GCE A-Level H1 Mathematics: Paper 1 practice
Practise paper 1 with the GCE A-Level H1 Mathematics question types and full mark budget. Work through the questions before opening feedback.
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Learning goals
- Interpret a function as one output for each input
- Model and interpret exponential and logarithmic relationships
- Use logarithm laws with domain conditions
- Interpret and draw graphs of functions
- Use quadratic structure and discriminants to reason about roots
- Solve simultaneous equations by justified methods
- Solve quadratic equations by suitable methods
- Solve inequalities and represent their solution sets
- Formulate, use and interpret a mathematical model
- Interpret a derivative as gradient or rate of change
- Differentiate within the permitted function scope
- Differentiate power sums on the stated input interval
- Differentiate exponential compositions on the stated input interval
- Differentiate logarithmic compositions on the stated input interval
- Differentiate mixed function sums on the stated input interval
- Find a complete second derivative on the stated input interval
- Find the complete tangent to a polynomial at a stated input
- Find and classify every stationary point of a polynomial
- Find the greatest polynomial value and every attaining input on a closed interval
- Find the least polynomial value and every attaining input on a closed interval
- Apply the chain rule to composite functions
- Classify stationary points using justified derivative tests
- Find and interpret a tangent to a curve
- Model and solve an optimisation problem
- Find antiderivatives within the permitted scope
- Find complete power antiderivatives on the stated interval
- Find complete logarithmic antiderivatives on the stated interval
- Find complete exponential antiderivatives on the stated interval
- Find complete mixed antiderivatives on the stated interval
- Evaluate a polynomial definite integral with linked exact working
- Evaluate definite integrals
- Relate a definite integral to area under a curve
- Calculate event probabilities from an explicit sample space
- Distinguish independence from mutual exclusivity
- Calculate and interpret conditional probabilities
- Use counting principles, permutations and combinations
- Choose and use a binomial probability model
- Calculate an event probability in a specified binomial model
- Calculate a conditional probability in a specified binomial model
- Calculate the expectation of a specified binomial count
- Calculate the variance of a specified binomial count
- Recover the unique binomial parameters from exact moments
- Find the least trial count satisfying a binomial probability threshold
- Choose and use a normal probability model
- Calculate an event probability in a specified normal model
- Calculate a conditional probability in a specified normal model
- Recover a normal boundary from its specified tail probability
- Recover a normal mean from a probability and known variance
- Recover a normal variance from a probability and known mean
- Recover the unique normal mean and variance from two probabilities
- Calculate a linear combination's expectation from specified moments
- Calculate a linear combination's variance with justified dependence conditions
- Calculate a probability for a specified combination of normal variables
- Reason about sampling and sampling distributions
- Calculate a population-mean estimate from a specified independent sample
- Calculate an unbiased population-variance estimate from a specified independent sample
- Calculate an estimated standard error from a specified independent sample
- Calculate a sample mean's expectation from specified population moments
- Calculate a sample mean's variance from specified population moments
- Calculate a sample mean's standard error from specified population moments
- Calculate a sample-mean probability under a specified exact or approximate normal model
- Calculate a minimum sample size for specified normal-model precision
- Formulate and interpret a justified hypothesis test
- Calculate a signed population-mean test statistic under a stipulated normal-test model
- Calculate a population-mean test p-value for a prespecified alternative
- Calculate all standardised critical boundaries for a planned population-mean test
- Calculate all sample-mean critical boundaries for a planned known-variance normal test
- Determine a population-mean test decision using the unrounded planned p-value and level
- Fit and interpret correlation and regression models
- Calculate product-moment correlation from specified paired data
- Calculate complete ordered intercept and gradient from specified paired data
- Calculate original-response model prediction from specified paired data
- Calculate signed residual on the fitted response scale from specified paired data
- Calculate sum of squared residuals on the fitted response scale from specified paired data
- Calculate original-input solution of a specified fitted calibration model from specified paired data
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Paper instructions
Mock exam
Work through a full exam-style paper. This is exam practice only and doesn’t count towards your course progress.
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- About 180 minutes.
- Feedback appears after you finish and submit.
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