• Descriptive
    • Moments
    • Concentration
    • Central Tendency
    • Variability
    • Stem-and-Leaf Plot
    • Histogram & Frequency Table
    • Data Quality Forensics
    • Conditional EDA
    • Quantiles
    • Kernel Density Estimation
    • Normal QQ Plot
    • Bootstrap Plot

    • Multivariate Descriptive Statistics
  • Distributions
    • Binomial Probabilities
    • Geometric Probabilities
    • Negative Binomial Probabilities
    • Hypergeometric Probabilities
    • Multinomial Probabilities
    • Dirichlet
    • Poisson Probabilities

    • Exponential
    • Gamma
    • Erlang
    • Weibull
    • Rayleigh
    • Maxwell-Boltzmann
    • Lognormal
    • Pareto
    • Inverse Gamma
    • Inverse Chi-Square

    • Beta
    • Power
    • Beta Prime (Inv. Beta)
    • Triangular

    • Normal (area)
    • Logistic
    • Laplace
    • Cauchy (standard)
    • Cauchy (location-scale)
    • Gumbel
    • Fréchet
    • Generalized Extreme Value

    • Normal RNG
    • ML Fitting
    • Tukey Lambda PPCC
    • Box-Cox Normality Plot
    • Noncentral t
    • Noncentral F
    • Sample Correlation r

    • Empirical Tests
  • Hypotheses
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    • Bayesian Inference
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    • Empirical Tests
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  • Models
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  • Time Series
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    • Blocked Bootstrap Plot
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    • VRM
    • Standard Deviation-Mean Plot
    • Spectral Analysis
    • ARIMA

    • Cross Correlation Function
    • Granger Causality
  1. Appendices
  2. F  Critical values of Student’s \(t\) distribution with \(\nu\) degrees of freedom
  • Preface
  • Getting Started
    • 1  Introduction
    • 2  Why Do We Need Innovative Technology?
    • 3  Basic Definitions
    • 4  The Big Picture: Why We Analyze Data
  • Introduction to Probability
    • 5  Definitions of Probability
    • 6  Jeffreys’ axiom system
    • 7  Bayes’ Theorem
    • 8  Sensitivity and Specificity
    • 9  Naive Bayes Classifier
    • 10  Law of Large Numbers

    • 11  Problems
  • Probability Distributions
    • 12  Bernoulli Distribution
    • 13  Binomial Distribution
    • 14  Geometric Distribution
    • 15  Negative Binomial Distribution
    • 16  Hypergeometric Distribution
    • 17  Multinomial Distribution
    • 18  Poisson Distribution

    • 19  Uniform Distribution (Rectangular Distribution)
    • 20  Normal Distribution (Gaussian Distribution)
    • 21  Gaussian Naive Bayes Classifier
    • 22  Chi Distribution
    • 23  Chi-squared Distribution (1 parameter)
    • 24  Chi-squared Distribution (2 parameters)
    • 25  Student t-Distribution
    • 26  Fisher F-Distribution
    • 27  Exponential Distribution
    • 28  Lognormal Distribution
    • 29  Gamma Distribution
    • 30  Beta Distribution
    • 31  Weibull Distribution
    • 32  Pareto Distribution
    • 33  Inverse Gamma Distribution
    • 34  Rayleigh Distribution
    • 35  Erlang Distribution
    • 36  Logistic Distribution
    • 37  Laplace Distribution
    • 38  Gumbel Distribution
    • 39  Cauchy Distribution
    • 40  Triangular Distribution
    • 41  Power Distribution
    • 42  Beta Prime Distribution
    • 43  Sample Correlation Distribution
    • 44  Dirichlet Distribution
    • 45  Generalized Extreme Value (GEV) Distribution
    • 46  Frechet Distribution
    • 47  Noncentral t Distribution
    • 48  Noncentral F Distribution
    • 49  Inverse Chi-Squared Distribution
    • 50  Maxwell-Boltzmann Distribution
    • 51  Distribution Relationship Map

    • 52  Problems
  • Descriptive Statistics & Exploratory Data Analysis
    • 53  Types of Data
    • 54  Datasheets

    • 55  Frequency Plot (Bar Plot)
    • 56  Frequency Table
    • 57  Contingency Table
    • 58  Binomial Classification Metrics
    • 59  Confusion Matrix
    • 60  ROC Analysis

