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The origin of statistics was due to the administrative requirements of the state. Statistical methods are techniques or principles with the help of which numerical data is systematically treated, so as to present a comprehensible view of them. Thus statistics is a science of collection, presentation, analysis, and interpretation of data. Applied statistics crop up a course designed for senior undergraduate and research students who need to ma out experiments and bear out statistical analysis of their data. The content generally covers a huge number of topics which are motivated by problem solving in many diverse departments of application. Applied statistics assignment help bring forth a well-built and trouble free method to understand all the subjects with perfection. The thesis encrusted will include modelling with emphasis on model formulation, grasping the implication of model assumptions, diagnostic mechanism for model checking and interpretation, log – linear regression for Poisson counts, and exploratory tools for summarizing multivariate responses. So the applied statistics assignment help reduces the difficulty level in understanding the above mentioned matters.
About Applied Statistics
Applied statistics has come into existence as a denouement of an experiment and ample experience of over and above 40 years. Applied statistics is intended to pioneer the concepts, interpretation, and terminology of the course of study in an elementary presentation with least possible mathematical background which does not surpass college algebra. It is a compendium, an elementary initiation to the mount up field of statistics.It takes nearly 15 week, 3 hours per week, with little adjustment as time allows for covering the contents subdued in applied statistics. Through applied statics assignment help the material had been presented in such a way that only college algebra can be a prerequisite for the course that covers the whole text.
Upon victorious fulfillment of the requirements for this course, students will be able to
• Working proficiency of the statistical computing package.
• Fit modest as well as numerous linear regression models and interpret model parameters.
• Encapsulate and scrutinize relationships between a response variable and a co variate or covariates.
• Assess and cleanse simple and multiple linear regression models based on diagnostic measures.
• Negotiate model selection in a multiple linear regression modelling context.
• Understand basic multivariate analyses techniques and the bootstrap.
Errors in Statistics
• Type I Errors – This type of error occurs where the null hypothesis is incorrectly discarded giving a false positive.
• Type II Errors – This type of error occurs where the null hypothesis fails to be discarded and a real difference between populations is missed.
• It can be used to study numerical facts. It cannot be used for measuring qualitative data like intelligence or beauty.
• Statistics cannot study individual items. In statistics we can consider only a group of values. It is not possible to say the average mark of a student in a particular subject.
• It is only a method of solving a problem.
• Only experts can understand statistics.
• It is true only to an average. It need not be accurate in all cases.
The applied statistics assignment help deals with the problems as well as the benefits of statistics and bring forth the best to the students.
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