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.

Tutorial

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

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

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

Step by step guidance for using iNZight.

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Source: Auckland University