Descriptive Data Analysis Descriptive Data Analysis for Nominal Variables: Gender and Local Political Party Activism Organize the Data into a Frequency Distribution Display the Data in a Graph Describe What Is Average or Typical of a Distribution Describe Variability Within a Distribution Describe the Relationship Between Two Variables Descriptive Data Analysis for Ordinal Variables: Continuing Our Research Example Organize the Data into a Frequency Distribution Display the Data in a
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Methodology Introduction The aim of this chapter is to present and discuss the methodology applied to the research of this dissertation. It has been identified in the literature review that there is still a lack of data regarding festival experience and the underlying motives behind attending a festival. In order to gain a deeper insight into festival attendee motives the researcher will focus on the popular Tomorrowland Festival to identify the motivations of attendees and how the overall production
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Research Design Content analysis is a process in which narrative data are divided into units of analysis to examine the contents of a particular body of material for the purpose of identifying patterns‚ themes or biases (Leedy and Ormrod‚ (2005). Further‚ qualitative content analysis has been defined in other ways such as: • “a research method for the subjective interpretation of the content of text or [narrative] data through the systematic classification process of coding and identifying
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"thematic analysis method by Boyatzis (1998). This method consists of four category theory-driven codes‚ derived from the researcher’s or other existing theories; inductive codes‚ derived bottom-up from the researcher’s reading of the data; and prior-research driven codes. This helps the researcher moves his analysis from a broad reading of the data towards discovering patterns and developing themes. Also‚ the researcher develops "codes‚" words or phrases that serve as labels for sections of data. Depending
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Statistical Analysis and Application of Charts Presented To: Mam Ayesha IftikharPresented By: Hassan Bashir Roll Number: bba02141016 Program : BBA Semester : 2nd Date: 19-Oct-2014 Research Questionnaire/ Objective: Analysis of quantitative and qualitative data Uses of appropriate charts under the specific/general scenario. To ensure that statistical tools are the important for decision making. Type of Data: Quantitative Data Qualitative data Quantitative Data: Quantitative data is data expressing
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research. The qualitative research design was used in this study. Data was collected from an interpretative perspective thus provided a holistic understanding of how participants related and interacted with each other in this study (Maree‚ 2010). An exploratory design was also used to explore and answer the research questions. Qualitative research design is flexible and exploratory in nature. This method allows the researcher to adjust data continuously until a full meaning and knowledge of what is
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is a set of data in which the observations are written yij i= 1‚2 ‚…‚I j= 1‚2‚…‚J and displayed in a rectangular array as shown below TABLE1. Format and notation for a Two-Way Table i J 1 … J 1 . . . I y11 . . . y1J . . . . . . . . . yI1 . . . yIJ This data structure
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using Tracking Research data Approach: The analysis of brand Sofy has been done with a two stages of statistics and model building approach. MATRIX IDENTIFICATION At the very first stage the data for Sofy was plotted in scatter graphs for pattern identification. The various combinations of variables for independent and dependent variables were taken to shortlist the variables for further scientific tests. TEST AND ANALYTICS In the next stage of scientific data analysis‚ test of significance
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Greaves Brewery Case Analysis Greaves Brewery: Bottle Replenishment Case Analysis Case Synopsis The following is an analysis of the case‚ Greaves Brewery: Bottle Replenishment. It details the growing beer operation of Greaves Brewery located in the Caribbean island of Trinidad. The purchasing manager for the company‚ Alex Benson‚ is uncertain about how many bottles to order from the company’s German glass supplier. His decision is complicated by the possibility of a new bottle design being
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sensitive to the local structure of the data. The algorithm has nothing to do with and is not to be confused with k-means‚ another popular machine learning technique. When the input data to an algorithm is too large to be processed and it is suspected to be redundant (e.g. the same measurement in both feet and meters) then the input data will be transformed into a reduced representation set of features (also named features vector). Transforming the input data into the set of features is called feature
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