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  • Paired test; involves a type of location kind of a test that is used to compare measurements involving two criteria in order to assess whether the population of their mean differ—one of the most simple paired tests occurring when subjects are measured before and after treatment.
  • ANOVA assumptions: this assumes that the distribution of data is typical and also that homogeneity of variance. It states that variation among various groups should be nearly equal.
  • ANOVA test: involves ways of finding out if the results of an experiment or survey are meaningful. It helps you either reject or appreciate the null hypothesis or accept the so-called alternate hypothesis.
  • Linear regression: it involves a modeling kind of approach to the relationship between dependent variables or scalar response and one or even more independent variables.
  • Cluster analysis: only it a task that involves grouping a set of given objects in a way that the purpose that is in the same group is more similar.
  • Linear regression assumptions: there are four assumptions in linear regression.
  • 1. Linearity: involves the relationship between X and the mean of y

    2. Homoscedasticity: in this case, the residual of the variance is the same as any value of X

    3. Independence: the observations made are independent of each other.

    4. Normality: states that for any value of X that is fixed, Y should be normally distributed.

  • Hypothesis testing in statistics: it involves an act where an analyst tests a given assumption in regards to a specific population parameter. The methods play a significant role in helping statisticians in working out statistical data.

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