We are receiving a lot of questions about relevant analyses in the Analyse-it Method Validation edition to help in evaluating new diagnostic tests in the fight against COVID-19. Below are some quick links that will help, but contact us if you have questions - we are working as normal.

CLSI EP12-A2 for diagnostic accuracy see Sensitivity/Specificity, Estimating the sensitivity and specificity of a binary test and Comparing the accuracy of two binary diagnostic test.
CLSI EP17-A2 for detection limits of molecular methods using Probit analysis, see: Estimating the detection limit using a Probit fit.

EP12-A2 example sensitivity specificity of 2 diagnostic tests

EP17-A2 example Probit detection limit

Plotting bivariate data

The scatter plot is a fundamental tool for looking at bivariate data. It shows the important characteristics of the data and can be used to decide what model may describe the relationship between the variables.

Open the file tutorials\TV Ad Yield.xlsx.
Click a cell in the dataset.
On the Analyse-it ribbon tab, in the Dataset group, click Dataset.
The dataset task pane is displayed.
Select Labels in first column.
Click Apply.
The dataset task pane is closed.
On the Analyse-it ribbon tab, in the Statistical Analyses group, click Fit Model drop-down list, and then click Scatter.
The analysis task pane opens.A scatter plot is added to the analysis task pane.
In the Y variable list, select Retained impressions per week.
In the X variable list, select Budget.
Click Calculate.
The results are calculated and the analysis report opens.

The scatter plot suggests the relationship between advertising yield and spend is not a straight line. It appears the yield increases sharply at lower spend and levels out as spending increases.

Next topic: Fitting a simple regression model