Formal Statistical Inference: I

Formal Statistical Inference: I

Author: Nazmul Bashar

Students will have introductory concept about the statistical inference.

This is the beginning of about four week course on Formal Statistical inference. Students will be learning about boot-strap sampling, why a sample is required and how to make a sample representative of the population. They will also learn about sampling variability and confidence interval which will introduce the concept of interval estimate.

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Revisit Informal Statistical Inference Level 2

Some of the students learned Informal Statistical Inference in Level 2 (12 MAT2). Also, in year 11 all the students worked on Multi-variate data, where the students were asked to compare two sub-groups. All should be familiar with dot plot and box-plots (drawn side by side) for comparison.

Also , the students were familiar with PPDAC cycle of statistics and learned how to pose a valid statistical question (with the required elements).

The topic we are about to do, is an extension of level 1 and level 2 statistical comparison topics.

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Source: Auckland University

Bootstrap sampling and Confidence Interval (median)

Bootstrapping process is a comparatively new sampling method which uses computer power. It is a resampling process with replacement i.e. say, if you have a population of 700 kiwis and you want to extract a sample of size 30. Kiwi Kaper will be demonstrated in class to illustrate this process. We will use iNZight to illustrate that with animations.

Source: Auckland University

Booklet on Formal Inference : pdf

This booklet contains all the description, explanations about the contents. It also gives comprehensive idea about report writing.

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Source: Jake Wills

INzight Instructions for this topic

Step by step guidance for using iNZight.

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Source: Auckland University