I have a web page where users are shown a set of products and they express interest in the products that they like by selecting them.
I am running an AB experiment to test two versions of this page, each version showing a different set of products. The metric that I am testing in this experiment is the number of products that are selected by users.
All the resources I found on AB testing assumed a "trials and conversions" model, where there are X number of trials and, from those trials, Y number of conversions occur. In this model, Y <= X.
In my case, I have X = # impressions of the page and Y = # products selected on that page where Y > X in some cases. How can I determine which version performs better in this case?
For every variant you can divide the total number of objects by the number of impressions. That way you will select the variant that gives you more objects per user.
Related
I have an organism x with n number of individuals tested. One individual is only tested once. The individuals are trained and tested in a choice array with multiple possible choices. The test choices are of two types p and q with 10 each, amounting to a total of 20 possible choices. The individual is allowed to choose as many times as they want till they are done making choices. What do i do with this data set? How do i analyse the preference?
I have to run a montecarlo where, for some products, certain exchanges are relate to each other in the sense that my process can take as input any of the products in different (bounded) proportions but with fixed sum.
Example:
my product a takes as inputs a total of 10 kg of x,y, and z alltogheter and x has a uniform distribution that goes from 0 to 4 kg, y from 1 to 6 and z from 3 to 8 with their sum that must be equal to 10. So, every iteration I would need to get a random number for my three exchanges within their bounds making sure that their sum is always 10.
I have seen that in stats_array it is possible to set the bounds of the distributions and thus create values in a specified interval but this would not ensure that the sum of my random vector equals the fixed sum of 10.
Wondering if there is already a (relatively) straightforward way to implemented this in bw2
Otherwise the only way I see this feasible is to create all the uncertainity parameters with ParameterVectorLCA, tweak the value in the array for those products that must meet the aforementioned requirements (e.g with something like this or this) and then use this array with modified parameters to re-run my MC .
We are working on this in https://github.com/PascalLesage/brightway2-presamples, but it isn't ready yet. I don't know of any way to do this currently without hacking something together by subclassing the MonteCarloLCA.
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 have a dataset of size (61573, 25). The rows represent users whereas the columns represent views on particular movie genres. For example, if data[i,j] == 3 that means that user i has viewed 3 movies of gender j in total. As expected ,rows are sparse and right-skewed.
What I would like to do is to compute how much engaged a user is on each of the 25 movie genders by assigning to him one of the following tags: {VL, L, A, H, VH}.
What I have tried so far is to compute z-scores, either row or column -wise (I haven't tried to standardize values twice, though (i.e. first on rows and then on columns)), and then apply the following function depending on how far away the z-scores are from 0:
(-oo, -2] --> VL
(-2, -1] --> L
(-1, +1) --> A
[+1, +2) --> H
[+2, +oo) --> VH
In either case, my problem is that the results seem very bad in most of the cases probably because they are laying between -1 and +1, and thus are almost always marked as A (i.e. average). So, what else should I try based on your opinion? How would YOU approach this problem?
The z-scores clearly are not the right way to go.
The reason is that they are based on the assumption that your data is normal distributed. I have strong doubts that your data is normal distributed - in particular, it probably doesn't have any negative values, does it?
Have you tried just using quantiles? top 10%, bottom 10% etc.?
I'd like to use Excel to generate a randomized lab partner list, without using VB (due to security settings on the PCs).
Parameters are as follows:
Number of students: 10-30, one worksheet per total number desired
Number of partners: Three for first two labs, and two for the other four-five.
Number of lab stations: 10
Repeats: Ideally none, but it is permissible for a student to have a repeat partner from one of the first two labs.
Excel version: 2007
To clarify, each student will have two labs where they share a lab station with up to two other students, giving a maximum lab size of 30 students. After that, they will be strictly limited to two students per station, giving a maximum of 20 students. Each student will have four of these limited labs, with there being a total of five such labs presented, to allow for either odd-numbered classes, or a class size between 21-30.
Each student is simply numbered from 1-30, so a cell could, for instance, state "5, 24" as the two students for that lab station.
True RNG is not important, and in fact, only needs to be performed once to make these matrices.
I think this is a bit tricky without using VBA, but here is one approach that is OK for small groups. I have tried it using a group of just nine so that the screen shot should be readable.
The method is basic Fisher-Yates
A Start with a group of students size n represented by a list of numbers 1 to n.
B Generate a random number r in range 1 to n
C Pick the rth element from the list
D Remove the rth element from the list
E Reduce n by 1
F Repeat from B until n=1.
In Excel:-
Fill A2:A10 and D2:L2 with numbers 1-9
Put the following in B2 and pull down:-
=RANDBETWEEN(1,10-A2)
Put this in C2 and pull down:-
=OFFSET(D2,0,B2-1)
Put this in D3 and pull down and across:-
=IF(D2>=$C2,E2,D2)
The ID's will be in column C so the first three would be in group 1, the next three in group 2 etc.
By the way, your question is a special case of generating non-repeating random numbers - see
Generating unique random numbers without VBA
The array formula described here does it in one step - modified slightly for this problem it would look like
=SMALL(IF(COUNTIF(C$1:C1,ROW(INDIRECT("1:9")))=0,ROW(INDIRECT("1:9"))),RANDBETWEEN(1,(9-ROWS(C$2:C2)+1)))