• 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
    • Theoretical Aspects of Hypothesis Testing
    • Bayesian Inference
    • Minimum Sample Size

    • Empirical Tests
    • Multivariate (pair-wise) Testing
  • Models
    • Manual Model Building
    • Guided Model Building
  • Time Series
    • Time Series Plot
    • Decomposition
    • Exponential Smoothing

    • Blocked Bootstrap Plot
    • Mean Plot
    • (P)ACF
    • VRM
    • Standard Deviation-Mean Plot
    • Spectral Analysis
    • ARIMA

    • Cross Correlation Function
    • Granger Causality
  1. Appendices
  2. H  Lower-tail critical values of the \(\chi^2\)-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. H  Lower-tail critical values of the \(\chi^2\)-distribution with \(\nu\) degrees of freedom

Appendix H — Lower-tail critical values of the \(\chi^2\)-distribution with \(\nu\) degrees of freedom

The following table displays the lower-tail critical values \(c\) for the \(\chi^2\)-distribution with \(\nu\) degrees of freedom and a significance level \(\alpha\): P\((0 \leq X \leq c) = \alpha\).

Probability less than the critical value (source: NIST/SEMATECH (n.d.), URL: web page) Values rounded to 3 decimal places; 0.000 denotes a value less than 0.0005.
\(\nu\) 0.10 0.05 0.025 0.01 0.001
1 0.016 0.004 0.001 0.000 0.000
2 0.211 0.103 0.051 0.020 0.002
3 0.584 0.352 0.216 0.115 0.024
4 1.064 0.711 0.484 0.297 0.091
5 1.610 1.145 0.831 0.554 0.210
6 2.204 1.635 1.237 0.872 0.381
7 2.833 2.167 1.690 1.239 0.598
8 3.490 2.733 2.180 1.646 0.857
9 4.168 3.325 2.700 2.088 1.152
10 4.865 3.940 3.247 2.558 1.479
11 5.578 4.575 3.816 3.053 1.834
12 6.304 5.226 4.404 3.571 2.214
13 7.042 5.892 5.009 4.107 2.617
14 7.790 6.571 5.629 4.660 3.041
15 8.547 7.261 6.262 5.229 3.483
16 9.312 7.962 6.908 5.812 3.942
17 10.085 8.672 7.564 6.408 4.416
18 10.865 9.390 8.231 7.015 4.905
19 11.651 10.117 8.907 7.633 5.407
20 12.443 10.851 9.591 8.260 5.921
21 13.240 11.591 10.283 8.897 6.447
22 14.041 12.338 10.982 9.542 6.983
23 14.848 13.091 11.689 10.196 7.529
24 15.659 13.848 12.401 10.856 8.085
25 16.473 14.611 13.120 11.524 8.649
26 17.292 15.379 13.844 12.198 9.222
27 18.114 16.151 14.573 12.879 9.803
28 18.939 16.928 15.308 13.565 10.391
29 19.768 17.708 16.047 14.256 10.986
30 20.599 18.493 16.791 14.953 11.588
31 21.434 19.281 17.539 15.655 12.196
32 22.271 20.072 18.291 16.362 12.811
33 23.110 20.867 19.047 17.074 13.431
34 23.952 21.664 19.806 17.789 14.057
35 24.797 22.465 20.569 18.509 14.688
36 25.643 23.269 21.336 19.233 15.324
37 26.492 24.075 22.106 19.960 15.965
38 27.343 24.884 22.878 20.691 16.611
39 28.196 25.695 23.654 21.426 17.262
40 29.051 26.509 24.433 22.164 17.916
41 29.907 27.326 25.215 22.906 18.575
42 30.765 28.144 25.999 23.650 19.239
43 31.625 28.965 26.785 24.398 19.906
44 32.487 29.787 27.575 25.148 20.576
45 33.350 30.612 28.366 25.901 21.251
46 34.215 31.439 29.160 26.657 21.929
47 35.081 32.268 29.956 27.416 22.610
48 35.949 33.098 30.755 28.177 23.295
49 36.818 33.930 31.555 28.941 23.983
50 37.689 34.764 32.357 29.707 24.674
51 38.560 35.600 33.162 30.475 25.368
52 39.433 36.437 33.968 31.246 26.065
53 40.308 37.276 34.776 32.018 26.765
54 41.183 38.116 35.586 32.793 27.468
55 42.060 38.958 36.398 33.570 28.173
56 42.937 39.801 37.212 34.350 28.881
57 43.816 40.646 38.027 35.131 29.592
58 44.696 41.492 38.844 35.913 30.305
59 45.577 42.339 39.662 36.698 31.020
60 46.459 43.188 40.482 37.485 31.738
61 47.342 44.038 41.303 38.273 32.459
62 48.226 44.889 42.126 39.063 33.181
63 49.111 45.741 42.950 39.855 33.906
64 49.996 46.595 43.776 40.649 34.633
65 50.883 47.450 44.603 41.444 35.362
66 51.770 48.305 45.431 42.240 36.093
67 52.659 49.162 46.261 43.038 36.826
68 53.548 50.020 47.092 43.838 37.561
69 54.438 50.879 47.924 44.639 38.298
70 55.329 51.739 48.758 45.442 39.036
71 56.221 52.600 49.592 46.246 39.777
72 57.113 53.462 50.428 47.051 40.519
73 58.006 54.325 51.265 47.858 41.264
74 58.900 55.189 52.103 48.666 42.010
75 59.795 56.054 52.942 49.475 42.757
76 60.690 56.920 53.782 50.286 43.507
77 61.586 57.786 54.623 51.097 44.258
78 62.483 58.654 55.466 51.910 45.010
79 63.380 59.522 56.309 52.725 45.764
80 64.278 60.391 57.153 53.540 46.520
81 65.176 61.261 57.998 54.357 47.277
82 66.076 62.132 58.845 55.174 48.036
83 66.976 63.004 59.692 55.993 48.796
84 67.876 63.876 60.540 56.813 49.557
85 68.777 64.749 61.389 57.634 50.320
86 69.679 65.623 62.239 58.456 51.085
87 70.581 66.498 63.089 59.279 51.850
88 71.484 67.373 63.941 60.103 52.617
89 72.387 68.249 64.793 60.928 53.386
90 73.291 69.126 65.647 61.754 54.155
91 74.196 70.003 66.501 62.581 54.926
92 75.100 70.882 67.356 63.409 55.698
93 76.006 71.760 68.211 64.238 56.472
94 76.912 72.640 69.068 65.068 57.246
95 77.818 73.520 69.925 65.898 58.022
96 78.725 74.401 70.783 66.730 58.799
97 79.633 75.282 71.642 67.562 59.577
98 80.541 76.164 72.501 68.396 60.356
99 81.449 77.046 73.361 69.230 61.137
100 82.358 77.929 74.222 70.065 61.918
NIST/SEMATECH. n.d. E-Handbook of Statistical Methods. NIST/SEMATECH. http://www.itl.nist.gov/div898/handbook/.
G  Upper-tail critical values of the \(\chi^2\)-distribution with \(\nu\) degrees of freedom

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