Basic Stats Tools
"All The Mathematical Methods I Learned In My University Math Degree Became Obsolete In My Lifetime" - Keith Devlin
Basic Data
Statistics:
Enter the data into the list L. Click on the appropriate folder on the left to see the corresponding values: (https://www.desmos.com/calculator/cm8mh2cbqd)
Expected Value and Variance for Probability Distributions: By entering the values of a random variable X in the xi column and the respective probabilities in the pi column, one can see the expected values of the distribution E, as well as it's variance V. (Link https://www.desmos.com/calculator/0h4rkfo2dh)
Linear Regression: Enter the data in the given table. The regression analysis gives you r, r^2, the slope b_1, and the y-intercept b_0. (Link https://www.desmos.com/calculator/h4ne0ifuif)
Reiterated Sampling Visualizer: Enter in any Discrete Probability distribution into P, then let n be the number of times we sample P, and let nr_samples be the number of times we record these sums. Then the following displays the distribution of those sums. Notice that as n increases, the curve both shrinks in relative width, and approaches normalacy.
Bayes
Theorem:
Enter in P(X|Y), P(X|Y^c) and P(Y) and this will compute P(X) and all
the possible "and" events. Additionally, if you know what P(X)
is, you can enter that as X_Target and find the P(Y) which yields that
value: (https://www.desmos.com/calculator/ybbhdvruoj)
Expected Value and Variance for Probability Distributions: By entering the values of a random variable X in the xi column and the respective probabilities in the pi column, one can see the expected values of the distribution E, as well as it's variance V. (Link https://www.desmos.com/calculator/0h4rkfo2dh)
Linear Regression: Enter the data in the given table. The regression analysis gives you r, r^2, the slope b_1, and the y-intercept b_0. (Link https://www.desmos.com/calculator/h4ne0ifuif)
Reiterated Sampling Visualizer: Enter in any Discrete Probability distribution into P, then let n be the number of times we sample P, and let nr_samples be the number of times we record these sums. Then the following displays the distribution of those sums. Notice that as n increases, the curve both shrinks in relative width, and approaches normalacy.
xxxxxxxxxxP = [0, 1/6, 1/6, 1/6, 1/6, 1/6, 1/6] #Enter a Discrete Probability Distributionnr_samples = 1000. #Number of times we sample the mean (bar{X}) distribution n=1 #of reiterated samplesX=GeneralDiscreteDistribution(P)sumcounts = [0] * (len(P)*n-n+1)for i in range(nr_samples): Z=0 for j in range(n): Z+=X.get_random_element() sumcounts[Z]+=1bar_chart(sumcounts)