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So this chap has modelled the outbreak.

[ATTACH=full]101948[/ATTACH]

Before I start, I want to explain what the tables above and below mean.

The figures underneath the percentages are dates. The dates are estimates of when the country, state or city will have experienced 10%, 50%, 90%, and 99% of the total COVID-19 cases that they will experience in the first wave.
The peak day is the day each has the most new claims.
“Expected Total” is my estimate for the total number of reported COVID-19 claims in the first wave.
“% pop” is the percentage of each population that will be reported as infected with COVID-19.
“% complete” is the ratio of estimated current total cases to estimated final total cases fo the first wave.
Pseudo-R2 is the percentage of the total variation in the total cases explained by my three-parameter nonlinear regression. Because the regression is nonlinear, it is not an F-statistic, and gives us only a spit-in-the-wind sense for how good the regression is. Some have asked if I could add error bands to my models and the answer is no, because the nonlinearity of the equation makes that difficult. I’m only working with Excel, and looking through my old Econometrics texts, they don’t have an answer for this one. Maybe I should start modeling in R.

jog on
duc

<blockquote data-quote="ducati916" data-source="post: 1065728" data-attributes="member: 2975">So this chap has modelled the outbreak.[ATTACH=full]101948[/ATTACH] Before I start, I want to explain what the tables above and below mean.<ul>
<li data-xf-list-type="ul">The figures underneath the percentages are dates. The dates are estimates of when the country, state or city will have experienced 10%, 50%, 90%, and 99% of the total COVID-19 cases that they will experience in the first wave.</li>
<li data-xf-list-type="ul">The peak day is the day each has the most new claims.</li>
<li data-xf-list-type="ul">“Expected Total” is my estimate for the total number of reported COVID-19 claims in the first wave.</li>
<li data-xf-list-type="ul">“% pop” is the percentage of each population that will be reported as infected with COVID-19.</li>
<li data-xf-list-type="ul">“% complete” is the ratio of estimated current total cases to estimated final total cases fo the first wave.</li>
<li data-xf-list-type="ul">Pseudo-R2 is the percentage of the total variation in the total cases explained by my three-parameter nonlinear regression. Because the regression is nonlinear, it is not an F-statistic, and gives us only a spit-in-the-wind sense for how good the regression is. Some have asked if I could add error bands to my models and the answer is no, because the nonlinearity of the equation makes that difficult. I’m only working with Excel, and looking through my old Econometrics texts, they don’t have an answer for this one. Maybe I should start modeling in R.</li>
</ul>jog onduc</blockquote>

[QUOTE="ducati916, post: 1065728, member: 2975"] [FONT=Book Antiqua]So this chap has modelled the outbreak. [ATTACH=full]101948[/ATTACH] [I]Before I start, I want to explain what the tables above and below mean. [LIST] [*]The figures underneath the percentages are dates. The dates are estimates of when the country, state or city will have experienced 10%, 50%, 90%, and 99% of the total COVID-19 cases that they will experience in the first wave. [*]The peak day is the day each has the most new claims. [*]“Expected Total” is my estimate for the total number of reported COVID-19 claims in the first wave. [*]“% pop” is the percentage of each population that will be reported as infected with COVID-19. [*]“% complete” is the ratio of estimated current total cases to estimated final total cases fo the first wave. [*]Pseudo-R2 is the percentage of the total variation in the total cases explained by my three-parameter nonlinear regression. Because the regression is nonlinear, it is not an F-statistic, and gives us only a spit-in-the-wind sense for how good the regression is. Some have asked if I could add error bands to my models and the answer is no, because the nonlinearity of the equation makes that difficult. I’m only working with Excel, and looking through my old Econometrics texts, they don’t have an answer for this one. Maybe I should start modeling in R. [/LIST] [/I] jog on duc [/FONT] [/QUOTE]

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