I would like to perform a regression on a data set. However, the dataset is very large and includes data from companies over a period from 1989 to 2019. As can be seen in the attached image, the maximum observations per group is 30.
Is it possible to specify the regression command or the data set to use only the maximum number of observations per group. So only for companies where data for 30 years are consistently available?
xtreg tobins_q_w rdi_w ivol_w firm_size_log_w industry_q_w growth_w lev_w adi_w os_w, fe
Outcome xtreg
egen OK = total(!missing(tobins_q_w, rdi_w, ivol_w, firm_size_log_w, industry_q_w, growth_w, lev_w, adi_w, os_)), by(PERMNO)
and then regress conditionally on OK == 30.
Related
I want to create a graph which shows the total capacity for each week relative to remaining availability across a series of specific dates. Just now when I attempt this in Power Bi it calculates this correctly for one of the values (remaining availability) but generates a value much higher than expected by manual calculation for the total capacity - instead showing the total for the entire column rather than for each specific date.
Why is Power Bi doing this and how can I solve it?
So far, I have tried generating the graph like this:
(https://i.stack.imgur.com/GV3vk.png)
and as you can see the capacity values are incredibly high they should be 25 days.
The total availability values are correct (ranging from 0 to 5.5 days).
When I create matrices to see the sum breakdown they are correct but it only appears to be that when combined together one of the values changes to the value for the whole column.
If anyone could help me with this issue that would be great! Thanks!
When I apply Month/Year to Cases or Deaths from my data, the values explode. For Cases it goes from approximately 48 million to over 1 billion, and for Deaths it goes from about 700 thousand to over 22 million. However, when I try the same thing with Initial Claims or the Stringency Index, my values remain correct. I'm trying to find the month over month percentage change by the way. And I'm using the Date column. I only select 2020 and 2021 in the filter for Year.
What I'm asking about is Sheet 21.
Link to workbook: https://public.tableau.com/app/profile/nilajah.rivers/viz/CoronaVirusProject_16323687296770/Sheet21
Your problem is that the data points are daily cumulative deaths. If you change the date aggregation to anything other than days, Tableau will default to summing the numbers for all the days in the month. This will give the wrong result, obviously.
If you want to show the correct total deaths or cases regardless of the time aggregation (months, days, weeks etc.) then you could use the New Case or New Death numbers plus a running sum table calculation. This will always give the correct total for the time period.
Table calculations will also allow automatic calculation of the period to period % change from the same data fields.
This is a common problem when working with datasets that offer pre-calculated aggregations. Tableau doesn't need that as it can dynamically calculate the aggregation of a field over any given time period but it is easy to forget which field has pre-aggregated data and which has raw data. Pre-aggregated fields assume a particular time period and can't be used for different time periods without disentangling that assumption (which is unnecessary if you also have the raw data (in this case daily new deaths/cases).
I have a metric that shows the state of a server. The values are integers and if the value is 0 (zero) then the server is stable, else it is unstable. And the graph we have is at a minute level. So, I want to show an aggregated value to know how many hours the server is unstable in the selected time range.
Lets say, if I select "Last 7 days" as the time duration...we have get X hours of instability of server.
And one more thing, I have a line graph (time series graph) that shows the state of server...but, the thing is when I select "Last 24 hours or 48 hours" I am getting the graph at a minute level...when I increase the duration to a quarter I am getting the graph for every 5 min or something like that....I understand it's aggregating the values....but does any body know how the grafana is doing the aggregation ??
I have tried "scaleToSeconds" function and "ConsolidateBy" functions and many more to first get the count of non zero value minutes, but no success.
Any help would be greatly appreciated.
Thanks in advance.
There are a few different ways to tackle this, there are 2 places that aggregation happens in this situation:
When you query for a time range longer than your raw retention interval and whisper returns aggregated data. The aggregation method used here is defined in your carbon aggregation configuration.
When Grafana sends a query to Graphite it passes maxDataPoints=<width of graph in pixels>, and Graphite will perform aggregation to return at most that many points (because you don't have enough pixels to render more points than that). The method used for this consolidation is controlled by the consolidateBy function.
It is possible for both of these to be used in the same query if you eg have a panel that queries 3 days worth of data and you store 2 days at 1-minute and 7 days at 5-minute intervals in whisper then you'd have 72 * 60 / 5 = 864 points from the 5-minute archive in whisper, but if your graph is only 500px wide then at runtime that would be consolidated down to 10-minute intervals and return 432 points.
