dataset for statistics project

dataset for statistics project

Utilizing Datasets for Statistical Analysis: A Comprehensive Guide

1. Introduction to Datasets and Statistical Analysis

A dataset (e.g. spreadsheet) is known as a file commonly composed of files to a table of rows (e.g. record) and columns (e.g. variables, attributes). The rows are usually an organization with a leading role called an observation. One can easily access and manipulate the records without any programming tasks by loading data into a software-supported dataset. Using any statistical software, one of the most important skills: data management is the one related to working with data in a dataset. This involves working with one or an entire dataset, transforming and extracting data based on various conditions, applying labeling and reductions, and also creating new variables. The data management (e.g. resampling, filtering, and summarizing data) is usually performed using simple built-in query and transformation capabilities of Excel, SPSS, Stata, and Mac OS/Windows. Data analysts can be expected to have at least basic skills in this area, and programming languages and scripts (e.g. SQL, Microsoft Access, R, and SAS).

Given that the data analyses and releases also receive worldwide attention, a dataset is considered to be one of the most essential elements for scientific research. Therefore, knowing how to properly utilize datasets is required for making valid scientific inferences. There are many different types of statistical datasets and various types of data analyses that can be performed. In this context, a detailed instructional guide for the entire process for statistical analysis is presented in this study. Here, we are dealing with data management, labeling, reduction, grouping, and validating datasets. Terms used are quite general and applicable to use in a wide variety of statistical processes and software. The purpose of this chapter is to simplify the descriptive and exploratory data analysis of datasets that can be applicable for any discipline.

2. Types of Datasets for Statistical Projects

At the Large Data level, finding critical points varies from straightforward to more complex datasets. It is frequently assumed that at about 450-500 observations, the Central Limit Theorem will hold and an approximate normal distribution can be justified for means. In these datasets, students learn relatively advanced statistical techniques. Very Large Data is a dataset with a large number of observations, and it can contain millions of units depending on the field and the research questions. These projects usually deal with big data analysis techniques and software which enable large-scale statistical testing. In general, these projects utilize computer-based statistical tools, such as R, Python, and SAS, for the analysis of large datasets. These projects usually involve practical experimentation, hypothesis testing, and statistical analysis or the prediction and forecasting of future influences perceivable.

The most commonly utilized datasets for statistical projects within all levels, beginners to advanced, are Very Small Data (VSD) and Small Data (SD). Very Small Data are datasets consisting of n ≤ 30 observations, while Small Data are datasets of n > 30 and n < 60 observations. Very Small Data are used more often by beginners and early stages of students and are more manageable for learning and introductory purposes. Although Small Data can be used for preliminary analyses, it is a more appropriate dataset size for advanced statistical analysis. Datasets containing 60 or more observations are suitable for students or researchers performing statistical projects at intermediate or advanced levels, related to learning on statistical packages, reporting accurate results, and describing contributions to the research field. These datasets are classified in the Medium (60 < n < 150), Large (150 ≤ n < 500), and Very Large Data formats (n ≥ 500).

3. Data Collection Methods and Techniques

Advantages of using secondary data are reduced time requirement for data collection, reduced cost, access to large quantities of data, and the ability to use secondary data for longitudinal studies. Disadvantages of using secondary data include the possible occurrence of inconsistency within the data and possible arrival of the information at a different time than required. Data quality may be below the researcher’s acceptability level and hence accuracy is unclear. Data may not track the exact information needs of the specific study and there may be a perceived risk of cyclical or structural errors within the data. Finally, using secondary data is a time-consuming process to evaluate given the large amount of available sources of secondary data. Examples of Secondary Data are YOV (Your opinion value data) EFSA (European food security association) DOJ (Department of Justice) BEA (Bureau of Economic analysis) IRS (Internal Revenue Service) ANTS – AIDED NEW TECHNOLOGY SYSTEM. Data collection instruments can be either structured or unstructured depending upon the participant’s exposure to the method of collection of data.

Data collection can be broadly classified into two types – primary data and secondary data. Primary data is the data obtained directly from original sources by using methods like survey, observation, and experimentation and is specific to the point of view of its original investigators. Secondary data is the data obtained from published or unpublished sources, which have already been collected by some other individual or organization for some other research objectives other than the user’s current objectives. Secondary data can be classified as internal and external secondary data. Internal secondary data can be obtained from the organization directly and relates to revenues, operations, sales, customers, and employees of the organization. External secondary data refers to the data collected or assimilated directly from sources outside the firm, and to a large extent, this data being already assembled ahead of time in a nonbipedal form, is obtained and made available to the firm.

4. Common Statistical Tools and Techniques

Bivariate correlation analysis. Most frequently, the relationship between variables is analyzed by using Pearson’s product moment correlation (r). This correlation analysis offers both the degree and the direction of the association between two continuous variables. Results are between -1 and 1, where 0 shows no relationship and -1 and 1 point out perfect negative or positive relationships, respectively. However, moderately strong negative or positive relationships are usually regarded as those from -0.5 to -1 and 0.5 to 1, respectively. With large sample sizes, practical applications and pattern configurations often become important for evaluating the significance of the relationships by ignoring the numerical results of correlation coefficients.

Univariate descriptive statistics. Univariate data analysis refers to the study of the distribution and properties of a single data variable. This type of analysis does not always involve inference or hypothesis testing. However, desiring to observe and understand the nature of your data is always crucial. A location (or central tendency) measure and a measure of variability are typically calculated for almost all continuous data distribution forms, depending on the level of measurement (i.e. nominal, ordinal, interval, and ratio). The most frequently used location measures are the mode, median, and arithmetic mean. Mode is generally recommended for usage with nominal-level data. Median, rather than arithmetic mean, can be preferred when there are outliers and skewness in the data.

5. Case Studies and Applications of Statistical Analysis

These are real applications of data mining and statistical modeling that show the value of using datasets. Some examples use methodologies described elsewhere in the book and are revisited with actual problems, datasets, and methods. Other examples use completely different statistical techniques and are included to illustrate the range of problems to use datasets. This is a rich source of applications and problem areas where statistical research issues are forefront.

This chapter is a compilation of many case studies, applications, and examples. This chapter combines previous editions of this chapter, primarily encompassing the topic itself from Ron Kenett’s “Modern Industrial Statistics: The Dossier” and his Applications of Statistics and Management Science: Selections from, and “Case Studies Using the Analysis of Industrial Data” published by the ASQC, as well as Charles Acree’s Alternative Approaches to Combining Data published by the ASA. New (or revised) material includes Tom Sheridan’s “A Field Guide for Developers of Workplace Safety DSSs,” Anne Robinson and Oded Netzer’s “New Approaches to Pricing and New Product Features,” and, to introduce the topic of Bayesian statistics, Christian Robert’s “Hierarchical Bayes Models of Opinions, Confidence and Uncertainty in Financial Risk Assessment and Decision Making: The ECB’s Risk Survey between 2001 and 2006.”