# Numerical and statistical methods pdf

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Posted on Basic concepts of Probability and Discrete Random Variables. Course Educational Objectives: To acquaint students with the fundamental concepts of probability and statistics and to develop an understanding of the role of statistics in engineering. Also to introduce Numerical techniques to solve the real world applications. Course Outcomes: Upon successful completion of the course, the students should be able to Calculate fundamental concepts such as the cumulative distribution function, expectations, and distributions of random variables. Evaluate estimators, construct confidence intervals, and perform hypothesis tests. Solve engineering problems using Numerical techniques. Estimation of Variances point and Interval estimation , Hypotheses concerning one variance, Hypotheses concerning two variance, Estimation of Proportions, Hypotheses concerning one Proportion, Hypotheses concerning several Proportions.
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## Data Types in Statistics Data Types are an important concept of statistics, which needs to be understood, to correctly apply statistical measurements to your data and therefore to correctly conclude certain assumptions about it. This blog post will introduce you to the different data types you need to know, to do proper exploratory data analysis EDA , which is one of the most underestimated parts of a machine learning project. Table of Contents:. Having a good understanding of the different data types, also called measurement scales, is a crucial prerequisite for doing Exploratory Data Analysis EDA , since you can use certain statistical measurements only for specific data types. You also need to know which data type you are dealing with to choose the right visualization method. Think of data types as a way to categorize different types of variables.

The purpose of this article is to provide a basic understanding of the statistical methods for conducting effective data analysis. Quantitative research involves the collection and analysis of different types of variable in the form of raw data, which needs to be cleaned before starting the data analysis. A biostatistician must be involved from the planning stages of the research process to ensure the validity of the sampling process and the collected data. The statistical analysis includes descriptive analysis for summarizing the data and inferential statistics for comparing between the subgroups to determine a statistically significant association. The relevant statistical tests must be applied and the results appropriately reported using P -values and confidence intervals. The possibility of type I and type II errors should be considered during the final interpretation of the results as well as the clinical significance of the results even if the P -values are found to be statistically significant. Users Online:

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