Para resolver este ejercicio, nosotros hemos utilizado el programa Síagro y cargado un archivo de datos (ficticio, por razones de confidencialidad) con la siguiente información.
Let’s imagine that in June 2019 there was a uterine prolapse of a sow after farrowing and our management program informs us that this is the first prolapse of this year and that the average for the last four years is 0.29 monthly prolapses. Is the appearance of this event important or not? If we want to see it graphically, we can generate a time series chart.
To solve this exercise, we have used the Síagro program and loaded a data file (fictitious, for confidentiality reasons) with the following information.
Prior to starting the analysis, we are going to explain the database. As we can see, the variables are counts and, moreover, they are counts of exceptional or occasional events that are normally independent of each other. The opportunity area of the event is the same among the time periods. Therefore, we will make a C chart.
This file contains data on the appearance of uterine prolapses from a sow farm taken monthly for 60 months. It has three variables:
- Date: start date of each month.
- Prolapse: total number of prolapses that occurred during that month.
If we access to EDA / Sequential in Síagro and select the variable “prolapse” from the database, we will obtain the following graph:
If we access SPC Analysis and select the study variable, the option of a C chart will automatically appear:
The graph informs us that the average appearance of prolapses in this time studied is 0.4 prolapses per month (center) and that the upper control limit is 2.29 prolapses per month (UCL), so, now, we can make the decision not to carry out any update. The calculation of the upper control limit is carried out automatically by Síagro following the statistical formula.