average

This is what I desire for my query average for 16 wks min 9 wks max 15 wks increasing month NOT alphabetic And my query so far looks like My code is TRANSFORM SUM(Detail.Quantity) SELECT Detail.ItemCode FROM Detail INNER JOIN Header ON Detail.SalesOrderNo = Header.SalesOrderNo WHERE Header.OrderDate>=dateadd("m",-4,Date()) GROUP BY Detail.ItemCode PIVOT MonthName(Month([Header.OrderDate])); Thank you so much in advance! And It'd be appreciated If anyone can answer why 'October' is showing as of 2/1/2017? while my code has WHERE Header.OrderDate>=dateadd("m",-4,Date()) ? Consider a join of two

I am trying to average every n-th object of a specific column in a dataframe using the following code. I understand that using the for-loop is computationally inefficient. This is why I would like to ask whether there is a more efficient way to create the average of every n-th row? My data looks a little bit like this. set.seed(6218) n <- 8760 s1 <- sample(30000:70000, n) s2 <- sample(0:10000, n) inDf <- cbind(s1, s2) EDIT: I call h_average like this: h_average(inDf, 24, 1, 1) This would mean that I average every first point of "every" 24 point subset. So the points 1, 25, 49, 73,... Also I

I have request: Model.group(:p_id).pluck("AVG(desired)") => [0.77666666666666667e1, 0.431666666666666667e2, ...] but when I ran SQL SELECT AVG(desired) AS desired FROM model GROUP BY p_id I got ----------------- | desired | |-----------------| | 7.76666666666667| |43.1666666666667 | | ... | ----------------- What is the reason of this? Sure I can multiply, but I bet where are should be an explanation. I found that Model.group(:p_id).pluck("AVG(desired)").map{|a| a.to_f} => [7.76666666666667,43.1666666666667, ...] Now I'm struggle with other task, I need numbers attributes in pluck so my

How do I average?. I am suppose to find the average of the GPA , total of students, and the total of GPA . example: input : 4 4 4 4 output: Total Students :4 Total GPA :16 Average GPA: 4 import java.util.Scanner; public class practice { public static void main(String[] args) { Scanner keyboard = new Scanner(System.in); int count = 0; double GPA = 0, total = 0, average; System.out.println("Enter GPA"); while (GPA >= 0) { GPA = keyboard.nextDouble(); total = total + GPA; count++; } average = total / count; System.out.println("Total students: " + count); System.out.println("Total GPA " + total);

I need to calculate in a function the average score of a column named: "totalscore" from my database table "score" I tried to do Active record select_avg() but I am not getting anything. Any idea how I can do this? function calculateaverage(){ $dataArr = array(); $data = $this->db->get('score'); $maxrows = $data->num_rows(); $data = $this->db->get('score'); for ($i = 1; $i<= $maxrows-1; $i++){ $this->db->select('totalscore'); foreach ($data->result() as $row) { $dataArr[$i] = $row->totalscore; } } return $dataArr; } You can try this code, very simple and straight forward. write it in your

I have a simple query that I want an average on. This is what it looks like now, and I want to know the average of my count per Opname_OpnameID. SELECT Opname_OpnameID, count(*) as 'behandelingen per opname' FROM behandeling GROUP BY Opname_OpnameID If you want the average count, presumably over the entire table, then just do exactly that: SELECT AVG(cnt) AS total_avg FROM ( SELECT COUNT(*) AS cnt FROM behandeling GROUP BY Opname_OpnameID ) t; You can use count(distinct) and not use a subquery: SELECT count(*) / count(distinct Opname_OpnameID) FROM behandeling 来源： https://stackoverflow.com

I am trying to calculate the medoid in matlab. However, i don't know how to do this. My dataset consists of multiple points of three-dimensional data (so a cloud of points in a system with three axes). The medoid is the point " whose average dissimilarity to all the other objects in the cluster is minimal " (wikipedia). Does anyone know how to calculate the medoid in matlab? Btw.: as far as i know the k-medoid algorithm cannot be used to calculate the medoid (efficiently), which is why i am looking for another way. Should not be difficult to do that once you provide the metric. Here is an