Course outline

Aim

• To build confidence and understanding of mathematical ideas.
• To enable the student to interpret and solve mathematical problems.
• To improve examination technique and to make the student aware of common examination errors.
• To make the student fully aware of the content and emphasis of the specification.
• To strengthen revision methods.

Key Topics Covered

• Mathematical models in Statistics
  • The basic ideas of mathematical modelling as applied in probability and statistics.

• Representation and summary of data
  • Histograms, stem and leaf diagrams and box plots.
  • Measures of location: Mean; median; mode.
  • Measures of dispersion: Variance; standard deviation; range; interquartile and interpercentile ranges (including use of simple interpolation).
  • Skewness.
  • The concept of outliers.

• Probability
  • Elementary probability.
  • Sample space. Complementary events.
  • Conditional probability.
  • Independent and mutually exclusive events.
  • Sum and product laws.

• Correlation and Regression
  • Scatter diagrams.
  • Explanatory and response variables.
  • Linear regression.
  • The Product Moment Correlation Coefficient (PMCC).

• Discrete random variables
  • The probability function.
  • The cumulative distribution function.
  • Mean and variance.
  • The discrete uniform distribution.

• Normal distribution
  • Including the mean, variance and use of tables of the cumulative distribution function.