In terms of charting, you may fruitfully interpret "dependent variable" as "y axis". Then take a look at the many closely related questions which have appeared here. I had seen some of those, but not all and I'm working my way through them now. Show 1 more comment. Active Oldest Votes. In pharmacology we use a logarithmic scale for drug concentrations far more often than not, and in many cases linear scales are only the product of non-pharmacologists dabbling with drugs ;- Another good reason for a log scale, probably the one that you are interested in for time-series data, comes from the ability of a log scale to make fractional changes equivalent.
Improve this answer. Michael Lew Michael Lew Can you elaborate on "values can be reasonably expressed as x", though? Say you want to document the ability of a fisherman. You can count the number of fish caught per day or you can measure the interval between successive catches. Either measurement makes sense but they are non-linearly related to each other. They are scaled reciprocals of each other and so can be one-to-one converted to the other. The log of the interval and the log of the number per day are linearly related to each other and differ by a constant negative factor.
I have to admit, it took me a while to sift through all your points and had to google a few terms, like "heteroscedastic variance". I'm still piecing together exactly what the real impact of the answer will mean to my work, but I'm grateful for a general direction and some guidelines to point me along the way.
The being expressed that way more naturally means that many relationships become much easier linear expressions with the log there. Add a comment. Peter Ellis Peter Ellis One follow-up question if you're so inclined , why would you replace points with "hexagonal" bins? Is that the same idea as "Sunflower Plots"? I've not heard that term before. The point is to divide the plotting area into hexagonal bins and then color them eg from light to dark according to how many points are in each bin.
Can be a good way to get around plotting large datasets which otherwise have a tendency just to turn into a mass of black. That's a great way of visualizing the data-its very similar to geographic heat maps that I use.
Did you create that in R? Sign up or log in Sign up using Google. Sign up using Facebook. Sign up using Email and Password. Post as a guest Name. That would be a great return for investors! Yet, nowadays I'd be hard-pressed to find investors touting the first 3 years of Tesla's stock performance.
In [1]:. In [2]:. In [3]:. Input Relationship Output 1 x 1 1 2 x 1 2 3 x 1 3. In [4]:. In [5]:. I attended the University of Michigan for college. In [6]:. DataFrame data.
In [7]:. In [8]:. In [9]:. Look closely at how the scale on the x-axis changed. Real-Life Example: Tesla Inc. In recent years, the Tesla stock has surged upwards despite a lot of volatility. These postings are my own and do not necessarily represent BMC's position, strategies, or opinion. See an error or have a suggestion? Please let us know by emailing blogs bmc. Walker Rowe is an American freelancer tech writer and programmer living in Cyprus.
He is the founder of the Hypatia Academy Cyprus , an online school to teach secondary school children programming. You can find Walker here and here. September 19, 4 minute read. The logarithmic scale in Matplotlib A two-dimensional chart in Matplotlib has a yscale and xscale. They can be any of: matplotlib. LinearScale —These are just numbers, like 1, 2, 3.
LogScale —These are powers of You could use any base, like 2 or the natural logarithm value, which is given by the number e. Using different bases would narrow or widen the spacing of the plotted elements, making visibility easier. SymmetricalLogScale and matplotlib.
LogitScale —These are used for numbers less than 1, in particular very small numbers whose logarithms are very large negative numbers. Series dg. Learn ML with our free downloadable guide This e-book teaches machine learning in the simplest way possible.
0コメント