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A paired t-test is used to compare two population means where you have two samples in which observations in one sample can be paired with observations in the other sample. Examples of where this might occur are: • Before-and-after observations on the same subjects (e.g. students' diagnostic test results before and
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
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: >>>
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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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
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.
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