This is the curriculum mapping page for statistics topics. The topics are grouped according to a summary of the Edexcel schemes.

Each module contains a brief description of the content followed by subsections with more detailed accompanying headings, so it should be easy to translate this into other schemes of work.

Problems or articles are variously suggested for the overall module, topic, topic subsection or detailed objective. However, with these statistics modules it can be less clear when and where a problem, article or activity will be most suitable for use. We have put material in a suggested place, but recommend that you consider the material in the context of a whole module when determining the most appropriate point of use. Also, the material often draws together strands from statistics and probability as a whole, so certain aspects of certain problems might be of use at different points in the curriculum. Thus, if searching for enrichment material, you might wish to look for material in modules outside those of primary interest.

Finally, please note that this material is under development. New problems will arrive on a regular basis and it is intended that detailed teachers' notes will eventually accompany all of the problems.

Please also see stemNRICH for problems linked directly into science, many of which have statistical relevance.

STATISTICS S1 Mathematical models in probability and statistics; representation and summary of data; probability; correlation and regression; discrete random variables; discrete distributions; the Normal distribution.	Odd one out , The Monte-Carlo method , Stats Statements
MATHEMATICAL MODELS IN PROBABILITY AND STATISTICS	.
The basic ideas of mathematical modelling as applied in probability and statistics.	Epidemic Modelling
REPRESENTATION AND SUMMARY OF DATA	.
Histograms, stem and leaf diagrams, box plots. Using histograms, stem and leaf diagrams and box plots to compare distributions.	Data matching
Measures of location, mean, median, mode	.
Measures of dispersion, variance, standard deviation, range and interpercentile ranges.	.
Skewness. Concepts of outliers	.
PROBABILITY	.
Elementary probability.	Snooker frames
Sample space. Exclusive and complementary events.	Teams
Conditional probability.	Rain or shine , Put Out, Knock-Out
Independence of two events	.
Sum and product laws	FA cup , Limiting probabilities
CORRELATION AND REGRESSION	.
Scatter diagrams. Linear regression	.
Explanatory (independent) and response (dependent) variables	.
The product moment correlation coefficient, its use, interpretation and limitations.	.
DISCRETE RANDOM VARIABLES	.
The concept of a discrete random variable.	Distribution maker
The probability function and the cumulative distribution function for a discrete random variable.	.
Mean and variance of a discrete random variable.	Random Inequalities
The discrete uniform distribution.	.
THE NORMAL DISTRIBUTION	.
The Normal distribution including the mean, variance and use of tables of the cumulative distribution function	Into the normal distribution

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STATISTICS S2 The Binomial and Poisson distributions; continuous random variables; continuous distributions; samples; hypothesis tests.	Lion hunting
THE BINOMIAL AND POISSON DISTRIBUTIONS	.
The binomial and Poisson distributions.	Overbooking
The mean and variance of the binomial and Poisson distributions.	.
The use of the Poisson distribution as an approximation to the binomial distribution.	.
CONTINUOUS RANDOM VARIABLES	.
The concept of a continuous random variable.	.
The probability density function and the cumulative distribution function for a continuous random variable	pdf matcher , Circle PDF , Scale invariance , Normal intersection
Relationship between density and distribution functions.	Into the exponential distribution , PCDF
Mean and variance of continuous random variables.	Whats your mean?
Mode, median and quartiles of continuous random variables.	.
CONTINUOUS DISTRIBUTIONS	.
The continuous uniform (rectangular) distribution	.
Use of the Normal distribution as an approximation to the binomial distribution and the Poisson distribution (with continuity correction)	.
HYPOTHESIS TESTS	Understanding hypotheses
Population, census and sample. Sampling unit, sampling frame	Very old man
Concepts of a statistic and its sampling distribution.	.
Concept and interpretation of a hypothesis test. Null and alternative hypotheses.	.
Critical region	.
One-tailed and two-tailed tests.	.
Hypothesis tests for the parameter p of a binomial distribution and for the mean of a Poisson distribution	.
Experimental design	Reaction timer timer

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STATISTICS S3 Combinations of random variables; sampling; estimation, confidence intervals and tests; goodness of fit and contingency tables; regression and correlation	The Monte-Carlo method
COMBINATIONS OF RANDOM VARIABLES	.
Distribution of linear combinations of independent random variables.	Uniform sum? , Aim high , Time to evolve 2
SAMPLING	.
Methods for collecting data. Simple random sampling.	.
Use of random numbers for sampling.	.
Other methods of sampling: stratified, systematic, quota.	.
ESTIMATION, CONFIDENCE INTERVALS AND TESTS	.
Concepts of standard error, estimator, bias.	.
The distribution of the sample mean.	.
Concept of a confidence interval and its interpretation.	.
Confidence limits for a Normal mean, with variance known.	.
Hypothesis tests for the mean of a Normal distribution with variance known.	.
Use of Central Limit theorem to extend hypothesis tests and confidence intervals to samples from non-Normal distributions.	.
Use of large sample results to extend to the case in which the variance is unknown	.
Use of large sample results to extend to the case in which the population variances are unknown.	.
GOODNESS OF FIT AND CONTINGENCY TABLES	.
The null and alternative hypotheses.	.
Chi-squared test	Chi-squared faker
REGRESSION AND CORRELATION	.
Spearman's rank correlation coefficient, its use, interpretation and limitations.	.
Testing the hypothesis that a correlation is zero.	.

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STATISTICS S4 Quality of tests and estimators; one-sample procedures; two-sample procedures.	.
QUALITY OF TESTS AND ESTIMATORS	.
Type I and Type II errors.	.
Size and Power of Test.	.
The power test.	.
Assessment of the quality of estimators	.
ONE-SAMPLE PROCEDURES	.
Hypothesis test and confi dence interval for the mean of a Normal distribution with unknown variance.	.
Hypothesis test and confi dence interval for the variance of a Normal distribution.	.
TWO-SAMPLE PROCEDURES	.
Hypothesis test that two independent random samples are from Normal populations with equal variances.	.
Use of the pooled estimate of variance	.
Hypothesis test and confidence interval for the difference between two means from independent	.
Normal distributions when the variances are equal but unknown	.
Paired t-test.	.
4) (MEI) GENERATING FUNCTIONS	Spinners