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IB Maths: Analysis & Approaches: Statistics and probability

Data, regression, probability, binomial and normal distributions. 14 subtopics.

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In this topic: 4.1 · 4.2 · 4.3 · 4.4 · 4.5 · 4.6 · 4.7 · 4.8 · 4.9 · 4.10 · 4.11 · 4.12 · 4.13 · 4.14

4.1Sampling and data

Population: everyone of interest. Sample: the part you actually measure.

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4.2Presenting data

Frequency tables and histograms (equal class widths at SL).

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4.3Measures of central tendency and dispersion

Centre: mean, median, mode (modal class for grouped data). Spread: range, IQR, standard deviation \(\sigma\) (use your GDC).

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4.4Correlation and regression

Pearson's \(r\) lies between \(-1\) and \(1\). Values close to \(\pm1\) show strong linear correlation.

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4.5Introduction to probability

Relative frequency estimates probability from experiments.

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4.6Combined events, Venn and tree diagrams

Use Venn diagrams, tree diagrams and sample space diagrams.

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4.7Discrete random variables

A game is fair if the expected gain is 0.

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4.8The binomial distribution

Conditions: fixed number of trials, two outcomes, constant probability, independent trials.

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4.9The normal distribution

Bell-shaped and symmetric about \(\mu\); mean = median = mode.

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4.10Regression line of x on y

To predict \(x\) from a given value of \(y\), use the regression line of \(x\) on \(y\).

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4.11Conditional probability and independence

P(A\mid B)=\frac{P(A\cap B)}{P(B)}\qquad A,B\text{ independent}\iff P(A\mid B)=P(A)

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4.12Standardisation of normal variables

\(z\) tells you how many standard deviations a value is from the mean.

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4.13Bayes' theorem HL

A tree diagram makes Bayes questions much easier.

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4.14Variance of discrete and continuous random variables HL

The mode of a continuous random variable is where \(f(x)\) is greatest.

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