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Sample Code snippet to copy list items from source list to destination list

we need to copy list items from source list to destination list,

The following things has to be considered

  1. Source List is having around 17000 items.
  2. CSOM C# Approach should be used.
  3. Throttling should be handled so that the context should not break during execution.

Things we cannot try

  • Tools based migration as it costs to the client.

any help would be appreciated..

Variance of (bootstrap) sample mean conditional on sample

I am having some trouble understanding the manipulation of the variance of the bootstrap mean, conditional on the sample. \begin{align*} Var(\bar{X}^B|X^S) & = Var({X}^B|X^S) \ \ & = \frac{E(X_{i}^B – \bar{X})^2|X^S}{n} \ \ & = \frac{\frac{1}{n}\sum_{i=1}^n(X_{i} – \bar{X})^2}{n} \end{align*}

Where $ {X}^B$ are bootstrap quantities and $ X^S$ is the sample. I understand the first equality (iid), the second (definition of conditional variance), but not the third. How does taking the sample mean allow us to replace $ X_{i}^B$ with $ X_{i}$ ?

Thank you.

Can’t see the design and build of Sample App “Todo” (Sample Xamarin app). How to fix it?

Can’t see the design and build of Sample App “Todo” (Sample Xamarin app). How to fix it?

In the design says “Some assembly references are missing. Building to restore the NuGet cache might resolve this issue.”

Error list :

Severity    Code    Description Project File    Line    Suppression State Error   XDG0008 The name "WindowsPage" does not exist in the namespace "using:Xamarin.Forms.Platform.UWP".  Todo.UWP    MainPage.xaml   1     

In the output Xamarin Diagnostic :

[I:]:                     Found Xamarin.Android [I:]:                     Found Xamarin.Android [I:sdk]:                  Runtime path: C:\Program Files (x86)\Microsoft Visual Studio17\Community\MSBuild\Xamarin\Android [I:sdk]:                  Framework path: C:\Program Files (x86)\Microsoft Visual Studio17\Community\common7\ide\ReferenceAssemblies\Microsoft\Framework\MonoAndroid\v1.0 [I:]:                     Tracking android devices started [D:]:                     TrackDeviceTask got:  [I:]:                     Got new device list from adb with 0 devices [D:]:                     Tracking avd started [D:]:                     avd watcher *.ini path: 'C:\Users\User\.android\avd' [D:]:                     avd watcher: got device list from avd with 2 devices [E:]:                     an error occurred loading avd `Pixel_2_API_28`, the system image does not exists [I:]:                     Looking for Android SDK... [D:]:                     ValidateAndroidSdkLocation: `C:\Program Files (x86)\Android\android-sdk`, result=True [D:]:                     ValidateAndroidSdkLocation: `C:\Program Files (x86)\Android\android-sdk`, result=True  

Screenshot : Problem Screenshot

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Sample size, P-values (its relationship), and data visualization with plots. ggplot and T test

We know that P-values (within t-test context as an example..) is highly sensitive to sample size. A larger sample will yield a smaller p-value remaining everything else constant. On the other hand, Cohen´s d effect size remains the same.

Sample size and P values

I’m inspired in this code here, but I´ve changed some parts to make the difference between means constant, instead of creating a random variable based on a normal distribution.

Although everything is working, I do imagine that some of the experts in this community could improve my syntax.

library(tidyverse)  ctrl_mean <- 8 ctrl_sd <- 1  treated_mean <- 7.9 treated_sd <- 1.2  sample <- numeric() #criar vetor para grupar resultados nsim <- 1000 #criar variavel t_result <- numeric()  for (i in 1:nsim) {    set.seed(123)    t_result[i] <- (mean(ctrl_mean)-mean(treated_mean))/sqrt((ctrl_sd^2/(i))+(treated_sd^2/(i))) #manual t test   sample[i] <- i # number of participants } ds <- data.frame(   sample = sample, #assign the sample size   t_result = round(t_result,3), #get the t test result   degrees = sample*2-2) #compute the degrees of freedom  ds %>%    filter(sample>1) %>%    mutate(P_Value = 2*pt(abs(t_result), df=degrees,lower.tail=FALSE)) %>%    left_join(ds,.) -> ds  #plot  ggplot(ds, aes(x=sample, y=P_Value)) +   geom_line() +   annotate("segment", x = 1, xend=sample, y = 0.05, yend = 0.05, colour = "purple", linetype = "dashed") +   annotate("segment", x = 1, xend=sample, y = 0.01, yend = 0.01, colour = "red", linetype = "dashed") +   annotate("text", x = c(1,1), y=c(.035,.001), label = c("p < 0.05", "p < 0.01")) 

Asymptotic rate for the expected value of the square root of sample average

I have iid random variables $ X_1, \dots, X_n$ with $ X_i \geq 0$ , $ E[X_i]=1$ and $ V[X_i] = \sigma^2$ . Let $ S_n = \frac{\sum_{i=1}^n X_i}{n}$ .

I’d like to say that $ E[\sqrt{S_n}] = 1-O(1/n)$ .

My first approach was to write $ E[\sqrt{S_n}] = \sqrt{E[S_n] – V[\sqrt{S_n}]} = \sqrt{1-V[\sqrt{S_n}]}$ .

I’m then left with showing that $ V[\sqrt{S_n}] = O(1/n)$ .

I’m unsure how to go about this. First, can I hope to prove such an asymptotic bound in general? If not, are there extra assumptions that can be made on the $ X_i$ so that this holds true?

converting CSV format data to graph sample format for implementing BFS

import collections   def breadth_first_search(graph, root):      visited, queue = set(), collections.deque([root])     while queue:          vertex = queue.popleft()         for neighbour in graph[vertex]:              if neighbour not in visited:                  visited.add(neighbour)                  queue.append(neighbour)    if __name__ == '__main__':     graph = {0: [1, 2], 1: [2], 2: []}      breadth_first_search(graph, 0) 

I am implementing BFS with the help of given sample data. But here I intend to use my own data. I am extracting graph from Neo4j into CSV format but how do I transform that extracted data into the sample format. Here is some of the sample data from CSV file.

"[{name:Keanu Reeves,born:1964},{roles:[Julian Mercer]},{title:Something's Gotta Give,released:2003}]" "[{name:Keanu Reeves,born:1964},{roles:[Shane Falco]},{title:The Replacements,tagline:Pain heals, Chicks dig scars... Glory lasts forever,released:2000}]" "[{name:Keanu Reeves,born:1964},{roles:[Johnny Mnemonic]},{title:Johnny Mnemonic,tagline:The hottest data on earth. In the coolest head in town,released:1995}]" "[{name:Keanu Reeves,born:1964},{roles:[Kevin Lomax]},{title:The Devil's Advocate,tagline:Evil has its winning ways,released:1997}]" 

No aggregate frame stats of the Native Activity sample

I run the Native Activity sample on MI Pad 4 Plus, with Android version 8.1.0. It’s ok. Then, I run “adb shell dumpsys gfxinfo com.example.native_activity “. It shows a little bit of information, without aggregate frame stats nor HISTOGRAM. Attached please find the screenshot. Is there any settings needed in the develop phase for this sample to get the gfxinfo?