Assessment, Statistics, and Research

If the posted data are accurate, we have an evident decrease on weekly deaths for the 7-days ending May 22 2020, which is the far right column. That is two weeks of decline and much lower than the April 18 column. The bad news is of course that the US has reached 100,000 deaths. Legend M21 to A25 and M1 represent the Month and Day. The numbers below the dates are for the 7-days including the date. For example, M1 = 11,989 deaths in the 7-days before and including May 1, 2020. The first M dates are for March, then A for the April dates, then back to May again for M1 and so forth. The data are beginning to look like a bell curve, but with many states allowing more freedom of movement, it is too early to tell if there will be a rise in a week or two. The data are from https://ourworldindata.org/grapher/total-daily-covid-deaths I use the download data file and create the chart in Excel. Read more about statistics in these two books. Creating Surveys

We have an evident decrease on weekly deaths for the 7-days ending May 15 2020. Downward progress (green columns) resumed after the May 8 increase (yellow column). Legend M21 to A25 and M1 represent the Month and Day. The numbers below the dates are for the 7-days including the date. For example, M1 = 11,989 deaths in the 7-days before and including May 1, 2020. The first M dates are for March, then A for the April dates, then back to May again for M1 and so forth. The data are beginning to look like a bell curve, but with many states allowing more freedom of movement, it is too early to tell if there will be a rise in a week or two. The data are from https://ourworldindata.org/grapher/total-daily-covid-deaths I use the download data file and create the chart in Excel. Read more about statistics in these two books. Creating Surveys  on   AMAZON Read more about basic statistics in  APPLIED STATISTICS: CONCEPTS FOR COUNSELORS  at AMAZON

We have a slight increase on weekly deaths for the 7-days ending May 8 2020. Downward progress (green columns) has halted. Legend M21 to A25 and M1 represent the Monty and Day. The numbers below the dates are for the 7-days including the date. For example, M1 = 11,989 deaths in the 7-days before and including May 1, 2020. The first M dates are for March, then A for the April dates. The data are beginning to look like a bell curve, but with many states allowing more freedom of movement, it is too early to tell if there will be a rise in a week or two. The data are from https://ourworldindata.org/grapher/total-daily-covid-deaths I use the download data file and create the chart in Excel. Read more about statistics in these two books. Creating Surveys  on   AMAZON Read more about basic statistics in  APPLIED STATISTICS: CONCEPTS FOR COUNSELORS  at AMAZON Connections Follow this blog My Page      www.suttong.com My

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   https://ourworldindata.org/grapher/total-daily-covid-deaths Date               Deaths M21 213 M28 1447 A4 5450 A11 11620 A18 18277 A25 13963 Read more about statistics in

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

Statistical models are needed to guide government leaders and health service providers concerned with maximizing the number of infected people who survive and providing high quality care to those in need. All models have multiple assumptions. In the midst of a pandemic such as Covid-19, new data are constantly being processed. Thus, parameters will need to be changed as new data change the models. Multiple outcomes must be considered without biased interpretations favoring either lower or higher estimates. NYT 13 March 2020 reported by Sheri Fink This report refers to four scenarios and is early than the ones further down the page. "Between 160 million and 214 million people in the United States could be infected over the course of the epidemic, according to a projection that encompasses the range of the four scenarios. That could last months or even over a year, with infections concentrated in shorter periods, staggered across time in different communities

Some highly intelligent scientists have used available data to plot trends related to the spread of Covid-19 such as number of identified infections, recoveries, and deaths. Others plotted ideas rather than data such as flattening the curve. Some rely on "common sense" and others rely on data. Given my experience in teaching graduate research methods and statistics and talking with physicians and psychologists about statistics, I think it worth reminding any readers of this post that a lot of very intelligent people have difficulty with statistical models. There are many PhDs younger than I who know more methods of sophisticated data analyses than I learned during my PhD or since; however, not everyone stops to consider their assumptions when modeling data. That's my point here--we need to review and challenge the assumptions about the data, the way the data are plotted, and how people interpret those data. Missing data are very important to developing an accurat