Statistical Forecasting

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 and confidence.

Here below are information on topics related to the field of forecasting and statistics. You will also find topics related to forecasting in business.

Summary of Methods used in Forecasting
Sales Forecasting
Budget Forecasting
How to Do Forecasting by Regression Analysis
Box-Jenkins Methodology
Modeling Financial Time Series
Multivariate Data Analysis
Central Limit Theorem
Structural Equation Modeling
Geometric Mean
Purpose of Budgets : Should you even have a personal budget?
Sales Forecasting Techniques
Origin of Statistics and Probability
Misuse of Statistics
Strange and Interesting Statistical Facts
What to consider in using Statistics in Decision Making
Statistical Sampling Methods
Characteristics and Differences of Mode, Median and Mean
More information on Statistics
More information on Forecasting
Data Mining and Knowledge Discovery

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 data. Statistical 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 are:

Population - a set of all the elements of interest in a study
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.


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