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df = 2.74335149908 >>> mean, var, skew, kurt = t.stats(df, moments='mvsk'). Display the probability density function (pdf):. >>> >>> x = np.linspace(t.ppf(0.01, df), t.ppf(0.99, df), 100) >>> ax.plot(x, t.pdf(x, df), 'r-', lw="5", alpha="0".6, label='t pdf'). Alternatively, freeze the distribution and display the frozen pdf: >>>
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t takes df as a shape parameter. The probability density above is defined in the “standardized" form. To shift and/or scale the distribution use the loc and scale parameters. Specifically, t.pdf(x, df, loc, scale) is identically equivalent to t.pdf(y, df) / scale with y = (x - loc) / scale . Examples. >>> >>> from scipy.stats import t
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In probability and statistics, Student's t-distribution is any member of a family of continuous probability distributions that arises when estimating the mean of a normally distributed population in situations where the sample size is small and population standard deviation is unknown. It was developed by William Sealy Gosset
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t Table cum. prob t .50 t .75 t .80 t .85 t .90 t .95 t .975 t .99 t .995 t .999 t .9995 one-tail. 0.50. 0.25. 0.20. 0.15. 0.10. 0.05. 0.025. 0.01. 0.005. 0.001 0.0005 two-tails. 1.00. 0.50. 0.40. 0.30. 0.20. 0.10. 0.05. 0.02. 0.01. 0.002. 0.001 df. 1. 0.000. 1.000. 1.376. 1.963. 3.078. 6.314. 12.71. 31.82. 63.66. 318.31. 636.62. 2. 0.000. 0.816.
y = tpdf(x,nu) returns the probability density function (pdf) of the Student's t distribution at each of the values in x using the corresponding degrees of freedom in nu. The t distribution converges to the standard normal distribution as the degrees of freedom approaches infinity
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