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, lines of best fit and Pearson correlation coefficient r. • Distinguish positive/negative/zero and strong/weak/no correlation. • Correlation does not imply causation; use regression line for prediction with caution.
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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 P(A union B)=P(A)+P(B)-P(A intersection B). • Use conditional probability and independence, with/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 equation of regression line of x on y for prediction. • Know prediction direction matters; an x-on-y line is not always reliable for predicting y from x.
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Learning objective
• Use P(A|B)=P(A intersection B)/P(B). • Use P(A intersection B)=P(B)P(A|B). • Test for independence using conditional probability.
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Learning objective
• Standardize normal variables using z-values. • z-value measures number of standard deviations from the mean. • Use z-values to find unknown means and standard deviations.
Start with the concept explanation, then practise to create mastery evidence.