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Case study analysis of inventory control in an organization
Different types of quantitative studies
The nature of quantitative data
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Quantitative data
First of all before writing about Quantitative Techniques, we should know about the Quantitative Data. Quantitative data is kind of data that can be measured numerically. For Example: quantitative data is used to measure things accurately, such as the temperature, the amount of people in a crowed or the height of a structure.
Although quantitative data usually involves numbers and equations, some data records actions, such as the frequency.
Quantitative analysis
After knowing about the data we should know about how to analyze the data that are collected. Analysis here means that seeks to understand behavior by using complex mathematical and statistical modeling, measurement and research.
Quantitative analysis can be done
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With the help of quantitative techniques, the decision maker is able to discover policies for attaining the lay down objectives. Quantitative techniques also include methods that focus on objective measurements and analyzing numbers in order to describe conclusions about research subjects. In short, quantitative techniques are to be expected in decision-making process.
Types of Quantitative Techniques:
Quantitative techniques can be classified into three categories. They are:
1. Mathematical Quantitative Techniques
2. Statistical Quantitative Techniques
3. Programming Quantitative Techniques
1. Mathematical Quantitative Techniques
A technique in which quantitative data are used down with the principles of mathematics is known as mathematical quantitative techniques.
Mathematical quantitative techniques entail: Permutation and Combinations Set Theory Matrix Algebra Determinants Differentiation Integration Differential Equation
Among the above mention mathematical quantitative techniques, we did about the Matrix Algebra and
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Uses of Quantitative Techniques
Today, all decisions in business and industry are made with the help of quantitative techniques.
Quantitative techniques of linear programming are used for best allocation of scarce resources in the problem of determining product mix.
Inventory control techniques are useful in dividing when and how much items are to be acquire so as to maintain a balance between the cost of holding and cost of ordering the inventory.
Quantitative techniques can use statistics to generalize a finding.
It often reduces and restructures a complex problem to a limited number of
First, all data have both an objective and a subjective component. Numbers can be easily assigned to all qualitative data (such as open-ended questions in surveys), and any number obtained by a quantitative study is interpreted using a subjective or qualitative judgment. Second, using differen...
The SWOT analysis involves four steps. They are strength, weakness, opportunity, and threats. This will assist you to ident...
Broadly, statistics is a set of disciplines for study quantitative information. Implied that several methods used to collect or process or interpret quantitative data from large amount of information, then finally generate a calculated number, for example average, mean, standard deviation…etc. All of these are the key reference for decision making or predicting consequences. Thus, it enables us to estimate the extent of our errors.
I am investigating the impact of math in the various parts of baseball. I have collected data from the various aspects of baseball such as hitting statistics and pitching statistics that are from the MLB. With this collection of data that I have, I used a number of mathematical processes to analyze this data and get to the final statistic. Such as the pitcher's statistics of how many runs they allowed in a certain amount of innings and applying the mathematical formulas to figure out the end statistic which is their ERA and that tells us overall how good that pitcher is performing. Some of this data is shown in graphs throughout the paper and another big formula used in baseball is the projectile motion formula used to calculate how far home runs travel. In the end these calculations will prove how math is so important to baseball and how these formulas contribute to that by figuring out the end statistics of various parts of important baseball data such as hitting and
Quantitative means statistics that involve numbers e.g. IQ, weight and qualitative means statistics that are not shown with numbers e.g. hair and eye colours. The two investigations I decided to do were: 1) Two pieces of quantitative information - Contrast the variations in weight and height.. The aim is to find out if there is any correlation between weight and height and if so what it is. Also I will separate this coursework further by dividing it into male and females to see if there is any difference in correlation there.
Quantitative methods in the social sciences are an effective tool for understanding patterns and variation in social data. They are the systematic, numeric collection and objective analysis of data that can be generalized to a larger population and seek to find cause in variance (Matthews and Ross 2010, p.141; Henn et al. 2009, p.134). These methods are often debated, but quantitative measurement is important to the social sciences because of the numeric evidence that can be used to drive more in depth qualitative research and to focus regional policy, to name a few (Johnston et al. 2014). Basic quantitative methods, such as descriptive and inferential statistics, are used regularly to identify and explain large social trends that can then
8.) Data - means facts or information. People use data as a basis for drawing conclusions about the topic or theme they are studying.
... (such as linear regression and logistic regression) or forecasting techniques (such as neural networks and survival analysis) can be used to determine a given result, such as demand forecasting or customer attraction through direct marketing and evaluate their responses.
Variety. Variety is the different data types, representation and semantic interpretation. Dumbill (2012: 7) declares that “rarely does data present itself in a form perfectly ordered and ready for processing - it could be text from social networks, image data, a raw feed directly from a sensor source”.
Decisions can be made using below mentioned approaches (Various Types Of Decision Making Models, 2009).
On the other hand, Quantitative research refers to “variance theory” where quantity describes the research in terms of statistical relationships between different variables (Maxwell, 2013). Quantitative research answers the questions “how much” or “how many?” Quantitative research is an objective, deductive process and is used to quantify attitudes, opinions, behaviors, and other defined variables with generalized results from a larger sample population. Much more structured than qualitative research, quantitative data collection methods include various forms of surveys, personal interviews and telephone interviews, polls, and systematic observations. Methods can be considered “cookie cutter” with a predetermined starting point and a fixed sequence of
Probability and Statistics most widespread use is in the arena of gambling. Gambling is big all over the world and lots of money is won and lost with their aid. In horse racing especially the statistics of a horse in terms of its physical condition and winning history sway numbers of persons into believing that the mathematical evidence that is derived can actually be a good indicator of a race’s outcome. Usually it is if the odds or probability are great in favor of the desired outcome. However the future is uncertain and races can turn out any of a number of different ways.
On the other hand, quantitative research allows you to test hypothesis derived from theories, associated with the issues being investigated. It is less flexible, as there are standardized procedures and techniques for collecting, organizing and analyzing the data (Kuada, 2012).
To achieve the research objectives the process of research must be carried out by certain principles and to use appropriate methods. It is very important that the methods used to obtain the desired results, and this starts to clearly define the objectives and what we need to know, and also by choosing the methods and tools to help us and to ease the process. (Kumar, 2008)
The basic aspects of the decision making process. First the problem is defined, relevant objects are observed and classified, measurements are taken of their activities and data are collected in a database or file so that the decision maker can understand the problem. From this data, functional relationships are develop from various patterns and predictions, or inferences are made. Criteria are selected for decision making and for enlisting various alternatives. From these criteria a decision model is used to select the best or most satisfactory alternatives or course of action. Management action is then taken. Finally, an accounting system is used to measure the outcome of the action and to feed the results back into the decision process for management to make the next decision. In some case, many of these processing steps are automated. In other case, they are done by the decision maker based on the information provided by the accounting information system and computer software used to assists management in the decision making process. Throughout the decison making process are communication channels through which information flows, and there are numerous feedback loops for more information on the results of actions and its bearing on future decisions.