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Anticipating Patterns and Statistical Inferences in Statistics

This free online course describes the concepts used for collecting, analyzing, and drawing conclusions from data.

Publisher: ADU
This free online course covers anticipating patterns involving the exploration of random phenomena using probability and simulation. The course includes details on statistical Inference used in estimating population parameters and hypotheses. By the end of this course, you will be able to use probability for anticipating the distribution of data under a given model. You will also be able to select appropriate models for data distribution
Anticipating Patterns and Statistical Inferences in Statistics
  • Duration

    3-4 Hours
  • Students

  • Accreditation


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This free online course begins by exploring the concept of statistics and its relationship with probability. The course takes a look at how data accuracy is enhanced by the use of means and other statistical objects. You will study how statistical significance is stated with some level of confidence. You will be introduced to the relevance of discrete random variables and probability distribution. You will also be able to explain how random variables, in contrast to other variables in mathematics, can take different values in a range with an associated probability. Do you know that discrete variables can be characterized? This course analyzes how discrete random variables take on finite or infinite sets of discrete values. The course discusses Normal and Gaussian distribution and their occurrence in continuous distribution. You will learn about the parameters used in normal distribution.  The properties of normal distribution and how tables are used in normal distribution will also be highlighted.

The course goes on to explain sampling distribution and its relationship with the probability distribution of statistical data. The course describes the differences in data from the same population. You will study the specific characteristics of sampling distributions. The Central Limit Theorem and Law of Large Numbers are regarded as fundamental theorems of statistics. The course explains the role of the central limit theorem in ensuring the validation of statistical procedures. The course also explains how sampling distribution is used in determining the difference between two independent sample proportions. You will go through experiments on sampling distributions on two independent sample means. It is important to note that t-distribution plays an important role in various statistical analyses, including linear regression analysis. The course suggests learner's tools that can be used to calculate the cumulative distribution function for various degrees of freedom. The course also analyzes chi-square distribution and how it is used to test the goodness of fit of a data distribution. It is also used for estimating confidences surrounding variance and standard deviation for a random variable from a normal distribution.

Furthermore, you will be taken through estimation as an essential concept in statistics. You will learn how estimation is determined through measured and observed data. It is important to learn about the parameters used in calculating errors and stating the level of confidence on data based on probability. You will learn how choices are made to arrive at confidence levels. You will also be able to describe statistical inference and how it answers specific questions about an unknown population. The course explains how a significance test is used as a formal procedure for comparing observed data. You will be shown the parameters used in calculating large samples' confidence interval of proportion. Parameters used in large sample confidence intervals will also be highlighted. You will learn how to test a hypothesis based on the size of a population with an unknown mean. You will also conduct mathematical experiments on how to test for the difference between two means. Start this course today and get insights into the major concepts and tools used for collecting, analyzing, and drawing conclusions from data.

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