Carlsson et al. studied inter-observational and intra-observer variability in patients with manual ezekiel. [4] Reliability between and within the observer has been assessed with the assistance of CCI All measures are subject to error, which contributes to the uncertainty of the outcome. Errors can be classified as human or technical error. Maybe you pass a small volume from one tube to another and you don`t get all the fullness in the second tube because you knocked it over: it`s a human error. Results: average and median hair glucose levels of POC were 7.99 mmol/L and 6.25 mmol/L, respectively, while average and median laboratory venous venous glucose concentrations were 7.63 mmol/L and 5.35 mmol/L. The values for the capillaries of POC HbA (1c) and laboratory hbA (1c) were identical: average, 7.06%; median, 6.0%. The r correlation coefficient for POC and laboratory results was 0.98 for glucose and 0.99 for HbA (1c). The average difference in the result was 0.36 mmol/L for glucose (95% Cl, 0.13-0.62; Concordance limits [LOA], from 2.07 to 2.79 mmol/L; P -0.007) and it looked like a lot of material and was on a clinical topic that should be quite familiar, my students. This was particularly attractive to me because I have spent a lot of time in God`s land in recent years and I am diabetic who measures his own blood sugar every day. The paper was available online, so I looked around. For a statistician, the reason for their results is easy to pin down and has nothing to do with measuring the volume of lifting. If we take a very simple model and consider each measure as the sum of the actual value of the measured quantity and dash per measure, we have: As noted above, correlation does not mean compliance.

The correlation refers to the existence of a relationship between two different variables, while the agreement considers the agreement between two measures of a variable. Two sets of observations, strongly correlated, may have a poor agreement; However, if the two sets of values agree, they will certainly be strongly correlated. For example, in the hemoglobin example, the correlation coefficient between the values of the two methods is high, although the agreement is poor [Figure 2]; (r – 0.98). The other way of looking at it is that, although the different points are not close enough to the dotted line (least square line; [2], indicating a good correlation), these are quite far from the running black line that represents the perfect chord line (Figure 2: the black line running). If there is a good agreement, the dots should fall on or near this line (of the current black line). In the other, regression was used. Carr et al. (1979) compared two methods of measuring the left ventricular fraction of the heart. These authors gave the regression line of one method, pond wood, on the other, angiography. They found that the slope of the regression line was very different from the regression line and concluded that the methods did not match.

The reasoning was that if the two methods were agreed, a plot of one against the other would have to follow a line in which the two measures were equal, the line y-x, with inclination 1 and intercept 0. Dependent and independent variables are measured with errors, but the regression line with the smallest squares ignores the x error. It assesses the average y value for an observed x. The expected slope is «There`s something wrong, but I don`t know what it is,» he said. If two instruments or techniques are used to measure the same variable on a continuous scale, Bland Altman plots can be used to estimate match. This diagram is a diagram of the difference between the two measurements (axis Y) with the average of the two measurements (X axis).