Quality Output tools allow users to easily generate project project outcomes, models and reports.
These tools allow users to:
- Carry out statistical analysis of project outcomes or process data
- Calculate ROIC pre- or post-project
- Generate project reports using pre-completed work over the duration of a project
- Set and review hypotheses around projects
Our Statistical Analysis tool includes powerful statistical tests used within Statistical Process Control (SPC) methodology. Users can input raw data to calculate:
- Standard deviation: most commonly used in healthcare settings to benchmark data against regional and national indicators. Data can be entered for a number of sources the chart will then calculate the mean (average) and also the standard deviation +1 and -1. In benchmarking terms anything that falls between the +1 and -1 lines is classed as normal. Data that fall out of these indicators is classed as outliers this can either mean the data is over or under performing.
- Pearson’s correlation: a parametric nominal test which looks at two sources of data to see if variables are related and if changes in one are accompanied by changes in the other.
For example, if a ward had 10 beds, would more falls occur if patients were situated further away from a nurses station
- Spearman’s correlation (also known as Spearman’s rho): A non-parametric measure of correlation used in ordinal data sets. It is calculated the same as Pearson’s correlation using ranked data. It assesses how well the relationship between two variables can be described using a monotonic function.
- t-test: calculated either as one- or two-tailed tests.
A one sample t-test is used most commonly used in healthcare to measure quality control and is used in the testing of new drugs.
A two sample test also known a student’s test should only be used if the two samples are equal. If this is not the case, this is then referred to as Welch’s t-test. A practical example use of this type of test is in healthcare to monitor a cancer patient’s tumour size before and after treatment.
Anyone can enter data to use these tests but it is recommended that the user has some degree of statistical knowledge prior to carrying them out to ensure they are being performed correctly.
Return on Investment (capital) (ROIC)
Return on investment (capital) (ROI) is the concept of an investment of some resource yielding a benefit to the investor. A high ROI means the investment gains compare favorably to investment cost.
As a performance measure, ROI is used to evaluate the efficiency of an investment or to compare the efficiency of a number of different investments. In purely economic terms, it is one way of considering profits in relation to capital invested.
In business, the purpose of the “return on investment” (ROI) metric is to measure, per period, rates of return on money invested in an economic entity in order to decide whether or not to undertake an investment.
ROI and related metrics provide a snapshot of profitability, adjusted for the size of the investment assets tied up in the enterprise. ROI is often compared to expected (or required) rates of return on money invested.
Marketing decisions have obvious potential connection to the numerator of ROI (profits), but these same decisions often influence assets usage and capital requirements (for example, receivables and inventories). Marketers should understand the position of their company and the returns expected.
The report will be a template of headings with links to relevant information associated with the project. Each report template is unique to your organisation. In simple terms, “use your corporate report template”.
The tool aims to reduce time wasted on duplicating work, so if you have already completed previous stages using various tools, the information is made available to paste, edit and export in appropriate format alongside any charts and costings information to support the justification.
One of the most important applications of statistics is to use a sample to test an idea or hypothesis you have, regarding a data set. This branch of statistics is known as inferential statistics and the tool used to measure this is the chi square. Many other statistical models can be used depending on the scope and size of the project, however the chi square tends to be the easiest to use prior to project commencement.
The chi square test is also known as the goodness of fit and is a probability model. To give an example of this model when flipping a coin 100 times you can predict the outcome of heads or tails based on the most likely outcome, if you repeat this test.