Physics 203 at Portland State 2014

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white_noise_project [2014/05/24 00:49]
wikimanager [Software and data analysis]
white_noise_project [2014/06/04 03:46] (current)
wikimanager [Software and data analysis]
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* $n_i$ is the observed (or predicted) number of occurrences in the //​i//<​sup>​ th</​sup>​ bin           * $n_i$ is the observed (or predicted) number of occurrences in the //​i//<​sup>​ th</​sup>​ bin
* $N$ is the total number of observations           * $N$ is the total number of observations
-    * //<color red>​**Question:**</​color>//:​ I have the two histograms plotted. ​ For the quantitative comparison should I calculate the probability density for each bin?  for the exercise with $N=200$ would this be done by using the following equation: ${\text{probability density}}=$ $\frac{\text{#​ observations in bin}}{200\times 0.3}$ ?  I am having a hard time understanding if we need to calculate the predicted number in each bin.  Should I do this?  If so, how would I figure out how to do this? +    * //<color red>​**Question**</​color>//:​ I have the two histograms plotted. ​ For the quantitative comparison should I calculate the probability density for each bin?  for the exercise with $N=200$ would this be done by using the following equation: ${\text{probability density}}=$ $\frac{\text{#​ observations in bin}}{200\times 0.3}$ ?  I am having a hard time understanding if we need to calculate the predicted number in each bin.  Should I do this?  If so, how would I figure out how to do this?
* //​**Answer**//: ​ to compare "​experiment"​ with theory, you need either to convert your bin counts to the probability density (by dividing the counts by the total # of observations and by the bin width), and compare that to the theoretical curve, or, alternatively,​ convert the theoretical probability density to the predicted bin count, that is by multiplying the theoretical probability density by the total # of observations (200) and by the width of your bins (I guess, 0.3).  Then plot the two curves on the same plot, the theoretical curve using lines and the experimental bin counts using dots or other symbols. ​ --- //​[[nkuzma@pdx.edu|Nicholas Kuzma]] 2014/05/23 17:47//       * //​**Answer**//: ​ to compare "​experiment"​ with theory, you need either to convert your bin counts to the probability density (by dividing the counts by the total # of observations and by the bin width), and compare that to the theoretical curve, or, alternatively,​ convert the theoretical probability density to the predicted bin count, that is by multiplying the theoretical probability density by the total # of observations (200) and by the width of your bins (I guess, 0.3).  Then plot the two curves on the same plot, the theoretical curve using lines and the experimental bin counts using dots or other symbols. ​ --- //​[[nkuzma@pdx.edu|Nicholas Kuzma]] 2014/05/23 17:47//
- Save the exercises above for the "​Intro",​ "​Theory",​ and "​Methods"​ sections of your report   - Save the exercises above for the "​Intro",​ "​Theory",​ and "​Methods"​ sections of your report
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* Switch to the editing software (e.g. Word or Pages), and paste at the desired spot         * Switch to the editing software (e.g. Word or Pages), and paste at the desired spot
* on a PC, press ''​Alt''​ and ''​PrtScn''​ ("​Print Screen"​) at the same time, then release       * on a PC, press ''​Alt''​ and ''​PrtScn''​ ("​Print Screen"​) at the same time, then release
-        * Switch to the editing software (e.g. WordPowerpoint, or Paint)+        * Switch to the editing software (e.g. WordPowerpoint, or Paint)
* Paste at the desired spot         * Paste at the desired spot
* Crop the excessive margins as needed         * Crop the excessive margins as needed

| {{ :​projects:​noise:​noisefig5.png?​nolink |...}} | | {{ :​projects:​noise:​noisefig5.png?​nolink |...}} |
-^ Figure 5. Examples of histogram plots in Excel   ​+^ Figure 5. Examples of histogram plots in Excel.  ​^
+| In the top figure, the theoretical curve has been scaled (multiplied by $N\Delta x$) to yield the predicted numbers of counts in each bin.  |

-
This is the detailed list of tasks to be accomplished:​ This is the detailed list of tasks to be accomplished:​
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* Try to offer your intuition as to why adding (averaging) several pure sine waves results in such a drastically different histogram         * Try to offer your intuition as to why adding (averaging) several pure sine waves results in such a drastically different histogram
* If intrigued, try to experiment with fewer than 200 sine waves. At what point does the dramatic change of the histogram happen?         * If intrigued, try to experiment with fewer than 200 sine waves. At what point does the dramatic change of the histogram happen?
+    * //<color red>​**Question**</​color>//:​ I need to figure out what is wrong with my frequency graph (average of 200 sine waves). I have re-plotted it again, checked my formulae, and I still am unable to find any error.  ​
+      * //​**Answer**//:​ I looked at your file, and the data is actually correct. The problem is with your plot.  Do you know how to change the axis range? As it is now, you are "​zoomed in" too much on your figure: the //x// axis is only from 0.1 to 0.3 s somehow, and the //y// axis is from $-0.02$ to $+0.02$. Basically it is blowing up a tiny little aspect of the plot, and not showing the whole picture. ​
+        - Select your plot.
+        - On the "​ribbon"​ in excel, select "Chart Layout"​
+        - Then click the box "​Axes",​ "​Horizontal axis", "Axis options"​
+        - Make sure the first two boxes ("​minimum"​ and "​maximum"​) are checked, to stretch the scale over the whole range of your data
+        - Click OK
+        - Do the same for the vertical axis.
+        - Your plot will be showing the whole data then, and it will be correct.
- Finally, generate a number (you decide how many) of pure sine waves with different amplitudes and frequencies. ​   - Finally, generate a number (you decide how many) of pure sine waves with different amplitudes and frequencies. ​
* Try random frequencies and/or random amplitudes/​phases (using a scaled version of the ''​RAND()''​ function).     * Try random frequencies and/or random amplitudes/​phases (using a scaled version of the ''​RAND()''​ function).
white_noise_project.1400892569.txt.gz · Last modified: 2014/05/24 00:49 (external edit)