This unit standard is designed to provide credits towards the mathematical literacy requirement of the NQF at Level 4. The essential purpose of the mathematical literacy requirement is that, as the learner progresses with confidence through the levels, the learner will grow in a confident, insightful use of mathematics in the management of the needs of everyday living to become a self-managing person. An understanding of mathematical applications that provides insight into the learner`s present and future occupational experiences `and so develop into a contributing worker. The ability to voice a critical sensitivity to the role of mathematics in a democratic society and so become a participating citizen. People credited with this unit standard can: Critique and use techniques for collecting, organising and representing data. Use theoretical and experimental probability to develop models, make predictions and study problems. Critically interrogate and use probability and statistical models in problem-solving and decision-making in real-world situations.
This course is tailored to meet the mathematical literacy requirements of the NQF at Level 4, aiming to empower learners with the ability to confidently and insightfully apply mathematical principles to everyday life. Participants will develop skills in critiquing and utilising data collection techniques, as well as representing data effectively. The course emphasises the use of theoretical and experimental probability to create models, make predictions, and analyse real-world problems. By the end of this course, learners will be adept at employing statistical and probability models for informed decision-making and problem-solving, thereby becoming self-managing individuals, contributing workers, and active, critically sensitive citizens in a democratic society.
Course Content
- Situations or issues that can be dealt with through statistical methods are identified correctly
- Appropriate methods for collecting, recording, and organising data are used to maximise efficiency and ensure the resolution of a problem or issue
- Data sources and databases are selected in a manner that ensures the representativeness of the sample and the validity of resolutions
- Activities that could result in contamination of data are identified, and explanations are provided for the effects of contaminated data
- Data is gathered using methods appropriate to the data type and purpose for gathering the data
- Data collection methods are used correctly
- Calculations and the use of statistics are correct
- Graphical representations and numerical summaries are consistent with the data, clear, and appropriate to the situation and target audience
- Resolutions for the situation or issue are supported by the data and validated in terms of the context
- Experiments and simulations are chosen and/or designed appropriately in terms of the situation to be modelled
- Predictions are based on validated experimental or theoretical probabilities
- The results of experiments and simulations are interpreted correctly in terms of the real context
- The outcomes of experiments and simulations are communicated clearly
- Statistics generated from the data are interpreted meaningfully and interpretations are justified or critiqued
- Assumptions made in the collection or generation of data and statistics are defined or critiqued appropriately
- Tables, diagrams, charts, and graphs are used or critiqued appropriately in the analysis and representation of data, statistics, and probability values
- Predictions, conclusions, and judgments are made on the basis of valid arguments and supporting data, statistics, and probability models
- Evaluations of the statistics identify potential sources of bias, errors in measurement, potential uses, and misuses and their effects
- Non-accredited: Short course only
- Duration: 2h 00m
- Delivery: Classroom/Online/Blended
- Access Period: 12 Months
