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SQA Higher Applications of Mathematics

Higher · SQAApplications of Mathematics38 ghi chú trong 6 thư mục, 235 KB

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Notes for SQA Higher Applications of Mathematics (course specification version 2.0), in folders for the four content areas in the specification's order: Mathematical modelling; Statistics and probability; Finance; Planning and decision making. Each note has definitions, methods and worked examples, including the spreadsheet and statistical software skills the course uses. Delete any note you do not need once the notes are yours.

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Có gì bên trong

  • Mathematical modelling
    • Building a mathematical model6 KB
    • Linear, quadratic and exponential relationships6 KB
    • Recurrence relations4 KB
    • Choosing between models and improving them6 KB
    • Units and consistency5 KB
    • Error, tolerance and limits7 KB
    • Accuracy, precision and reading claims critically6 KB
    • Spreadsheets: presenting calculations clearly6 KB
    • Spreadsheets: functions, data and charts6 KB
  • Statistics and probability
    • Probability and data
      • Probability and independent events5 KB
      • Venn diagrams and tree diagrams6 KB
      • Types of data, populations and samples6 KB
      • Bias, outliers and data quality6 KB
      • Frequency tables, stem-and-leaf diagrams, bar and pie charts6 KB
      • Histograms and box plots6 KB
      • Contingency tables and misleading graphs6 KB
      • The shape of a distribution5 KB
      • Mean, standard deviation, median and interquartile range5 KB
    • Correlation, regression and testing
      • Scatter plots and correlation7 KB
      • Simple linear regression6 KB
      • Hypothesis testing6 KB
      • Confidence intervals6 KB
      • t-tests and paired t-tests6 KB
      • z-tests for two proportions5 KB
      • Errors, confounding variables and interpreting results7 KB
  • Finance
    • Effective rates of interest7 KB
    • Future and present value of a single payment6 KB
    • Series of payments7 KB
    • Changing, irregular and deferred payments7 KB
    • Loans and credit cards9 KB
    • Savings products7 KB
    • Insurance6 KB
    • Income and taxation6 KB
    • Inflation, purchasing power and financial strategies7 KB
  • Planning and decision making
    • Activity networks6 KB
    • Early and late times, float and the critical path7 KB
    • Gantt charts6 KB
    • Expected value and risk7 KB

Ghi chú đầu tiên

Mathematical modelling / Building a mathematical model

A mathematical model is a description of a real situation in mathematical terms: variables, relationships between them, and the assumptions that make the relationships usable. It is always a simplification, so the useful question is whether it is good enough for the purpose, not whether it is exactly true.

The modelling process

Modelling moves round a loop rather than along a line.

  1. Decide what the model is for, and state the question it has to answer.
  2. Identify the quantities involved and choose the variables.
  3. State the assumptions that simplify the situation.
  4. Choose a form for the relationship and write it as a formula, a table, a graph or a chart.
  5. Use the model to calculate or predict.
  6. Compare the output with what happens in reality or with data, and judge whether it is plausible.
  7. Change the assumptions or the form if the output is not good enough, and go round again.

Variables and constants

A variable is a quantity that changes. The independent variable is the one chosen freely or the one that drives the change, often time or distance, and the dependent variable is the one whose value is worked out from it. A quantity that stays fixed in the situation is a constant or parameter, such as a fixed booking fee or a growth rate that is assumed not to change.

Suppose a taxi company charges a fixed £3.50 plus £1.80 for every mile. The independent variable is the distance dd in miles, the dependent variable is the fare CC in pounds, and £3.50 and £1.80 per mile are parameters. Choosing variables means deciding which way the dependence runs: the fare depends on the distance, not the other way round.

Some situations need more thought than that. For the height of a ball thrown upwards, suitable variables are the height above the ground and the time since it was thrown. For the number of customers in a café during the day, they are the number of customers and the time of day. Stating units when the variables are chosen (metres, seconds, pounds per month) prevents mistakes later, and the units form part of the model.

Forms of a model

The same relationship can be written in several ways, and each suits a different use.

FormWhat it does well
FormulaGives exact values for any input, and can be typed into a calculator or spreadsheet
TableShows specific values side by side, and is how data usually arrives
GraphShows the shape at a glance: steady, speeding up, slowing down, turning round
ChartCompares categories, or shows how a total is split

A model for a situation in words often starts as a sentence. "The balance grows by 2% each month and £50 is added" describes a recurrence relationship. "The depth increases by 3 cm every minute" describes a linear one. Recognising which description goes with which form is the first skill, and the next two notes on relationships give the shapes to look for.

Assumptions

Every model leaves things out, and stating what was left out is part of the model. Typical assumptions are that a rate stays constant, that there are no other influences, that the quantities are measured exactly, and that the model is only used within a sensible range of the independent variable.

For the taxi fare, the assumptions are that the charge is exactly proportional to the distance after the booking fee, that there is no extra charge for waiting in traffic, and that nobody is charged for a part-mile differently. If a passenger reports a fare that does not match the formula, one of these assumptions is the place to look. The assumptions that fail tell you how to improve a model, which is covered further under choosing between models.

Estimating with a model

Some questions have no data and need an estimate built from assumptions you choose and state. The method is to break the quantity into parts that can each be estimated sensibly, multiply them, and round at every stage so that the working stays honest about how rough it is.

Estimate how many sandwiches a school canteen sells in a year.

  1. Assume 1,200 pupils.
  2. Assume about one in four buys a sandwich on a school day, so 1200×0.25=3001200 \times 0.25 = 300 sandwiches a day.
  3. Assume 190 school days in a year.
  4. Then 300×190=57 000300 \times 190 = 57\,000 sandwiches.

The answer is given as "about 57,000", or better "roughly 60,000", because the inputs were only accurate to one or two figures. Changing an assumption changes the answer in a predictable way: if only one pupil in five buys a sandwich, the estimate falls to 1200×0.2×190=45 6001200 \times 0.2 \times 190 = 45\,600. Showing how the result depends on the assumptions is what makes an estimate useful, because a reader can substitute their own figures.

Good estimates also check themselves for reasonableness. If the same canteen served 57,000 sandwiches and had 1,200 pupils, that is about 48 per pupil per year, which is plausible for a fifth of a school day each, and that agreement is some evidence the estimate is sensible.

Checking a model against the situation

Once a model gives an answer, three checks apply.

The answer should have the right units and size. A taxi fare of £0.03 for a ten-mile trip signals a slip somewhere. The behaviour should match what is known, for example a depth that increases should not be predicted to go negative. And the model should be used only over the range where the assumptions hold: a constant-rate model of a filling container stops being valid once the container is full.

If a check fails, the model is revised by altering an assumption or choosing a different form, for example replacing a constant rate by one that changes over time. The modelling loop then runs again from that step.

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