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The 2016 Marketing Analytics Summit / Inmar Analytics Forum held their Keynote and Graduate Final Competition at the Benton Convention Center in Winston-Salem. 4/12/16
I love seeing any upwards movement when looking at an web analytics software but when you have continuing upwards movement as pictured above, we call this the "hockey stick" in our office.
You can pretty much guarantee that we are giving each other mad hi-5 any time we see a hockey stick.
We offer data analytics for foundry in Pune. We have algorithms to organize, analyse the user foundry's green sand data legacy. Visit sandman.co.in/content/about_us for details.
The 2016 Marketing Analytics Summit / Inmar Analytics Forum held their Keynote and Graduate Final Competition at the Benton Convention Center in Winston-Salem. 4/12/16
Representatives from IBM visited Wake Forest to talk about technology, the Internet of Things, Blockchain, Watson, and more.
Information and Data Analytics enable employees to examine data in a specific context and make more informed business decisions to improve products and services.
In 2021, advanced analytical procedures will be used more for business #automation and task optimization.👍 It's no wonder that data analytics has become a critical tool in a variety of enterprises around the world.
👉Today top brands like Amazon, Starbucks, IKEA and more are utilizing data analytics for their great success and growth.
📌Learn more about data analytics and data science-related concepts here: Learnbay | Data Science and AI Training
Representatives from IBM visited Wake Forest to talk about technology, the Internet of Things, Blockchain, Watson, and more.
The 2016 Marketing Analytics Summit / Inmar Analytics Forum held their Keynote and Graduate Final Competition at the Benton Convention Center in Winston-Salem. 4/12/16
The 2016 Marketing Analytics Summit / Inmar Analytics Forum held their Keynote and Graduate Final Competition at the Benton Convention Center in Winston-Salem. 4/12/16
//Macro for ImageJ by
//Michael Cammer cammer@aecom.yu.edu for AIF www.aecom.yu.edu/aif/
//Data presented or published using this/these macro(s)/function(s) or modification(s) thereof
//must acknowledge the "Analytical Imaging Facility at the Albert Einstein College of Medicine"
//and ImageJ as explained at rsb.info.nih.gov/ij/docs/faqs.html
// revision 1.02 July 11, 2005
// This macro finds the area of the traced object.
// Assuming this area is of a perfect circle, the radius is calculated.
// Then the difference between the circle's radius and the real radii are calculated.
// This version measures the number of radii, evenly spaced, specified by variable n.
// All radii are normalized by a factor such that the standard radius is always 100.
macro"Radial Sweep [q]"{
requires('1.34k');
n = 256;
run("Set Scale...", "distance=1 known=1 pixel=1 unit= global");
run("Set Measurements...", " centroid area redirect=None decimal=0");
run("Measure");
xc = getResult("X", nResults-1);
yc = getResult("Y", nResults-1);
A = getResult("Area", nResults-1);
selectWindow("Results");
run("Close");
r = sqrt(A/3.1415);
factor = 100 / r;
r = r * factor;
// This routine gets all the pixels along a ROI, not just vertices.
// It only works with the magic wand tool or a complete freehand ROI tool.
// It does not work with the polygon tool!
getSelectionCoordinates(x_, y_);
x = newArray(x_.length+1);
y = newArray(x_.length+1);
for (i=0; i<x_.length; i++){
x[i] = x_[i];
y[i] = y_[i];
}
x[x_.length] = x_[0];
y[y_.length] = y_[0];
newx = newArray(65535);
newy = newArray(65535);
newindex = 0;
for (i=0; i<x.length; i++){
if (i 0) step = 1; else step = -1;
for (k=y0; k!=y1; k=k+step) {
newx[newindex] = x[i];
newy[newindex] = k;
newindex++;
} // for k
} // if (x[i+1] == x[i])
if (y[i+1] == y[i]){
x0 = x[i];
x1 = x[i+1];
dx = x1 - x0;
if (dx > 0) step = 1; else step = -1;
for (k=x0; k!=x1; k=k+step) {
newx[newindex] = k;
newy[newindex] = y[i];
newindex++;
} // for k
} // if (y[i+1] == y[i])
if ( (x[i] != x[i+1]) && (y[i]!=y[i+1]) ){
newx[newindex] = x[i];
newy[newindex] = y[i];
newindex++; }
} // if (i < (x.length-1))
} // for i
// This is the end of the routine that gets the coordinates along the edge.
// The coordinates are stored in the arrays x and y.
//setBatchMode(true);
stepsize = floor(newindex/n); //stepsize = 1;
print('//************************'); print(getTitle());
difference = newArray(65535);
newindex = newindex - 1;
sum = 0; count = 0;
for (i=0; i<newindex; i=i+stepsize) {
dx = xc - newx[i];
dy = yc - newy[i];
diag = factor * (sqrt ( (dx*dx) + (dy*dy) ));
//print(diag-r);
difference[count] = diag-r;
sum = sum + (diag-r); count++;
}
average = sum /count;
print("area", A);
print("average", average);
dif = newArray(count);
for(i=0;i 1){
variance = calculateUnbiasedVariance(a);
print("variance", variance);
stdev = pow(variance, 0.5);
print("stdev", stdev);
return stdev;}
else
return 0;
}
//----------------------------------------------------------------------------------
//Calculates BIASED variance as defined at davidmlane.com/hyperstat/A16252.html
//It is biased because it tends to underestimate the spread.
//We, however, like to err on the side of caution, a.k.a. UNBIASED.
//The variance is a measure of how spread out a distribution is.
//It is computed as the average squared deviation of each number from its mean.
//Function is passed an array of numbers and returns a single number.
function calculateBiasedVariance(a){
//get the mean
sum = 0;
for (i=0;i<a.length;i++) sum = sum + a[i];
mean = sum / a.length;
//get the top of the equation
sum2 = 0;
for (i=0;i 1){
//get the mean
sum = 0;
for (i=0;i<a.length;i++) sum = sum + a[i];
mean = sum / a.length;
//get the top of the equation
sum2 = 0;
for (i=0;i0) {
stdDev = (n*sum2-sum*sum)/n;
if (stdDev>0.0)
stdDev = Math.sqrt(stdDev/(n-1.0));
else
stdDev = 0.0;
}
else
stdDev = 0.0;
}