    • 61  Stem-and-Leaf Plot
    • 62  Histogram
    • 63  Data Quality Forensics
    • 64  Quantiles
    • 65  Central Tendency
    • 66  Variability
    • 67  Skewness & Kurtosis
    • 68  Concentration
    • 69  Notched Boxplot
    • 70  Scatterplot
    • 71  Pearson Correlation
    • 72  Rank Correlation
    • 73  Partial Pearson Correlation
    • 74  Simple Linear Regression
    • 75  Moments
    • 76  Quantile-Quantile Plot (QQ Plot)
    • 77  Normal Probability Plot
    • 78  Probability Plot Correlation Coefficient Plot (PPCC Plot)
    • 79  Box-Cox Normality Plot
    • 80  Kernel Density Estimation
    • 81  Bivariate Kernel Density Plot
    • 82  Conditional EDA: Panel Diagnostics
    • 83  Bootstrap Plot (Central Tendency)
    • 84  Survey Scores Rank Order Comparison
    • 85  Cronbach Alpha

    • 86  Equi-distant Time Series
    • 87  Time Series Plot (Run Sequence Plot)
    • 88  Mean Plot
    • 89  Blocked Bootstrap Plot (Central Tendency)
    • 90  Standard Deviation-Mean Plot
    • 91  Variance Reduction Matrix
    • 92  (Partial) Autocorrelation Function
    • 93  Periodogram & Cumulative Periodogram

    • 94  Problems
  • Hypothesis Testing
    • 95  Normal Distributions revisited
    • 96  The Population
    • 97  The Sample
    • 98  The One-Sided Hypothesis Test
    • 99  The Two-Sided Hypothesis Test
    • 100  When to use a one-sided or two-sided test?
    • 101  What if \(\sigma\) is unknown?
    • 102  The Central Limit Theorem (revisited)
    • 103  Statistical Test of the Population Mean with known Variance
    • 104  Statistical Test of the Population Mean with unknown Variance
    • 105  Statistical Test of the Variance
    • 106  Statistical Test of the Population Proportion
    • 107  Statistical Test of the Standard Deviation \(\sigma\)
    • 108  Statistical Test of the difference between Means -- Independent/Unpaired Samples
    • 109  Statistical Test of the difference between Means -- Dependent/Paired Samples
    • 110  Statistical Test of the difference between Variances -- Independent/Unpaired Samples

    • 111  Hypothesis Testing for Research Purposes
    • 112  Decision Thresholds, Alpha, and Confidence Levels
    • 113  Bayesian Inference for Decision-Making
    • 114  One Sample t-Test
    • 115  Skewness & Kurtosis Tests
    • 116  Paired Two Sample t-Test
    • 117  Wilcoxon Signed-Rank Test
    • 118  Unpaired Two Sample t-Test
    • 119  Unpaired Two Sample Welch Test
    • 120  Two One-Sided Tests (TOST) for Equivalence
    • 121  Mann-Whitney U test (Wilcoxon Rank-Sum Test)
    • 122  Bayesian Two Sample Test
    • 123  Median Test based on Notched Boxplots
    • 124  Chi-Squared Tests for Count Data
    • 125  Kolmogorov-Smirnov Test
    • 126  One Way Analysis of Variance (1-way ANOVA)
    • 127  Kruskal-Wallis Test
    • 128  Two Way Analysis of Variance (2-way ANOVA)
    • 129  Repeated Measures ANOVA
    • 130  Friedman Test
    • 131  Testing Correlations
    • 132  A Note on Causality

    • 133  Problems
  • Regression Models
    • 134  Simple Linear Regression Model (SLRM)
    • 135  Multiple Linear Regression Model (MLRM)
    • 136  Logistic Regression
    • 137  Generalized Linear Models
    • 138  Multinomial and Ordinal Logistic Regression
    • 139  Cox Proportional Hazards Regression
    • 140  Conditional Inference Trees
    • 141  Leaf Diagnostics for Conditional Inference Trees
    • 142  Conditional Random Forests
    • 143  Hypothesis Testing with Linear Regression Models (from a Practical Point of View)

