Diploma in AP Statistics

This free online course examines the use of probability in data exploration, as well as dataset characterizations.

Publisher: ADU
This free online course covers anticipating patterns involving the exploration of random phenomena using probability and simulation. The course also describes data exploration as a statistical concept in data analysis where data analysts use data visualization and statistical techniques to describe dataset characterizations. By the end of this course, you become ready to apply statistical reasoning in various fields of learning.
Diploma in AP Statistics
  • Duration

    10-15 Hours
  • Students

  • Accreditation






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The Diploma in AP Statistics course introduces learners to the major concepts and tools for collecting, analyzing, and drawing conclusions from data. Do you want to learn the skills needed in exploring data and making statistical inference? This course takes the learners through the technology used in investigating, problem-solving and writing to build conceptual understanding. The Diploma in AP Statistics course is an excellent option for learners who possess adequate mathematical knowledge and quantitative reasoning ability. In this course, you will explore statistics through discussion and activities. You will be able to design surveys and experiments. You will study how to select the best methods for collecting or analyzing data. Patterns and trends are pretty common in statistics. Having the ability to describe patterns, trends, associations, and relationships in data is very useful in becoming a top-notch statistician. The course will take you through how to use probability and simulation to describe probability distributions and define uncertainty in statistical inference. You will get to know about statistical reasoning can be used in drawing appropriate conclusions and justifying claims.

In addition, there are certain concepts that serve as the foundation of statistics. By taking this course, you will be introduced to data in real-world contexts. Variability in data suggests certain conclusions about the data distribution, however, not all variation is meaningful. In this course, you will understand how statistics allows us to develop shared understandings of uncertainty and variation. Learners will be able to define and represent categorical and quantitative variables. You will describe and compare distributions of one-variable data and interpret statistical calculations to assess claims about individual data points or samples. You will also learn how to apply the normal distribution model in statistics. Having access to a world of data is meaningless without the ability to organize and analyze that information. To develop these skills, learners will need multiple opportunities to interact with data presented in different formats. The course describes the patterns and characteristics that are seen in data and then compares the characteristics within the same set of data. Building on what they have learnt, learners will explore relationships in two-variable categorical or quantitative data sets. You will be able to use graphical and numerical methods to investigate an association between two categorical variables. You will be able to describe form, direction, strength, and unusual features for an association between two quantitative variables.

Depending on how data are collected, we may or may not be able to generalize findings or establish evidence of causal relationships. You will learn the important principles of sampling and experimental design in this course. Probabilistic reasoning allows statisticians to quantify the likelihood of random events over the long run and to make statistical inferences. You will be able to discuss simulations and abstract calculations of probability. Learners will be able to build upon their understanding of simulated or empirical data distributions. You will be taken through the fundamental principles of probability used to represent, interpret, and calculate parameters for theoretical probability distributions for discrete random variables. The course describes probabilistic reasoning to sampling, introducing learners to sampling distributions of statistics used in performing inferences. You will get to know how sample statistics can be used to estimate corresponding population parameters, measures of centre (mean) and variability (standard deviation). You will learn how sampling distributions can be determined directly from population parameters. The course further examines by examining the various tools of data visualization, which is one of the two main ways of condensing data sets and creating consumable results. You will be taken through the several advantages of using metrics as a method of data interpretation. Are you preparing for advanced coursework in statistics or other fields using statistical reasoning? Then start this course today.

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