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To understand statistics subject better we can split it into the following 2 major parts:
Descriptive statistics -This aspect of statistics uses descriptive coefficients to summarize a given data set. The coefficients can be a representation of either the entire or sample of the population. Descriptive statistics is used by analysts and researchers to understand the features of specific data.
Inferential Statistics -Inferential statistics are used to make inferences about a larger population from which a sample was drawn. For example, suppose a researcher wants to know the number of female students taking statistics in the UK then it is unfeasible to walk in all academic institutions counting the female students one by one. Instead, he can sample a few universities and colleges and generalize the findings on the entire student population in the UK.
Hypothesis testing is used to answer assumptions regarding a parameter in a population. The nature of the data used determines the methodology used by the analyst. In hypothesis testing, it is either you accept or reject the null hypothesis. The test should reveal whether the primary hypothesis is true or not. The analyst should proceed to formulate a new hypothesis to be tested if the first one is not true.
In statistics, the confidence interval is the probability that a population’s parameter will fall between two value sets for a proportion of times. This standard analysis tool measures the degree of certainty and uncertainty in a sampling method. Confidence Intervals can take a confidence level of either 95% or 99%.
Regression analysis is used to describe the relationship between dependent and independent variables. In this type of analysis, the researcher incorporates hypothesis tests to determine whether the relationships in the data sample can actually be seen in the population.
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