How to Pass a Calculated Member/measure to a Drill-thru Target Report
In order to avoid using Calculated Members--because from googling some people were saying you could not pass them via Drill-thru--I went back to my FM model and created 3 new measures (High Risk, Low Risk and Medium Risk). Now these will show up in the drill-thru definitions parameter list . . . my only problem is that how can I do a check to see which of the three measures has been selected by a user?
Remember, I basically have a line chart with 3 lines, one for each measure above (High, Medium or Low Risk) by time frame. A user will select a data point, High risk for March or Medium Risk for Semester 2, for example. I then need to pass the value for that datapoint to my Target (2nd) report. How can I check for which of the three measure values they passed through?!?
Related
Given the following data for 12 users:
username, number of deals for control, revenue from test, revenue from control
Here's an example of how the data looks like
Can you help me figure out how I can calculate the significance of the hypothesis that the test is more profitable (preferably using excel)?
The measure I was thinking of using was the % of lift in revenues for each customer.
P.s. I have a background in statistics but not an expert so please keep it as simple as possible.
Since each pair of incomes refers to the same individual, you can perform a paired t-test.
Variable 1: Control income
Variable 2: Deals income
Then follow these instructions (copied here for posterity):
In Excel, click Data Analysis on the Data tab.
From the Data Analysis popup, choose t-Test: Paired Two Sample for Means.
Under Input, select the ranges for both Variable 1 and Variable 2.
In Hypothesized Mean Difference, you’ll typically enter zero. This value is the null hypothesis value, which represents no effect. In
this case, a mean difference of zero represents no difference between
the two methods, which is no effect.
Check the Labels checkbox if you have meaningful variables labels in row 1. This option helps make the output easier to interpret. Ensure
that you include the label row in step #3.
Excel uses a default Alpha value of 0.05, which is usually a good value. Alpha is the significance level. Change this value only when
you have a specific reason for doing so.
Click OK.
Alternatively, you can indeed calculate the difference between the two incomes, and then perform a one sample t-test (assuming that the difference is zero). However, such a test is not available out-of-the-box in Excel; the procedure is described here.
I work for a hospital that is part of a larger network. We were recently asked by our corporate overlords to address the use of a specific laboratory test. in general, this test should only be performed daily, which should be considered to corresponded to a 24 hour period from last draw. sometimes, however, based on when people arrive to the hospital (e.g. 7pm), and in the interest of bundling labs for a single draw, they may be drawn sooner to coincide with routine testing i.e. 5am. it would never be necessary to otherwise need to repeat within a short (8 hour) window, particularly on the same day.
we have been asked to validate to see if we are adhering to this general practice, as testing any more frequent than that, say, within 12h of a previous test, has no real clinical value and thus adds unnecessary cost.
To address this issue I was given a dataset that among other items includes all instances the lab was performed including collection date and time.
please see HIPPA-safe example below (to be clear, no real data and identifiers are not real); the actual dataset has over 4,174 entries corresponding to 1,328 unique persons. everyone had at least one test performed, not everyone had >1.
I THINK what I want to do is an IF formula that reads the antecedent cell to 1) check if same person and 2) if so, perform a subtraction of the time stamp to display the relevant difference in time, which I can then filter, create histogram, etc. does this seem like a reasonable approach? is there a more preferable method to facilitate analysis? do any other forms of analysis come to mind?
=IF(B2=B1, D2-D1, "n/a")
example data set with formula:
any other forms of analysis come to mind?
By the looks of it you should consider taking the values under "Results" into account, assuming there is a band that might be considered 'normal' readings. The "one in 24 hours is sufficient" rule of thumb may well be appropriate for a series of values within the 'normal' band but not so much so if readings are close to 'danger level'.
That is, in some cases a higher than 'standard' frequency of monitoring may be in the patient's interest, even if not hospital policy, so it may be worth separating the "less than 24 hours interval" readings into those where the higher frequency provided information of little value (eg readings remaining within a 'normal' band) from any that crossed into or out of the band and/or large changes in value. This though may be more a matter of statistical analysis than programming and depend upon whether any action might be taken as a result of such "extra" readings.
Example data with desired outcome that I need to calculate
I have 12 items of a certain current value. I have a 'soft' cap of $1,000,000 for these values. Some of the items fall above, and some below this cap level.
I have an amount of money (for this example $900,000) that I want to distribute amongst only the items that fall below the cap (in this example 6 items), with the aim of bringing the value of these items up to but not over the cap value.
If I distribute the $900,000 evenly over these 6 items (each receiving $150,000), you can see that items 2 and 9 would then be over the $1,000,000 cap. So items 2 and 9 should only receive $100,000 to raise their value to the cap, then the remaining 4 items would receive and equal share on the remaining pool of money ($700,000 / 4 = $175,000).
So I need a formula to check every item to see if it needs a distribution (i.e below the cap) and then portion/divide out the money pool as illustrated above in the desired distribution column.