So, if you want to always have access to the count then you can change your carbon configuration to use sum aggregation for those series (and remove the existing whisper files so new ones are created with the new aggregation config), and pass consolidateBy('sum') in your queries, and you'll always get the sum back for each interval.
That said, you can also address this at query time by multiplying the average back out to get a total (assuming that your whisper aggregation config is using average). The simplest way to do that will be to summarize the data with average into buckets that match the longest aggregation interval you'll be querying, then scale those values by that interval to calculate the total number of minutes. Finally, you'll want to use consolidateBy('sum') so that any runtime consolidation will work properly.
consolidateBy(scale(summarize(my.series, '10min', 'avg'), 60), 'sum')
With all of that said, you may want to consider reporting uptime in terms of percentages rather than raw minutes, in which case you can use the raw averages directly.
When you say the value is zero (0), the server is healthy - what other values are reported while the server is unhealthy/unstable? If you're only reporting zero (healthy) or one (unhealthy), for example, then you could use the sumSeries function to get a count across multiple servers.
Some more information is needed here about the types of values the server is reporting in order to give you a better answer.
Grafana does aggregate - or consolidate - data typically by using the average aggregation function. You can override this using the 'sum' aggregation in the consolidateBy function.
To get a running calculation over time, you would most likely have to use the summarize function (also with the sum aggregation) and define the time period, e.g. 1 hour, 1 day, 1 week, and so on. You could take this a step further by combining this with a time template variable so that as the period grows/shrinks, the summarize period will increase/decrease accordingly.
I have a dataset that contains various wait-time metrics for all appointments in a practice for a year (check-in to call-back, call-back to check-out, etc). It contains appt time (one of about 40 15 minute slots), provider, various wait times.
I can get Tableau to show me, for each 15 minute slot, the average wait times for each provider in the practice.
What I can't seem to be able to do is also display the overall average for the practice for that given time slot so as to be able to compare that provider vs. the "office standard".
I'm super new to trying out Tableau, so I am sure it is something very simple.
Thanks in advance.
Use a level-of-detail (LOD) calculated field. An LOD calculation occurs at whatever aggregation level you specify, rather than what's on the row or column shelf.
You didn't provide any info about your data set so I will use made up names here.
This gives you the overall average wait time, regardless of other dimensions on row/column shelves:
{FIXED : avg([wait time])}
This gives you the overall average wait time per provider, regardless of other dimensions on row/column shelves:
{FIXED [Provider Name] : avg([wait time])}
See the online Tableau help at https://onlinehelp.tableau.com/current/pro/desktop/en-us/calculations_calculatedfields_lod_overview.html for more information. If you have filtering and need to calculate the overall without filters applied, look at the INCLUDE LOD keyword.
I am a very basic user of tableau and I have not found an answer to my question.
I have a txt file that has historical daily data for 98% of all the stocks in the US, with their daily capitalization. Each stocks has its TICKER, Daily Market Value for every trading day of the year, and its SECTOR.
I did a simple time series that display SUM([Mktval]) (sum of all individual market values) across all stocks, on a daily daily, and where I can see that the total value as of 2016 is about 24 Trillion USD, as in the image below.
When I change the view column from DAY to YEAR, I don't see the right values, but something a lot larger. So I realized that I need to do SUM([Mktval])/252 to get the right value for a year (there are 252 trading days in a year).
If I change the view to MONTH, as in the chart below, the numbers are again wrong because 252 is not the right value to use in the division.
Is there any way that Tableau can adjust the values automatically to reflect the AVG MktVal across different time intervals?
Thanks
Replace SUM(Mktval) on the Rows shelf with the following calculated field
avg({ fixed day(Date1) : sum(Mktval) })
That solution is all in one step. It is perhaps a bit more clear to use 2 steps. First, create a calculated field called total_daily_market_value defined as
{ fixed day(Date1) : sum(Mktval) }
Then make sure that calculated field is a measure. It is an LOD calculation that you can think of as a separate table with one value for each day showing the total market value for that day.
Drag that measure to a shelf, and then change the aggregation function to AVG(), MEDIAN(), MIN(), MAX() or STDEV() as desired. Tableau will aggregate the total_daily_market_value using your chosen aggregation function for whatever values of Date1 are in your view.