Course review
Review each learning objective in this topic, then open its concept explanation or question set.
Learning objective
• Understand population, sample, random sample, discrete and continuous data. • Assess reliability, bias, missing data, recording errors and outliers. • Use simple random, convenience, systematic, quota and stratified sampling.
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Learning objective
• Use frequency tables, histograms and cumulative frequency graphs. • Find median, quartiles, percentiles, range and IQR. • Produce and compare box-and-whisker diagrams; mark outliers.
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Learning objective
• Use mean, median, mode, modal class and grouped-data mean estimates. • Use IQR, standard deviation and variance; technology may calculate SD/variance. • Understand effects of adding/subtracting or scaling all data values.
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Learning objective
• Use scatter diagrams, best-fit lines and Pearson correlation coefficient r. • Distinguish correlation strength/direction and correlation vs causation. • Use y-on-x regression for prediction; interpret parameters and beware extrapolation.
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Learning objective
• Use trial, outcome, sample space, event and relative frequency. • Calculate P(A)=n(A)/n(U), complements and expected number of occurrences. • Represent sample spaces with lists or tables.
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Learning objective
• Use Venn, tree, sample-space diagrams and outcome tables. • Use combined-event, conditional-probability and independence formulae. • Handle probabilities with and without replacement.
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Learning objective
• Understand discrete random variables and probability distributions. • Find expected value/mean; interpret E(X)=0 as a fair game context.
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Learning objective
• Use binomial distribution as an appropriate model. • Use mean and variance of binomial distribution. • Use technology for binomial probabilities; formal proofs are not required.
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Learning objective
• Use normal distribution curve and its properties. • Use 68-95-99.7 rule around mean and standard deviation. • Use technology for normal and inverse-normal probabilities.
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Learning objective
• Use Spearman's rank correlation coefficient rs with technology. • Average ranks for tied data. • Compare Pearson vs Spearman, including monotonic relationships and outlier sensitivity.
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Learning objective
• Form null/alternative hypotheses, significance levels and p-values. • Use chi-square tests for independence and goodness of fit. • Use t-tests for comparing two population means; interpret test results. • At SL, t-test samples are unpaired and population variance is unknown.
Start with the concept explanation, then practise to create mastery evidence.