    • 144  Problems
  • Introduction to Time Series Analysis
    • 145  Case: the Market of Health and Personal Care Products
    • 146  Decomposition of Time Series
    • 147  Ad hoc Forecasting of Time Series
  • Box-Jenkins Analysis
    • 148  Introduction to Box-Jenkins Analysis
    • 149  Theoretical Concepts
    • 150  Stationarity
    • 151  Identifying ARMA parameters
    • 152  Estimating ARMA Parameters and Residual Diagnostics
    • 153  Forecasting with ARIMA models
    • 154  Intervention Analysis
    • 155  Cross-Correlation Function
    • 156  Transfer Function Noise Models
    • 157  General-to-Specific Modeling
  • Model Building Strategies
    • 158  Introduction to Model Building Strategies
    • 159  Manual Model Building
    • 160  Model Validation
    • 161  Regularization Methods
    • 162  Hyperparameter Optimization Strategies
    • 163  Guided Model Building in Practice
    • 164  Diagnostics, Revision, and Guided Forecasting
    • 165  Leakage, Target Encoding, and Robust Regression
  • References
  • Appendices
    • Appendices
    • A  Method Selection Guide
    • B  Presentations and Teaching Materials
    • C  R Language Concepts for Statistical Computing
    • D  Matrix Algebra
    • E  Standard Normal Table (Gaussian Table)
    • F  Critical values of Student’s \(t\) distribution with \(\nu\) degrees of freedom
    • G  Upper-tail critical values of the \(\chi^2\)-distribution with \(\nu\) degrees of freedom
    • H  Lower-tail critical values of the \(\chi^2\)-distribution with \(\nu\) degrees of freedom
  1. Appendices
  2. F  Critical values of Student’s \(t\) distribution with \(\nu\) degrees of freedom

Appendix F — Critical values of Student’s \(t\) distribution with \(\nu\) degrees of freedom

The following table displays the critical values \(|c|\), where \(c = t_{1-\alpha, \nu}\) is a quantile of the Student \(t\) distribution with \(\nu\) degrees of freedom and a significance level \(\alpha\): P\((X \leq c) = 1 - \alpha\).

For a symmetric (i.e. two-sided) interval we use the column corresponding to \(1 - \alpha/2\) and the row which is equal to \(\nu = N - K\) (where \(K\) is the number of parameters of the model; in a pooled two-sample \(t\) test, \(\nu = N_1 + N_2 - 2\); Welch’s \(t\) test uses the Welch-Satterthwaite formula for \(\nu\)). For a one-sided test or interval, use the column corresponding to \(1 - \alpha\) directly. For instance, if \(N\) is very large (i.e. \(N - K > 100\), which for the sample mean with \(K = 1\) means \(N > 101\)), the 0.975 column at \(\nu = 100\) gives 1.984, while 1.960 is the limiting value at \(\nu = \infty\) (the Normal Distribution).

