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Descriptive statistics is used to analyze and represent the data that have been previously collected. It includes frequency counts, ranges (high and low scores or values), means, modes, median scores, and standard deviations. Two important concepts to understand descriptive statistics are: Variables and Distribution.
It is a statistic or a measure that describes the data such as the average salary of employees. It is a describing data with tables and graphs which is quantitative or categorical variables. In terms of numerical descriptions, it has two terms center and variability.
The center gives some example measures of center of the data and variability gives some example measures of variability of the data. Now it is given the bivariate descriptions, the dependency measure is considered under correlation.
The main focus of this course is statistical analysis, which involves both descriptive statistics and inferential statistics. The major concern of descriptive statistics is to present information in a convenient, usable, and understandable form.
The main focus of this course is statistical analysis, which involves both descriptive statistics and inferential statistics. The major concern of descriptive statistics is to present information in a convenient, usable, and understandable form.
Descriptive statistics is used to describe a set of data in terms of its frequency of occurrence, its central tendency, and its dispersion. Descriptive statistics collects, summarizes and describes data. The Inferential statistics deals with drawing conclusions and/or making decisions concerning a population based only on sample data.
The major courses in Descriptive analytics include descriptive statistics, data visualization, random variables, probability theories, probability distributions, parameter estimation, regression methods, Hypothesis Testing I, t-test, confidence interval, Hypothesis testing II, ANOVA, Hypothesis testing III, Bootstrapping, cross validation and permutation tests etc.
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