Note: The pool of money to be distributed can change. Also the number of items below the cap can change. The cap value itself can change.
I am hoping to avoid VBA or Solver because the spreadsheet could be used on other people's computers.
Hopefully this makes sense. Thanks.
EDIT:
So far I have been able to get close by adding a helper column and using the following formula:
=IF(SUM($F$6:F14)=$D$23,0,E15*MIN(D15,($D$23-SUM($F$6:F14))/SUM(E15:$E$18)))
Working example when values are sorted.
This seems to work when the values are sorted in descending order, as shown in the example image above. But seems to break when the values are a bit more randomly assorted which is likely to happen (as in the original post).
Just to give you an idea of how the solver can be set up to do a capital budget model here is one, also shows the solver and its settings:
Data and description of variables
Picture 1 and Sample unbalanced paneldata
Picture 1 shows a balanced panel data that I have created using an unbalanced one provided as a sample in the same image, where I had multiple products (ID) for different amount of years (YEAR). For each product, there were a different number of Shops offering the given product (ID). So as stated, this is a balanced set created by sorting out for the same years, same products (ID), and same shops (marked by the orange area in the sample unbalanced paneldata). This is an important assumption that might affect the perception of the issue stated below. The following is therefore a description of the table shown in Picture 1:
Years indicates the amount of period a product lasts for a given product (ID)
Shop 1, Shop 2, Shop 3 indicate different prices for a given product (ID) by different firms
The minimum and second minimum value depict what shops for a given year and product (ID), have the lowest and second lowest price for that given year. This is needed to calculate the Price difference, which is **(Second minimum value - Minimum Value) / (Minimum Value)
An example of this, is given for row 5 (Year 01.01.1995 - ID 101) where Price difference would be (3999-3790)/3790 = 5,51% (In Picture 1)
Issue
In my balanced panel data, (Picture 1), I want to run a fixed effect regression in STATA using xtreg function, where the dependent variable is the Price difference, and number of shops selling a product are the independent variables. This is, so I can say how Price difference as a dependent variable is affected when there is 1 shop selling, when there are two shops selling, and when there are three shops selling.
Another problem is, is my assumption valid at all of creating a balanced panel? Is it correct to create a balanced from the unbalanced paneldata, or must I use the unbalanced panel to create such a variable?
So my main issue is how to create such independent variables, that measure the dimension of number of shops offering products. To
clarify what I mean, I have included an example of a sample fixed
effect regression that may explain the structure that I attempt to
seek, in Picture 2 below:
NOTE (In picture 2 expected cell mean to the right is the same as Price difference in Picture 1, and is used as dependent variable. They are regressed on number of firms/shops as independent variables, and these I have an issue creating)
Picture 2
What I have tried
I have tried, using dummy variables, on shops, but they ended up getting dropped. The dataset provided in picture 1 is a balanced data set as mentioned, which is needed to run (I assume) a fixed effect regression on a paneldata.
End remark
I stated this question earlier in a much more imprecise manner, where I apologiese for any inconvenience. The problem I think, might be that either I have set it up wrong in excel, hence the dummy's are dropped, or something of that nature. It might also be, that I have to use the unbalanced set in order to create this independent variable, so that might also be a problem, that I am attempting to use a balanced set instead of the unbalanced one.
In your unbalanced sample (as we discussed in the comments, the balanced sample will not make sense) we first need to create a variable for the number of shops offering each ID, let us say we have the same data as in the top portion of your Picture 1
egen number_of_firms = rownonmiss(Shop*)
xtset ID year // to use xtreg, we must tell Stata the data are panel
xtreg Price_difference i.number_of_firms
The xtreg is the regression shown in your Picture 2.
If you want the number of firms variable to be formatted a bit more like Picture 2, you can do something like this:
qui levelsof number_of_firms, local(num)
foreach n in `num' {
local lab_def `lab_def' `n' "`n' Firms"
}
label def num_firms `lab_def'
label values number_of_firms num_firms
label var number_of_firms "Number of Firms"
And then run the regression and the output will be formatted with the number of firms lables.
I am using chap-links-library in the project I am working on. We are trying to show different kind of activities that a user performed. We want to display the activities and number of times that has been performed by the user.
The initial zoom will be for 12 months and the user can zoom in to day level. I want to group all activities of same type performed in a month and show the cumulative count on initial display. When user zooms in they should be able to see the split up of activities and its count based on the visible range.
Any pointers on how to handle this in chap-links-library will be useful.
You can listen for the rangechange or rangechanged events (see docs), and when the visible range exceeds a certain threshold, replace all data in the Timeline with more coarse grained data when above it, or with the detailed data when below the threshold.
You may want to have a look at the successor of chap-links-library, vis.js. You can find an overview of the main changes here: http://almende.github.io/chap-links-library/#successor.