I am having some issues extending a newly created partition.    The idea is that I want the 37.96 GB of Free Space to be added to my DATA (D:) partition, and install Ubuntu on the 25.00 GB unallocated space. I had just installed Ubuntu on my D drive accidentally, so it added an additional 6.xx GB of Linux partition on my D-drive, so the free space should be behind the current data on the D drive (even though it seems like it isn't).  Anyhow, the "Extend Volume" option for my D: isn't available. How do I clean this up?
I want to extend my volume. However the unallocated space lies ahead of the volume. Is it possible to extend this volume without converting it to a dynamic volume?     I tried to use diskpart with the extend command but it doesn't work. Makes sense because the instructions says that the unallocated space has to be behind the volume.
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Given that$(3x-1)^7=a_7x^7+a_6x^6+a_5x^5+...+a_1x+a_0$, find $a_7+a_6+a_5+a_4+...+a_1+a_0$ Given that$$(3x-1)^7=a_7x^7+a_6x^6+a_5x^5+...+a_1x+a_0$$find $$a_7+a_6+a_5+a_4+...+a_1+a_0$$  Is there anyway to do the question without using binomial theorem and expanding the expression on the LHS?? Undergraduate research field different from PhD field When I'm going through a whole bunch of shots to determine which to delete and which to flag, it's fastest for me to just keep both hands on the keyboard.  However, if I want to zoom in to 100% on a picture and then pan to a specific area, it seems I can only get halfway there -- 'Z' to zoom, but then I'm stuck having to pick up the mouse to pan around.  I was excited to find page-up/page-down/home/end can be used, but they seem to exist to provide an exhaustive, linear scrolling of the image, panning top-to-bottom then left-to-right, without unconstrained 2d movement.  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When performing my data analysis, I first tried a multiple linear regression. From this, only two variables were significant. However, when I ran a linear regression comparing each variable individually to $y$, all but one were significant ($p$ anywhere from less than 0.01 to less than 0.001). It was suggested that this was due to multicollinearity.  My initial research on this suggests checking for multicollinearity by using . I downloaded the appropriate package from R, and ended up with the resulting VIFs: 3.35, 3.59, 2.64, 2.24, and 5.56. According to various sources online, the point you should be worried about multicollinearity with your VIFs is either at 4 or 5.   I am now stumped about what this means for my data. Do I or do I not have a multicollinearity problem? If I do, then how should I proceed? 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