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NZAMT NZQA NZ Grapher NZ Maths Census at School Study It Khan Academy Desmos

AS 2.9 Make an Inference: Overview

Inference HOME | Achievement Objectives | Overview | Statistical Cycle
- Write an introduction | Using NZgrapher | Discussing sample statistics
- Box plots | Discussing the boxplot & dotplot | Sampling methods | Sample variability and size
- Informal confidence interval | Comparing two populations - discussion
- Writing a report | Revision

1

Overview

Review of Level 1 AS 1.10 Multi variate data

Overview:
Population, One Variable & Two Categories (groups), Sampling, Box plot overview. iNZight introduction

NZ paid work & qualifications data cards
Take a sample of 11 cards
Display the data - making box plots
Collect class box plots together (same variable)

Class notes, Blank notes

Qualifications & Work Data Cards:
Data set csv
Variables | sets of axis
One data card | All data cards
| excel version | Population displays

Populations usually have unknown parameters (unknown mean, median, standard deviation etc) because the population is too large or difficult to measure, or the measurement of items require their destruction or death.

Each time we sample from a population our sample varies, and we get different sample statistics (mean median etc) and sample displays (box plots)

We can use our sample to construct a confidence interval within which we can be reasonably sure the actual population mean (or median) will lie. Even though every sample taken will vary, and every confidence interval will be different overall most (95%) or the confidence intervals will contain the true population mean (or median)

A larger sample results in a narrower confidence interval.

The greater the sample side the better our estimate of the population mean (or median)

 

A confidence interval does NOT correct for poor sampling methods, biased samples, or samples that do not represent the population well or contain errors. If the sample data is poor, the resulting confidence interval will be poor

To compare two populations to determine if the population means are different we construct a confidence interval for each population. If the confidence intervals overlap then we cannot say the population means are different. If the confidence intervals do not overlap then we can be reasonably sure that the population means are different.

 

 

 

Exploring Data Web site

 

 

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