Data Presentation and Interpretation
Data Presentation and Interpretation
Data Presentation
- Understanding the need for summarising raw data using graphical techniques such as Histogram, Cumulative Frequency Curves, Box-Whisker plots, Stem Leaf plots provides distinct visual representation.
- Recognising the various types of charts and graphs; Line graph, Bar graph, Pie chart, Scatterplot, to represent data visually depending on the set of data and information you wish to illustrate.
- Applying cumulative frequency table to sketch a cumulative frequency graph and gain insights such as the median, percentiles, and the spread of the data.
- Creating frequency polygons and understanding their effectiveness in comparing two sets of data.
- Interpreting, and drawing valid inferences from statistical graphs/charts to make informed decisions.
Data Interpretation
- Calculating and understanding statistical measurements like - mean, mode, and median or central tendency measures to determine a ‘typical’ data value.
- Measuring spread of data using range, interquartile range, or standard deviation. The wider the spread, the more varied the data.
- Understanding how outliers can affect the data set and measures such as mean and standard deviation.
- Applying the concept of correlation and determining the correlation coefficient ‘r’ quantitatively with the help of a calculator or estimated by eye.
- Recognising the application of linear regression analysis to find the ‘line of best fit’ which allows us to make predictions based on the data.
- Interpreting the equation of the line of best fit in a way that makes sense in the real world; using it to make predictions about the data.
Handling Bivariate Data
- Understanding the importance of scatter diagrams in visualising bivariate data.
- Learning how to use and interpret from a scatter diagram and recognising positive, negative, or no correlation.
- Using the line of best fit on a scatter plot to make predictions about data.
- Identifying outliers in bivariate data and making informed decisions about whether to include them in analysis.
- Implementing Spearman’s Rank Correlation Coefficient and understanding its use in testing the strength of association between two ranked variables.
Remember, data interpretation and presentation is immensely valuable for decision making in all sectors, including business, science, health care, government and many more. It is key to acquire sound knowledge and skills in this area for anyone planning to pursue a career in any field involving mathematics.