Introduction to Quantitative Analysis Flashcards
What is Quantitative?
It refers to the measurement, analysis, or description of something using numerical data and mathematical techniques, typically to quantify or express it in terms of quantity or amount
What is Quantitative Analysis?
A scientific approach to managerial decision making involving the processing of raw data to produce meaningful information.
Purpose: Helps decision-makers evaluate alternatives to make informed choices.
Process: Raw Data > Quantitative Analysis > Meaningful Information
Application of QA in a real situation
Quantitative analysis in a real situation means using math and statistics to figure out the best prices for products. For example, a retail company can use this method to find the right prices that make them the most money by looking at their sales history and customer data.
“Garbage In, Garbage Out” mean
Emphasizes the importance of data quality in the analysis process
What are the different types of data?
Alphanumeric: A data type that combines numbers and letters, often used for codes, identifiers, or alphanumeric strings.
Text: Data consisting of sentences and paragraphs used in written communication, such as articles, books, or documents.
Image: Data comprising graphics, shapes, figures, and visual elements, commonly found in pictures, illustrations, and diagrams.
Audio: Data representing human voices and other sounds, including music, speeches, or recorded conversations.
What is the maximax strategy in decision environments?
The maximax strategy involves looking at the best possible outcome for each course of action and selecting the action with the largest of these best outcomes.
Describe the maximin strategy in decision environments?
The maximin strategy involves considering the worst possible outcome for each course of action and choosing the action with the largest of these worst outcomes.
What is the Laplace strategy used for in decision analysis?
The Laplace strategy calculates the average outcome for each alternative and selects the one with the largest average as the decision.
Explain the Hurwicz strategy in decision-making, including the role of α.
The Hurwicz strategy uses a coefficient of realism, α. It multiplies the best outcome in a row by α and the worst outcome in the same row by (1-α), then adds these values together. The alternative with the highest resulting sum is chosen as the decision.
How does the minimax regret strategy work in decision analysis?
The minimax regret strategy computes the regret (opportunistic loss) for each alternative by subtracting each entry from the highest value in its column. It then selects the alternative with the lowest or minimum regret as the decision.
It helps you make a conservative decision by minimizing the potential maximum regret.
Decision Value = (α * Best Outcome) + ((1 - α) * Worst Outcome)
Depending on whether α is closer to 0 or 1, it makes the decision more optimistic or pessimistic. If α is closer to 1, it’s more optimistic, and if it’s closer to 0, it’s more pessimistic.
Optimistic refers to having a positive outlook or expectation for the future, believing that things will turn out well.
Pessimistic, on the other hand, refers to having a negative or gloomy outlook, expecting that things will not go well and focusing on potential problems or difficulties.
EV = P (X), where
Formula for Expected Value
- EV = expected value
- P = probability of an event
- X = amount to be received for a particular event
Qualitative
Qualitative data consists of non-numeric information that can be observed but not measured. It describes qualities or characteristics and is typically categorical in nature. Qualitative data is often used to categorize and classify information into groups or classes.
Examples:
- The colors of houses in a subdivision - Qualitative (This data consists of categories or labels, such as “red,” “blue,” “green,” etc., and does not involve numerical values.)
- Plate numbers of cars traveling around EDSA - Qualitative (The plate numbers are typically alphanumeric codes or labels, and they do not represent numerical values.)
Quantitative
Quantitative data, on the other hand, is numeric and measurable. It represents quantities, amounts, or values and is typically expressed through numbers. This type of data is used for performing mathematical calculations and statistical analysis.
Examples
- **The general weighted average of the Dean’s Listers **- (This data involves numerical values representing the average, which can be measured and quantified.)
- Student’s score in a Statistics exam - (This data involves numerical values representing the scores achieved by students and can be measured.)
- Number of tourist individuals entering the country - (This data involves numerical values representing the count of tourists, which can be quantified and measured.)
Continuous
- Continuous data is data that can take on an infinite number of values within a given range.
- It is measured and represented on a continuous scale and can have decimal or fractional values.
- Continuous data is often associated with measurements and is not limited to specific distinct points.
- Examples of continuous data include height, weight, temperature, and time.
-
Waiting time at a dental clinic - (Waiting time can take on any real value within a range, including fractions of minutes or seconds.)
2.** Liters of bottled syrup** - (The volume can take on any real value within a range, including fractions of liters.)
3.**Distance traveled **- (Distance can take on any real value within a range, including fractions of a unit of measurement like kilometers or miles.)
