Statistics for Bi - Hypothesis Testing

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Statistics for Business Intelligence – Hypothesis Testing


1. What is Hypothesis testing in Business Intelligence terms? 2. Define - “Statistical Hypothesis Testing” – “Inferences in Business” – and “Predictive Analysis” 3. Importance of Hypothesis Testing in Business with Examples 4. Statistical Methods to perform Hypothesis Testing in Business Intelligence 5. Identify Statistical variables required to compute Hypothesis testing. a. Correlate computing those variables from the data available in normalized tables arranged in row x columns. 6. Computing Statistical Hypothesis Testing for Business Decisions using Algorithms 7. User Interface Development for Presentation of Hypothesis feature 8. How does it fit in Prajna?

1. What is Hypothesis testing in Business Intelligence?

Hypothesis Testing – is used to prove or disprove the research (Business proposed decision) hypothesis by providing more measurable or concrete hypothesis statement. for example, a research hypothesis could be that the stock market index reflects the state of monsoon in the country. A statistical hypothesis might look at the values of the index with the percentage increase or decrease in rainfall during the year compared to previous years.

Hypothesis Testing is a study about
* How to test a sample against a benchmark?
* How to assess the risk of incorrect decisions?
Identifying the confidence intervals for a decision-required item does these.

2. Statistical Hypothesis Testing – Inferences drawn in Business – and Predictive Analysis made for Business

Inferences – Inferential statistics is the term given to the branch of statistics that uses the information from the sample to infer the information about the population. For example, given a sample mean , the population mean (also called a parameter) can be determined using inferential statistics.

Statistical Hypothesis – A statistical hypothesis test is a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled). In statistics, a result is called statistically significant if it is unlikely to have occurred by chance alone, according to a pre-determined threshold probability, the significance level. The phrase "test of significance" was coined by Ronald Fisher: "Critical tests of this kind may be called tests of significance, and when such tests are available we may discover whether a second sample is or is not significantly different from the first."[1]

Predictive Analysis - 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.

A hypothesis has the potential to be correct or incorrect and still be called a hypothesis, whereas a prediction must end up being correct in order to be called a prediction. A hypothesis can most definitely be seen as a prediction, if you are correct. If not, it was just a hypothesis, theory, or educated guess that was wrong.

3. Importance of Hypothesis Testing in Business Intelligence –

Every day in business, whether the business is small or large, profit or nonprofit, managers are faced with decisions. Examples:
1. Should a school district hire a full time staff member to perform background checks on substitute teachers or should that task be outsourced? 2. Does a large car dealership need to provide better training for its maintenance department employees? 3. Should a Ice Cream Manufacturer raise the price on single-scoop on ice-cream cones?

Today’s managers also have to constantly show improvement in their business processes.

1. Has a Ski resort decreases its response time to accidents since last year? 2. Does a new manufacturing process produce...
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