The discussion should lead to a method for using percentages to choose the ‘best estimator’. One approach could be to average the percentage error for the doll’s height estimate and the teacher’s height estimate. You may wish to give a prize to the winner. How accurate? The concept and calculation of error’ is explained in terms ‘percentage ‘student-friendly’ on this webpage: It has been decided to use positive and negative percentage errors to differentiate between over-estimates and under-estimates. If the percentage error’ is going to be ‘mean calculated, all the percentage errors must first be made positive. The Patient Admission Prediction Tool is used to forecast the number of patients who will turn up to a hospital on any given day of the year. For example, on 28 June, the forecast was 206 people but the actual number was 221. The forecast was 15 the actual. This error could be written as . Percentage Data for predicted and actual numbers of admissions in the first 20 days in July are provided. Ask each student, or group of students, to calculate the percentage error for one (or more) days and then write the error for each day on individual sticky-notes. The answers for the first two days are given. Record these on sticky-notes as well. Ask students to arrange the sticky-notes in order of magnitude of the error to answer these questions. Number of days for which the forecast was an under-estimate. Number of days for which the forecast was correct. Number of days for the forecast was an over-estimate. Number of days for the forecast was less than 5% above or below or actual. Number of days for which the forecast was less than 10% above or below or actual. Discussion questions Students may notice that the largest was more than 30%. What is a reasonable error? Will that reasonableness change depending on the context?
The discussion should lead to a method for using percentages to choose the ‘best estimator’. One approach could be to average the percentage error for the doll’s height estimate and the teacher’s height estimate. You may wish to give a prize to the winner. How accurate? The concept and calculation of ‘percentage error’ is explained in ‘student-friendly’ terms on this webpage: It has been decided to use positive and negative percentage errors to differentiate between over-estimates and under-estimates. If the ‘mean percentage error’ is going to be calculated, all the percentage errors must first be made positive. The Patient Admission Prediction Tool is used to forecast the number of patients who will turn up to a hospital on any given day of the year. For example, on 28 June, the forecast was 206 people but the actual number was 221. The forecast was 15 the actual. This error could be written as . Percentage Data for predicted and actual numbers of admissions in the first 20 days in July are provided. Ask each student, or group of students, to calculate the percentage error for one (or more) days and then write the error for each day on individual sticky-notes. The answers for the first two days are given. Record these on sticky-notes as well. Ask students to arrange the sticky-notes in order of magnitude of the error to answer these questions. Number of days for which the forecast was an under-estimate. Number of days for which the forecast was correct. Number of days for the forecast was an over-estimate. Number of days for the forecast was less than 5% above or below or actual. Number of days for which the forecast was less than 10% above or below or actual. Discussion questions Students may notice that the largest was more than 30%. What is a reasonable error? Will that reasonableness change depending on the context?
MI_Hospitals_Teacher.docx