Specification points
OCR Level 3 Certificate in Core Maths B (MEI): Quantitative Problem Solving H869
140 points in 2 topics, with OCR's codes and titles.
Grade boundaries·OCR command words
Codes and titles are read by a script from OCR's published specification and have not been checked by a person. What each point covers is in the specification itself: OCR’s specification.
01 Introduction to Quantitative Reasoning
- 01.1 Use of technology
- IQRq1 Using a calculator
- IQRq2 Reading spreadsheets
- IQRq3 Entering spreadsheet formulae and data
- IQRq4 Interpreting spreadsheet formulae
- IQRq5 Copying spreadsheet formulae
- IQRq6 Numerical solutions with a spreadsheet
- IQRq7 Graphs and statistical diagrams in spreadsheets
- 01.2 Modelling
- IQRp1 Simplifying assumptions for modelling
- IQRp2 Choosing a simple model
- IQRp3 Predictions from a model
- IQRp4 Comparing model outcomes with reality
- IQRp5 Appraising model assumptions
- IQRp6 Improving simple models
- IQRp7 Communicating mathematical results
- IQRe1 Rough estimates
- IQRe2 Estimates to check calculations
- IQRe3 Upper and lower bounds
- IQRe4 Error bounds and tolerances
- IQRa1 Representing situations with formulae
- IQRa2 Substituting into formulae
- IQRa3 Solving equations and rearranging formulae
- IQRm1 Similar shapes: lengths, areas and volumes
- IQRm2 Measures to meet regulations
- IQRm3 Working with units
- IQRm4 Diagrams, maps and scale drawings
- IQRm5 3-D objects in 2-D
- IQRm6 Displacement, speed, velocity and acceleration
- IQRn1 Ratio and proportion in context
- 01.3 Statistics
- IQRs1 Deciding what data to collect
- IQRs2 Collecting data ethically
- IQRs3 Processing and presenting data
- IQRs4 Interpreting results of a statistical question
- IQRs5 Types of data
- IQRs6 Inaccurate values in data
- IQRs7 Reading and constructing tables
- IQRs8 Sample and population
- IQRs9 Inferring population properties from samples
- IQRs10 Variability and features of distributions
- IQRs12 Misleading statistical diagrams
- IQRs13 Skewness
- IQRs14 Scatter diagrams and lines of best fit
- IQRs15 Measures of central tendency
- IQRs16 Measures of spread
- IQRs17 Weighted means
- IQRs18 Normal distribution as a model
- IQRs19 Unusual values beyond three standard deviations
- IQRs20 z-scores
- IQRs21 Normal probability plots
- 01.4 Finance
- IQRf1 Information needed for financial decisions
- IQRf2 Obtaining financial information
- IQRf3 Finding possible financial solutions
- IQRf4 Choosing appropriate financial solutions
- IQRf5 Percentages and index numbers
- IQRf6 Appreciation and depreciation
- IQRf7 Average annual percentage growth rate
- IQRf8 Foreign exchange calculations
- IQRf9 Choosing the best exchange rate
- IQRf10 Regular outgoing costs
- IQRf11 Information from financial tables
- IQRf12 Costing a project with a spreadsheet
- IQRf13 Demand curves
- IQRf14 Language of finance
- 01.5 Working with exponentials
- IQRl1 Large and small numbers in standard form
- IQRl2 Calculating in standard form
- IQRl3 Exponential growth and decay
- IQRl4 Graphs of exponential growth and decay
- IQRl5 Solving power and exponential equations
- IQRl6 Logarithmic scales
- 01.6 Working with graphs and gradients
- IQRg1 Independent variable on the horizontal axis
- IQRg2 Tables of values and plotting graphs
- IQRg3 Tables of values from graphs
- IQRg4 Graphs from different contexts
- IQRg5 Direct and inverse proportion graphs
- IQRg6 Linearising a graph
- IQRg7 Straight-line graphs and formulae
- IQRg8 Gradient of a straight line in context
- IQRg9 Gradient of a curve by tangent
- 01.7 Risk
- IQRu1 Equally likely outcomes
- IQRu2 Estimating probability by counting outcomes
- IQRu3 Relative frequency
- IQRu4 Two-way tables and probability
- IQRu5 Dependent and independent events
- IQRu6 Tree diagrams and conditional probability
- IQRu7 Understanding risk
- IQRu8 Risk assessments
02 Statistical Problem Solving
- 02.1 Problem analysis
- SPSs1 Formulating problems statistically
- SPSs2 Different statistical approaches
- SPSs3 Choosing a standard distribution
- SPSs4 Recognising standard statistical procedures
- SPSs5 Modelling assumptions of distributions
- SPSs6 Inputs a model requires
- SPSs7 Collecting input data for a model
- SPSs8 Sources of variability in data
- SPSh1 Hypothesis testing process and vocabulary
- SPSh2 Large data sets and populations
- 02.2 Data collection
- SPSs9 Sampling methods
- SPSe1 Factual general knowledge
- SPSe2 Estimating everyday quantities without information
- SPSs10 Finding and using information from sources
- SPSs11 Evaluating public statements with numbers
- SPSs12 Evaluating others' statistical work
- 02.3 Process and presentation
- SPSs13 Techniques for processing raw data
- SPSs14 Cleaning data
- SPSs15 Data displays and summary measures
- SPSs16 Checking distributions with data displays
- SPSs17 Standard statistical notation for samples
- SPSs18 Estimating parameters from sample data
- SPSs19 Mean and standard deviation on a calculator
- SPSs20 Datum levels
- SPSs21 Linear transformations of mean and standard deviation
- SPSs22 Substituting inputs into a model
- SPSu1 Normal distribution as a model
- SPSu2 Standardising Normal values
- SPSu3 Estimating population proportions with the Normal
- SPSh3 Chi-squared test for contingency tables
- SPSh4 Interpreting chi-squared test results
- SPSb1 Vocabulary of bivariate data
- SPSb2 Spearman's rank correlation tests
- SPSb3 Pearson's correlation using technology
- SPSs23 Interpreting a solution in the original problem
- SPSs24 Interpreting hypothesis test results in context
- SPSs25 Recognising unreasonable solutions
- SPSs26 Sources of bias or error in results
- SPSs27 When to refine or replace an approach
- SPSs28 Proposing a refined approach
- SPSs29 Good enough solutions
- SPSs30 When more data would help
- SPSs31 Communicating a solution