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AustraliaVIC
VCE 12 Methods 2023

9.04 General applications

Interactive practice questions

For the average individual, completing their personal income tax return can take between $1$1 and $6$6 hours.

a

The time taken to complete the tax return, where $t$t is the time in hours, can be modelled by the probability density function:

$f\left(t\right)$f(t) $=$= $k\left(t-1\right)\left(6-t\right)\left(t+2\right)$k(t1)(6t)(t+2) if $1\le t\le6$1t6
$0$0 for all other values of $t$t

Calculate the value of $k$k, using the capabilities of your CAS calculator.

b

Calculate the probability, $p$p, that it takes someone exactly $2$2 hours to complete their tax return.

c

Using the capabilities of your CAS calculator, calculate the probability, $q$q, that it takes someone between $2$2 and $3.5$3.5 hours to complete their tax return. Give your answer correct to two decimal places.

d

Let $T$T be the continuous random variable representing the time it takes for an individual to complete their personal income tax return. Use calculus to calculate the expected value of $T$T to two decimal places.

Medium
9min

The time $t$t, in hours, that an owl spends hunting each night can be modelled by a continuous random variable with probability density function given below.

Medium
4min

The amount of coffee used by a café each week is modelled by a continuous random variable $X$X with a mean of $14.5$14.5 kg and a standard deviation of $1.5$1.5 kg. If the coffee costs $C=24X+15$C=24X+15 in dollars (due to cost per kg and weekly delivery fee), find:

Easy
2min

A written French examination is worth a total of $180$180 marks. The results of the examination can be modelled by a continuous random variable $X$X where the expected value $E\left(X\right)$E(X) is $117$117 and the variance $V\left(X\right)$V(X) is $15$15.

Medium
4min
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Outcomes

U34.AoS4.11

apply probability distributions to modelling and solving related problems

U34.AoS4.3

continuous random variables: - construction of probability density functions from non-negative functions of a real variable - specification of probability distributions for continuous random variables using probability density functions - calculation and interpretation of mean, 𝜇, variance, 𝜎^2, and standard deviation of a continuous random variable and their use - standard normal distribution, N(0, 1), and transformed normal distributions, N(𝜇, 𝜎^2), as examples of a probability distribution for a continuous random variable - effect of variation in the value(s) of defining parameters on the graph of a given probability density function for a continuous random variable - calculation of probabilities for intervals defined in terms of a random variable, including conditional probability (the cumulative distribution function may be used but is not required)

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