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Showing posts from April, 2020

Covid19 U S Deaths Weekly Chart

Are we there yet? The most recent weekly data suggest a downward move in the number of people who died in the past week. In the previous chart, there appeared to be a channel or range between 1800 and 2000 per day with some anomalies. Now that we have more data, it is possible to group the data. I am avoiding curves and means because it is not clear that there is a curve or that the data are normally distributed. The bar chart offers a clear picture of rapid increase and possibly (and hopefully a decline. In this chart, I used weekly totals beginning with March 7, 2020 (M = March, A = April). Important note: The numbers may be revised. This chart is for educational purposes only and not for planning. Here is my source for the data Date               Deaths M21 213 M28 1447 A4 5450 A11 11620 A18 18277 A25 13963 Read more about statistics in

Charting Troubles and Covid-19

How will we know if opening up the country is safe? My bottom line answer is when people stop dying from Covid-19. But tracking actual deaths can help us know whether we are making progress or not. We can measure progress against our own baseline. The chart provides an estimate of a baseline, which will need to be corrected when additional deaths are recorded. Think of the baseline as a channel. The national US baseline appears to be in the range of 1800 to 2000 with some outliers starting 8 April 2020. I am not using averages because the data are not obviously normalized. I prefer to look at a range of relatively stable values, which can be called a channel. The sharp deviations have to be ignored to get a sense of what is "typical" of a pattern. A new relatively stable range above or below this range should help us determine a new trend. If the numbers are corrected, we will need to revisit the range. I am looking at a move of about 20% either way for evi

Measurement Error Standard Error of Measurement

In testing, measurement error usually refers to the fact that the same people can obtain different scores on the same test at different times. In a broad sense, measurement error can also refer to the degree of accuracy of a test to correctly identify a condition, which is discussed as test validity. Recall that test score reliability is a necessary but insufficient condition for test score validity. Many tests in psychology, medicine, and education are useful. The reliability of the scores will vary depending on such factors as the properties of the test itself as well as how well the user follows standard procedures in administering the test, environmental factors that can affect the scores, and factors within the person taking the test. The scores on many tests conform to the pattern called the normal curve or bell curve. In classical test theory, the scores people obtain on tests are simply called obtained scores (symbol X). Statisticians consider the variation in scores t