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Level 1
Level 2
Level 3
Level 4
Level 5
1.10 Investigate a given multivariate data set using the statistical enquiry cycle
1.11 Investigate bivariate numerical data using the statistical enquiry cycle
1.12 Demonstrate understanding of chance and data
1.13 Investigate a situation involving elements of chance
2.8 Design a questionnaire
2.9 Use statistical methods to make an inference
2.10 Conduct an experiment to investigate a situation using statistical methods
2.11 Evaluate a statistically based report
2.12 Apply probability methods in solving problems
2.13 Investigate a situation involving elements of chance using a simulation
3.8 Investigate time series data
3.9 Investigate bivariate measurement data
3.10 Use statistical methods to make a formal inference
3.11 Conduct an experiment to investigate a situation using experimental design principles
3.12 Evaluate statistically based reports
3.13 Apply probability concepts in solving problems
3.14 Apply probability distributions in solving problems
Scholarship

Inference
Probability
Time series
Bivariate
Statistical Investigation Process
Statistical literacy
Comparisons
Experiments
iNZight
Data
Sampling variability
Bootstrapping
Box plots
Distribution Shape
Experimental Probability
Experimental design
Probability distributions
Population
Dot Plots
Randomisation test
Prediction
PPDAC
Relationship
Sample
Scatterplots
Regression
Survey
Five Number Summary
Trend
Correlation
Margin of Error
Theoretical Probability
Assessment
Sampling
Simulation
Sample size
Risk
Report writing
Variation
Multivariate
Distributions
Context
Internal assessment
Data Cards
Infographics
Designing survey questions
Confidence Intervals
Data sets
Sampling distributions
Seasonal Effects
Scatterplot
Formal Inference
Conditional probability
Statistics Education
Polls
Mean
Cross curricular
Median
Questionnaires
Careers
Smoothing
Variability
Bar graphs
NZSA
Forecasting
Binomial
Curriculum
Cleaning data
Histograms
True Probability
Independence
Sampling methods
Sampling error
Poisson
Statistical insight
Interpreting displays
Randomised experiments
Posing investigative questions
Spread
Randomness
Probability modelling
Causation
Multivariate tables
Data display
Research
Model estimate
Venn Diagrams
Non-sampling errors
Outlier
Chance
CODAP
two way tables
Informal Confidence Interval
Observational study
Resources
Data science
Census
rule of thumb
Statistical reasoning
Digital technology
coding
Technology
worry questions
Statistics project
Distribution
sample variation
Model
Big data
Shape
Bias
Measurement data
Probability tree
Investigation
Categorical data
Measurement error
Central tendency
Expected values
Triangular
Forecast
Association
Reports
Discrete random variables
Central Limit Theorem
Difference of two means
Modeling
Facts
Descriptive
Year 9
Chance distributions
Lesson plans
Games of chance
Data collection
Quantitative data
Randomisation variation
Experiements
Zoom in
Scholarship
Normal Distribution
Inquiry learning
I notice
sample statistic
Year 10
I wonder...
Representing data
Overview
Causality
Statistical Investigation
Graphical displays
iNZight Lite
visualising data
Learning experiences
Data scrapping
R code
Level 1 NCEA
parameters
FAO Statistical Yearbook
If your not having fun
Poisson distribution
Probability language
primary data
you're not doing it right!
FAQ
Approximations
e
Euler's number
Fibonacci
Model Eliciting Activity
MEA
Follow Up Tasks
SOLO
Noise
FUT
Dollar Street
video
Signal
Concord Consortium
spinner
Pascal's Triangle
te Reo Māori
COVID-19
Pandemics
Interactive data
Online learning
Parameter
Multiple representations
Investigations
Datasets
Gender
Sex
EDC
Physical graphs
Conclusions
Sorting
Classifying
Organising
Vocational pathways
Games
Google classroom
Doctopus
Making a call
Sociolinguistic
Press release
Visual essays
heuristics
Misconceptions
Science
interactive
Scampy
characteristics
conditions
Variation theory
Task design
IQR
Resampling
Interpreting CI
Goobric
collecting data
critical thinking
writing frame
Reliable
Significant
Visualising distributions
Probsim
Assumptions
rule or thumb
Linear
t-test
Uniform
Skew
relative frequency
Relative risk
misleading graphs
Professional development
No Textbook learning
key competencies
Engagement
US
Pachinkogram
Scheme
symmetrical
Plan
Univariate
strategy
Monty Hall problem
dependent
Long run relative frequency
Uncertainty
Data representation
Ethics
residual
Continuous data
Mutually Exclusive
Ratios
Formal report writing
Eikosogram
Classroom discourse
Achievement Objectives
Random error
Random sample
1.10
Standard
Level 1
Sourcing data
Average
Graphs
OSEM
Sphero robots
Literacy
University
Personalised learning
Stem & leaf
Statistical thinking
Level 2 NCEA
Welsh Baccalaureate
Artefacts
Selection bias
NZC
91584
3.12
Experimental units
Treatment
Response
Generalization
CPI
trial
Line of best fit
Linear relationship
box plot
Representative
interquartile range
Unimodal
Bimodal
Shift
data analysis