NCEA L1 Mathematics - business

This NCEA L1 (curriculum level 6) Mathematics course is designed to prepare students for a career in business and commerce. Continues to Mathematics at NCEA L2 and L3 and is an approved UE subject. The course covers;

Numeric reasoning (internal 4 credits, one-period in-class assessment).

Apply numeric reasoning involves: selecting and using a range of methods in solving problems, demonstrating knowledge of number concepts and terms and communicating solutions that would usually require only one or two steps.

Linear algebra (internal 3 credits, one-period in-class assessment).

Apply linear algebra involves: selecting and using a range of methods in solving problems, demonstrating knowledge of algebraic concepts and terms and communicating solutions that would usually require only one or two steps.

Right-angle triangles (internal 3 credits, two-period in-class assessment).

Apply right-angled triangles involves: selecting and using a range of methods in solving measurement problems, demonstrating knowledge of measurement, geometric concepts and terms and communicating solutions that would usually require only one or two steps.

Bivariate data analysis (internal 3 credits, one-week take-home assignment).

Investigating bivariate data involves: determining appropriate variables and measures - considering sources of variation - gathering and cleaning data - using multiple displays, re-categorising data to find patterns and relationships, in multivariate data sets and presenting a report of findings.

Algebraic procedures (external 4 credits, one-period common assessment task) - no calculator allowed.

Apply algebraic procedures involves: selecting and using procedures in solving problems, demonstrating knowledge of algebraic concepts and terms and communicating solutions using appropriate mathematical symbols.

Multivariate data analysis (internal 4 credits, one-week take-home assignment).

Investigating multivariate data involves: determining appropriate variables, cleaning data, using multiple displays, and re-categorising data to find patterns, variations, in multivariate data sets, comparing sample distributions visually, using measures of centre, spread, and proportion and presenting a report of findings.