Table F.1: Probability less than the critical value \(\left( t_{1-\alpha, \nu} \right)\) – (source: NIST/SEMATECH (n.d.); URL: web page )
\(\nu\) 0.90 0.95 0.975 0.99 0.995 0.999
1 3.078 6.314 12.706 31.821 63.657 318.313
2 1.886 2.920 4.303 6.965 9.925 22.327
3 1.638 2.353 3.182 4.541 5.841 10.215
4 1.533 2.132 2.776 3.747 4.604 7.173
5 1.476 2.015 2.571 3.365 4.032 5.893
6 1.440 1.943 2.447 3.143 3.707 5.208
7 1.415 1.895 2.365 2.998 3.499 4.782
8 1.397 1.860 2.306 2.896 3.355 4.499
9 1.383 1.833 2.262 2.821 3.250 4.296
10 1.372 1.812 2.228 2.764 3.169 4.143
11 1.363 1.796 2.201 2.718 3.106 4.024
12 1.356 1.782 2.179 2.681 3.055 3.929
13 1.350 1.771 2.160 2.650 3.012 3.852
14 1.345 1.761 2.145 2.624 2.977 3.787
15 1.341 1.753 2.131 2.602 2.947 3.733
16 1.337 1.746 2.120 2.583 2.921 3.686
17 1.333 1.740 2.110 2.567 2.898 3.646
18 1.330 1.734 2.101 2.552 2.878 3.610
19 1.328 1.729 2.093 2.539 2.861 3.579
20 1.325 1.725 2.086 2.528 2.845 3.552
21 1.323 1.721 2.080 2.518 2.831 3.527
22 1.321 1.717 2.074 2.508 2.819 3.505
23 1.319 1.714 2.069 2.500 2.807 3.485
24 1.318 1.711 2.064 2.492 2.797 3.467
25 1.316 1.708 2.060 2.485 2.787 3.450
26 1.315 1.706 2.056 2.479 2.779 3.435
27 1.314 1.703 2.052 2.473 2.771 3.421
28 1.313 1.701 2.048 2.467 2.763 3.408
29 1.311 1.699 2.045 2.462 2.756 3.396
30 1.310 1.697 2.042 2.457 2.750 3.385
31 1.309 1.696 2.040 2.453 2.744 3.375
32 1.309 1.694 2.037 2.449 2.738 3.365
33 1.308 1.692 2.035 2.445 2.733 3.356
34 1.307 1.691 2.032 2.441 2.728 3.348
35 1.306 1.690 2.030 2.438 2.724 3.340
36 1.306 1.688 2.028 2.434 2.719 3.333
37 1.305 1.687 2.026 2.431 2.715 3.326
38 1.304 1.686 2.024 2.429 2.712 3.319
39 1.304 1.685 2.023 2.426 2.708 3.313
40 1.303 1.684 2.021 2.423 2.704 3.307
41 1.303 1.683 2.020 2.421 2.701 3.301
42 1.302 1.682 2.018 2.418 2.698 3.296
43 1.302 1.681 2.017 2.416 2.695 3.291
44 1.301 1.680 2.015 2.414 2.692 3.286
45 1.301 1.679 2.014 2.412 2.690 3.281
46 1.300 1.679 2.013 2.410 2.687 3.277
47 1.300 1.678 2.012 2.408 2.685 3.273
48 1.299 1.677 2.011 2.407 2.682 3.269
49 1.299 1.677 2.010 2.405 2.680 3.265
50 1.299 1.676 2.009 2.403 2.678 3.261
51 1.298 1.675 2.008 2.402 2.676 3.258
52 1.298 1.675 2.007 2.400 2.674 3.255
53 1.298 1.674 2.006 2.399 2.672 3.251
54 1.297 1.674 2.005 2.397 2.670 3.248
55 1.297 1.673 2.004 2.396 2.668 3.245
56 1.297 1.673 2.003 2.395 2.667 3.242
57 1.297 1.672 2.002 2.394 2.665 3.239
58 1.296 1.672 2.002 2.392 2.663 3.237
59 1.296 1.671 2.001 2.391 2.662 3.234
60 1.296 1.671 2.000 2.390 2.660 3.232
61 1.296 1.670 2.000 2.389 2.659 3.229
62 1.295 1.670 1.999 2.388 2.657 3.227
63 1.295 1.669 1.998 2.387 2.656 3.225
64 1.295 1.669 1.998 2.386 2.655 3.223
65 1.295 1.669 1.997 2.385 2.654 3.220
66 1.295 1.668 1.997 2.384 2.652 3.218
67 1.294 1.668 1.996 2.383 2.651 3.216
68 1.294 1.668 1.995 2.382 2.650 3.214
69 1.294 1.667 1.995 2.382 2.649 3.213
70 1.294 1.667 1.994 2.381 2.648 3.211
71 1.294 1.667 1.994 2.380 2.647 3.209
72 1.293 1.666 1.993 2.379 2.646 3.207
73 1.293 1.666 1.993 2.379 2.645 3.206
74 1.293 1.666 1.993 2.378 2.644 3.204
75 1.293 1.665 1.992 2.377 2.643 3.202
76 1.293 1.665 1.992 2.376 2.642 3.201
77 1.293 1.665 1.991 2.376 2.641 3.199
78 1.292 1.665 1.991 2.375 2.640 3.198
79 1.292 1.664 1.990 2.374 2.640 3.197
80 1.292 1.664 1.990 2.374 2.639 3.195
81 1.292 1.664 1.990 2.373 2.638 3.194
82 1.292 1.664 1.989 2.373 2.637 3.193
83 1.292 1.663 1.989 2.372 2.636 3.191
84 1.292 1.663 1.989 2.372 2.636 3.190
85 1.292 1.663 1.988 2.371 2.635 3.189
86 1.291 1.663 1.988 2.370 2.634 3.188
87 1.291 1.663 1.988 2.370 2.634 3.187
88 1.291 1.662 1.987 2.369 2.633 3.185
89 1.291 1.662 1.987 2.369 2.632 3.184
90 1.291 1.662 1.987 2.368 2.632 3.183
91 1.291 1.662 1.986 2.368 2.631 3.182
92 1.291 1.662 1.986 2.368 2.630 3.181
93 1.291 1.661 1.986 2.367 2.630 3.180
94 1.291 1.661 1.986 2.367 2.629 3.179
95 1.291 1.661 1.985 2.366 2.629 3.178
96 1.290 1.661 1.985 2.366 2.628 3.177
97 1.290 1.661 1.985 2.365 2.627 3.176
98 1.290 1.661 1.984 2.365 2.627 3.175
99 1.290 1.660 1.984 2.365 2.626 3.175
100 1.290 1.660 1.984 2.364 2.626 3.174
\(\infty\) 1.282 1.645 1.960 2.326 2.576 3.090
NIST/SEMATECH. n.d. E-Handbook of Statistical Methods. NIST/SEMATECH. http://www.itl.nist.gov/div898/handbook/.
E  Standard Normal Table (Gaussian Table)
G  Upper-tail critical values of the \(\chi^2\)-distribution with \(\nu\) degrees of freedom

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