of statistical data
analysis usually involves four basic steps.
1. Defining the problem
2. Collecting the data
3. Analyzing the data
4. Reporting the results
Defining the Problem
An exact definition of the problem is imperative in order
to obtain accurate data about it. It is extremely difficult to gather
data without a clear definition of the problem.
Collecting the Data
We live and work at a time when data collection and statistical
computations have become easy almost to the point of triviality. Paradoxically,
the design of data collection, never sufficiently emphasized in the statistical
data analysis textbook, have been weakened by an apparent belief that
extensive computation can make up for any deficiencies in the design of
data collection. One must start with an emphasis on the importance of
defining the population about which we are seeking to make inferences,
all the requirements of sampling and experimental design must be met.
Designing ways to collect data is an important job in
statistical data analysis. Two important aspects of a statistical study
Population - a set of all the elements of interest in
Sample - a subset of the population
Statistical inference is refer to extending your knowledge
obtain from a random sample from a population to the whole population.
This is known in mathematics as an Inductive Reasoning. That is, knowledge
of whole from a particular. Its main application is in hypotheses testing
about a given population.
The purpose of statistical inference is to obtain information
about a population form information contained in a sample. It is just
not feasible to test the entire population, so a sample is the only realistic
way to obtain data because of the time and cost constraints. Data can
be either quantitative or qualitative. Qualitative data are labels or
names used to identify an attribute of each element. Quantitative data
are always numeric and indicate either how much or how many.
For the purpose of statistical data analysis, distinguishing
between cross-sectional and time series data is important. Cross-sectional
data re data collected at the same or approximately the same point in
time. Time series data are data collected over several time periods.
Data can be collected from existing sources or obtained
through observation and experimental studies designed to obtain new data.
In an experimental study, the variable of interest is identified. Then
one or more factors in the study are controlled so that data can be obtained
about how the factors influence the variables. In observational studies,
no attempt is made to control or influence the variables of interest.
A survey is perhaps the most common type of observational study.
Analyzing the Data
Statistical data analysis divides the methods for analyzing
data into two categories: exploratory methods and confirmatory methods.
Exploratory methods are used to discover what the data seems to be saying
by using simple arithmetic and easy-to-draw pictures to summarize data.
Confirmatory methods use ideas from probability theory in the attempt
to answer specific questions. Probability is important in decision making
because it provides a mechanism for measuring, expressing, and analyzing
the uncertainties associated with future events. The majority of the topics
addressed in this course fall under this heading.
Reporting the Results
Through inferences, an estimate or test claims about
the characteristics of a population can be obtained from a sample. The
results may be reported in the form of a table, a graph or a set of percentages.
Because only a small collection (sample) has been examined and not an
entire population, the reported results must reflect the uncertainty through
the use of probability statements and intervals of values.
To conclude, a critical aspect of managing any organization
is planning for the future. Good judgment, intuition, and an awareness
of the state of the economy may give a manager a rough idea or "feeling"
of what is likely to happen in the future. However, converting that feeling
into a number that can be used effectively is difficult. Statistical data
analysis helps managers forecast and predict future aspects of a business
operation. The most successful managers and decision makers are the ones
who can understand the information and use it effectively.