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Antidifferentiation
Application of integration
Bernoulli distributions
Binomial distributions
Calculus of exponential function
Calculus of logarithmic functions
Calculus of trigonometric functions
Confidence intervals for proportions
Cosine and sine rules
Differentiation rules
Functions and graphing
Fundamental theorem of calculus and definite integrals
General continuous random variables
Logarithmic laws and functions
Normal distribution
Random sampling
Sample proportions
The second derivative and application of differentiation
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19 Videos & Notes
Trapezoidal rule
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Trapezoidal rule
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Introduction and its use to find area under curves
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Features of log graphs
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Functions and graphing
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recognise the qualitative features of the log graphs
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Calculus of Trigonometric Functions
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Calculus of trigonometric functions
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Calculus of Trigonometric Functions: Using visual intuition
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Second Derivative
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The second derivative and application of differentiation
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The concept of the second derivative as the rate of change of the first derivative function
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Sample Proportion
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Sample proportions
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understand the concept of the sample proportion p^ as a random variable whose value varies between samples
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Random Sample
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Random sampling
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Understand the concept of a random sample.
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Logarithmic Laws
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Logarithmic laws and functions
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Establish and use log laws
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The mean of a continuous random variable
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General continuous random variables
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Understand and calculate the mean of a continuous random variable
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Applications of Product, Quotient and Chain Rules
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Differentiation rules
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explore how to apply the product, quotient and chain rules to differentiate functions
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Introduction to the normal distribution
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Normal distribution
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Introduction using examples
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Properties of definite integrals
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Fundamental theorem of calculus and definite integrals
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Coverage of all properites of definite integrals
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Sine Rule, Cosine Rule and Area of Triangle Formulas
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Cosine and sine rules
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establish and use the sine (ambiguous case is required) and cosine rules and the formula for the area of a triangle.
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Approximate Confidence Interval
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Confidence intervals for proportions
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use the approximate confidence interval, as an interval estimate for pp, where zz is the appropriate quantile for the standard normal distribution.
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Differentaition of logarithms
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Calculus of logarithmic functions
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Explanantions and examples
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Considering derivatives visually
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Calculus of exponential function
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Use of Desmos to show exponential functions and their derivatives
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Language of ‘at most’ and ‘at least’ in Binomial Distribution Problems
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Binomial distributions
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Identify contexts suitable to model binomial distributions and associated probabilities to solve practical problems, including the language of ‘at most’ and ‘at least’
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Mean and Variance of Bernoulli Distribution
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Bernoulli distributions
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recognise and determine the mean p and variance p(1−p) of the Bernoulli distribution with parameter P
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Why integration means area
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Application of integration
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Simple, logical explanation
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Integration rules
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Antidifferentiation
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Summary with examples
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