Best Ways to Analyze Data in Order to Improve Decision-Making

Descriptive Analysis:
Defined as quantitatively describing the main features of a collection of information. Descriptive analysis are distinguished from inferential analysis (or inductive analysis), in that descriptive analysis aim to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent. Two types of descriptive measures are: 1. Measures of central tendency: used to report a single piece of information that describes the most typical response to a question. 2. Measures of variability: used to reveal the typical difference between the values in a set of values. Two types of descriptive analysis are:

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1. Univariate analysis: Univariate analysis involves describing the distribution of a single variable, including its central tendency (including the mean, median, and mode) and dispersion (including the range and quantiles of the data-set, and measures of spread such as the variance and standard deviation). 2. Bivariate analysis: Used when a sample consists of more than one variable. Bivariate analysis is not only simple descriptive analysis, but also it describes the relationship between two different variables.

Descriptive statistics provides simple summaries about the sample and about the observations that have been made. In the business world, descriptive statistics provides a useful summary of many types of data. For example, investors and brokers may use a historical account of return behavior by performing empirical and analytical analyses on their investments in order to make better investing decisions in the future.

Inferential Analysis:
Used to generate conclusions about the population’s characteristics based on the sample data. For example to estimate the population mean weight using the sample mean weight. They can use inferential statistics to make judgments of the probability that an observed difference between groups is a dependable one or one that might have happened by chance. They can help in fact-based management to drive favorable decision making.

Differential Analysis:
Defined as a technique in which evaluation is confined to only those factors which are different or unique among possible alternatives. Also called incremental analysis or relevant cost analysis. It usually involves four steps:

1. Compute all costs associated with each alternative.
2. Ignore the sunk costs.
3. Ignore costs that remain largely constant among the alternatives.
4. Select the alternative offering the best cost-to-benefit ratio.

It’s important to note that differential analysis is a process that may be stretched beyond mere numbers. There could be intangible benefits to a certain decision that might eventually affect the business’ profits or even go beyond monetary gain.

Predictive Analysis:
Encompasses a variety of techniques that analyze current and historical facts to make predictions about future, or otherwise unknown, events. The approaches and techniques used to conduct predictive analytics can broadly be grouped into regression techniques and machine learning techniques. 1. Regression techniques: Regression models are the mainstay of predictive analytics. The focus lies on establishing a mathematical equation as a model to represent the interactions between the different variables in consideration. Depending on the situation, there is a wide variety of models that can be applied while performing predictive analytics. 2. Machine learning techniques: Machine learning, a branch of artificial intelligence, was originally employed to develop techniques to enable computers to learn.

Today, since it includes a number of advanced statistical methods for regression and classification, it finds application in a wide variety of fields including medical diagnostics, credit card fraud detection, face and speech recognition and analysis of the stock market. In certain applications it is sufficient to directly predict the dependent variable without focusing on the underlying relationships between variables. In other cases, the underlying relationships can be very complex and the mathematical form of the dependencies unknown. For such cases, machine learning techniques emulate human cognition and learn from training examples to predict future events.

In business, predictive models exploit patterns found in historical and transactional data to identify risks and opportunities. Models capture relationships among many factors to allow assessment of risk or potential associated with a particular set of conditions, guiding decision making for candidate transactions. They also help uncover hidden patterns and associations thus improving business outcomes.

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