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In today’s world of digitization and the emergence of large amounts of data, it is extremely important that we know how to extract the information that this data captures and that represents foundations we need in organizations for efficient decision-making. Therefore, for graduates, regardless of the field of study, the knowledge of quantitative methods and the ability of “statistical thinking” is extremely important. Quantitative methods and quantitative approach to research and analysis of data imply important advantages over qualitative approach, but their combination can certainly in many areas mean an effective approach to obtaining information that is hidden within data.
In this chapter, we first consider some selected methodological approaches to data analysis, in particular the basic principles of inferential statistics, which enables us to generalize the results of a random sample, with a certain degree of probability, to a statistical population. We also show how to use the logistic regression methodology on a case that shows the importance of combining the economic and geographic aspects of research, i.e. the “spationomy” approach.
In the next part of the chapter the multi-criteria decision-making is presented, that has proven useful in solving spatial decision problems. We develop and apply the multi-criteria model for the protection of agricultural land for food self-sufficiency. Based on the literature review, the theoretical backgrounds of multi-criteria decision-making, together with the use of multi-criteria decision making in land-use evaluation and management are introduced. In addition, the multi-criteria model for the protection of agricultural land for food self-sufficiency is developed, taking into account the characteristics of the protection of agricultural land and public data base information in Slovenia. For this purpose, we followed the frame procedure for multi-criteria decision making by using the group of methods based on assigning weights to criteria – in this research, we used the Simplified Multi-attribute Rating Technique and the Analytic Hierarchy Process. Selected geographical and economic factors were structured in the criteria hierarchy. In synthesis, the additive model was used in order to select the most favorable solution. The aggregate values obtained with an additive model were completed by considering synergies and redundancies among criteria by a fuzzy measure – discrete Choquet integral. The results enable suggesting measures for the protection of agricultural land for food self-sufficiency.
The normal distribution, characterized by the bell-shaped curve, is the most important probability distribution for statistical analysis. It is described in details for example in Agresti and Finlay ( 2009, p. 78).
T-distribution is described in details for example in Agresti and Finlay ( 2009, p.118).
According to the AHP scale: 2 – from equally important to moderately more important.
The given points correspond to the ones of employment and payment presented in Chen ( 2014).
This is also supported by the results of studies conducted in other countries (see, e.g., Mannaf and Uddin 2012).
The sensitivity analysis was made to verify how changes in criteria’ weights influence the aggregate alternatives’ values.
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- Quantitative Methods
- Chapter 2