High-quality quantitative data generated under standardized conditions is
critical for understanding dynamic cellular processes. We report strategies
for error reduction, and algorithms for automated data processing and for
establishing the widely used techniques of immunoprecipitation and immu-noblotting as highly precise methods for the quantification of protein levels
When you have completed this chapter, you will be able to: Organize raw data into frequency distribution; produce a histogram, a frequency polygon, and a cumulative frequency polygon from quantitative data; develop and interpret a stem-and-leaf display; present qualitative data using such graphical techniques such as a clustered bar chart, a stacked bar chart, and a pie chart; detect graphic deceptions and use a graph to present data with clarity, precision, and efficiency.
This much-needed book, from a selection of top international experts, fills a gap by providing a manual of applied quantitative financial analysis. It focuses on advanced empirical methods for modelling financial markets in the context of practical financial applications.Data, software and techniques specifically aligned to trading and investment will enable the reader to implement and interpret quantitative methodologies covering various models.
C H A P T E R
Good visibility – pictorial presentation of data
This chapter will help you to:
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illustrate qualitative data using pictographs, bar charts and pie charts portray quantitative data using histograms, cumulative frequency charts and stem and leaf displays present bivariate quantitative data using scatter diagrams display time series data using time series charts use the technology:
One of the most important advancements in recent social science research (including
applied social sciences and public policy) has been the application of quantitative
or computational methods in studying the complex human or social systems.
Research centers in
computational social sciences
have flourished on major university
campuses. Among others, the University of Chicago, University of Washington,
UCLA, and George Mason University have all established such a center recently to
promote the multidisciplinary research related to social issues....
Data can be defined as the quantitative or qualitative values of a variable. Data is plural of Datum which literally means to give or something given. Data is thought to be the lowest unit of information from which other measurements and analysis can be done. Data can be numbers, images, words, figures, facts or ideas. Data in itself cannot be understood and to get information from the data one must interpret it into meaningful information. There are various methods of interpreting data. Data sources are broadly classified into primary and secondary data....
Bài giảng "Phân tích dữ liệu trong phân tích định lượng - Data analysis quantitative research" giới thiệu tới người đọc các kiến thức: Sơ lược về SPSS, nghiên cứu khoa học và phân tích dữ liệu, quy trình nghiên cứu, vấn đề nghiên cứu,... Mời các bạn cùng tham khảo.
Astronomers are the oldest data collectors. The first catalogue of stars is due to
Hipparchus, in the second century B.C. Since that time, and more precisely since the end
of the last century, there has been an important increase in astronomical data. EHie to the
development of space astronomy during recent decades, we have witnessed a veritable
Confronted with this flood of data, astronomers have to change their methodology.
It is necessary not only to manage large databases, but also to take into account recent
developments in information retrieval....
This paper describes the application of the PARADISE evaluation framework to the corpus of 662 human-computer dialogues collected in the June 2000 Darpa Communicator data collection. We describe results based on the standard logﬁle metrics as well as results based on additional qualitative metrics derived using the DATE dialogue act tagging scheme. We show that performance models derived via using the standard metrics can account for 37% of the variance in user satisfaction, and that the addition of DATE metrics improved the models by an absolute 5%. ...
We present a corpus-based study of methods that have been proposed in the linguistics literature for selecting the semantically unmarked term out of a pair of antonymous adjectives. Solutions to this problem are applicable to the more general task of selecting the positive term from the pair. Using automatically collected data, the accuracy and applicability of each method is quantified, and a statistical analysis of the significance of the results is performed.
Chapter 6 – Sampling and estimation. This chapter include objectives: Define simple random sampling, define and interpret sampling error, distinguish between time-series and cross-sectional data; state the central limit theorem and describe its importance, distinguish between a point estimate and a confidence interval estimate of a population parameter,...
Chapter 2 - Descriptive statistics: Tabular and graphical methods. After mastering the material in this chapter, you will be able to: Summarize qualitative data by using frequency distributions, bar charts, and pie charts; construct and interpret Pareto charts (Optional); summarize quantitative data by using frequency distributions, histograms, frequency polygons, and ogives;...
Chapter 2 - Describing data: Frequency tables, frequency distributions, and graphic presentation. When you have completed this chapter, you will be able to: Organize qualitative data into a frequency table, present a frequency table as a bar chart or a pie chart, organize quantitative data into a frequency distribution, present a frequency distribution for quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
Chapter 2 - Describing data: Frequency tables, frequency distributions, and graphic presentation. After completing this unit, you should be able to: Organize qualitative data into a frequency table, present a frequency table as a bar chart or a pie chart, organize quantitative data into a frequency distribution, present a frequency distribution for quantitative data using histograms, frequency polygons, and cumulative frequency polygons.
The book is divided into four main parts: key issues in researching language learning/teaching, data collection, data analysis and writing up research. Both qualitative and quantitative methodologies are discussed, alongside popular mixed-methods approaches, such as triangulated studies, thus covering many of the methods commonly employed in the field.
This study uses what Cresswell (2003) refers to as a ‘mixed methods approach’, one that combines
quantitative and qualitative data collection and a ‘sequential explanatory strategy’ in which the
collection and analysis of the quantitative data is followed by the collection and analysis of the
qualitative data (p 215).
Recent advances in data collection and data storage techniques enable
marketing researchers to study the characteristics of a large range of
transactions and purchases, in particular the effects of household-specific
characteristics and marketing-mix variables.
This book presents the most important and practically relevant quantitative
models for marketing research. Each model is presented in detail
with a self-contained discussion, which includes: a demonstration of the
mechanics of the model, empirical analysis, real-world examples, and
interpretation of results and findings.
As currently taught, the introductory course in analytical chemistry emphasizes
quantitative (and sometimes qualitative) methods of analysis coupled with a heavy
dose of equilibrium chemistry. Analytical chemistry, however, is more than equilib-
rium chemistry and a collection of analytical methods; it is an approach to solving
chemical problems. Although discussing different methods is important, that dis-
cussion should not come at the expense of other equally important topics.
2 Features of marketing research data. The purpose of quantitative models is to summarize marketing research data such that useful conclusions can be drawn. Typically the conclusions concern the impact of explanatory variables on a relevant marketing variable, where we focus only on revealed preference data.