The theme of this. In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques are usually not well suited to managing this kind of data.
Carry out a variety of advanced statistical analyses including generalized additive models, mixed effects models, multiple imputation, machine learning, and missing data techniques using R. Skip to content. Toggle navigation. The aim of this Symposium was to promote advanced statistical methods in big-data sciences and to allow researchers to exchange ideas on statistics and data science and to embraces the challenges and opportunities of statistics and data science in the modern world.
It addresses diverse themes in advanced statistical analysis in big-data sciences, including methods for administrative data analysis, survival data analysis, missing data analysis, high-dimensional and genetic data analysis, longitudinal and functional data analysis, the design and analysis of studies with response-dependent and multi-phase designs, time series and robust statistics, statistical inference based on likelihood, empirical likelihood and estimating functions.
The editorial group selected 14 high-quality presentations from this successful symposium and invited the presenters to prepare a full chapter for this book in order to disseminate the findings and promote further research collaborations in this area. This timely book offers new methods that impact advanced statistical model development in big-data sciences. Advanced Statistical Methods in Data Science. Yi, Hao Yu. Questionnaire A questionnaire is one of the simplest and the quickest of getting information from a large number of people.
The respondents read the questions, interpret what is expected and then write down the answers themselves. Because there are many ways to ask questions, the questionnaire is very flexible. Open-ended Questionnaire: Open-ended questions allow respondents to answer in their own words. Questionnaire does not contain boxes to tick but instead leaves a blank 16 Arvin, Shelley.
Open-ended questionnaires might be used to find out what people think about a service. This type of questionnaire is in search of opinions which are rather than numbers; fewer questionnaires need to be distributed. Combination of both: This way it is possible to find out how many people use a service and what they think of the service in the same form. Begins with a series of closed — ended questions, with boxes to tick or scales to rank, and then finish with a section of open-ended questions or more detailed response.
The term is coined by psychologist and marketing expert Ernest Dichter In which the questions are asked in a group of people. Questions are asked in an interactive group setting where participants are free to talk with other group members.
The analysis of focus group data presents both challenges and opportunities when compared to other types of qualitative data. Some authors have suggested that data should be analysed in the same manner as interview data, while others have suggested that the unique features of focus group data - particularly the opportunity that it provides to observe interactions between group members - means that distinctive forms of analysis should be used.
Data analysis can take place at the level of the individual or the group. Focus group data provides the opportunity to analyse the strength with which an individual holds an opinion.
At the collective level, focus group data can sometimes reveal shared understandings or common views. Interview Questioning in the verbal form is known as Interview. As a research tool, interview is different from general interviewing in regard to preparation, construction and execution. It is controlled by the researcher to avoid any biasness and distortion.
In the research interview, the interviewer asks specific questions pertaining to research objectives and the respondent answers appropriately. The interview can be of flexible in its own form, such as structured or unstructured, individual or group, self- administered or other-administered, personal or non-personal, focused, telephonic, etc.
Interviewers differ in interest and skill, respondents differ in ability and motivation and content of interview differs in feasibility. To obtain a 23 Ibid. Each of these questions could be addressed using quantitative techniques such as structured questionnaires, attitude scaling, and measurement of standard outcomes such as mortality, morbidity or staff absence rates.
The R programs for statistical computation are clearly explained along with logic. You will come across various mathematical concepts, such as variance, standard deviation, probability, matrix calculations, and more.
You will learn only what is required to implement statistics in data science tasks such as data cleaning, mining, and analysis. You will learn the statistical techniques required to perform tasks such as linear regression, regularization, model assessment, boosting, SVMs, and working with neural networks.
By the end of the book, you will be comfortable with performing various statistical computations for data science programmatically. This site comply with DMCA digital copyright.
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