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Mathematical model
log-linear model is a mathematical model that takes the form of a function whose logarithm equals a linear combination of the parameters of the model
Log-linear_model
Class of statistical models
generalized linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to
Generalized_linear_model
Topics referred to by the same term
Log-linear can mean: Log-linear model, in mathematics Log-linear time, in computational complexity This disambiguation page lists articles associated with
Log-linear
2D graphic with logarithmic scales on both axes
Log–log plots are often used for visualizing log-log linear regression models with (roughly) log-normal, or log-logistic, errors. In such models, after
Log–log_plot
Type of statistical model
term linear model refers to any model which assumes linearity in the system. The most common occurrence is in connection with regression models and the
Linear_model
Statistical modeling method
In statistics, linear regression is a model that estimates the relationship between a scalar response (dependent variable) and one or more explanatory
Linear_regression
Statistical linear model
general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that
General_linear_model
Technique used in statistics
{\displaystyle \mathrm {X} ^{2}=} the deviance for the model. There are three assumptions in log-linear analysis: 1. The observations are independent and random;
Log-linear_analysis
Statistical model for count data
value can be modeled by a linear combination of unknown parameters. A Poisson regression model is sometimes known as a log-linear model, especially when
Poisson_regression
Statistical model for a binary dependent variable
In statistics, a logistic model (or logit model) is a statistical model that models the log-odds of an event as a linear combination of one or more independent
Logistic_regression
Linear regression model with a single explanatory variable
In statistics, simple linear regression (SLR) is a linear regression model with a single explanatory variable. That is, it concerns two-dimensional sample
Simple_linear_regression
Regression for more than two discrete outcomes
binary logistic regression as a log-linear model can be directly extended to multi-way regression. That is, we model the logarithm of the probability
Multinomial logistic regression
Multinomial_logistic_regression
Method used in statistics, pattern recognition, and other fields
in that they both look for linear combinations of variables which best explain the data. LDA explicitly attempts to model the difference between the classes
Linear_discriminant_analysis
Statistical test that compares goodness of fit
parameters. Many common test statistics are tests for nested models and can be phrased as log-likelihood ratios or approximations thereof: e.g. the Z-test
Likelihood-ratio_test
Parametric model in survival analysis
the accelerated failure time model to regression analysis (typically a linear model) where − log ( θ ) {\displaystyle -\log(\theta )} represents the fixed
Accelerated failure time model
Accelerated_failure_time_model
Mathematical model used for classification or regression
Linear discriminant analysis (LDA), provides an efficient way of eliminating the disadvantage we list above. As we know, the discriminative model needs
Discriminative_model
N-th root of the product of n numbers
( log 2 1 + log 2 2 + log 2 8 + log 2 16 ) / 4 = 2 ( 0 + 1 + 3 + 4 ) / 4 = 2 2 = 4. {\displaystyle {\sqrt[{4}]{1\cdot 2\cdot 8\cdot 16}}=2^{(\log _{2}\
Geometric_mean
Class of statistical survival models
Olivier (1981). "Covariance Analysis of Censored Survival Data Using Log-Linear Analysis Techniques". Journal of the American Statistical Association
Proportional_hazards_model
Task of selecting a statistical model from a set of candidate models
Analysis Log-linear analysis Model identification Occam's razor Optimal design Parameter identification problem Scientific modelling Statistical model validation
Model_selection
Type of data analysis
produces the following models: Logit models distinguish independent and dependent variables. Unlike logit models, log-linear models do not distinguish between
Multivariate logistic regression
Multivariate_logistic_regression
Statistical technique to aid interpretation of data
changes in an external factor. Linear trend estimation essentially creates a straight line on a graph of data that models the general direction that the
Linear_trend_estimation
Specialized form of regression analysis, in statistics
Google books Dawes, Robyn M. (1979). "The robust beauty of improper linear models in decision making". American Psychologist, volume 34, pages 571-582
Robust_regression
Time series model
conditional heteroskedastic (EGARCH) model by Nelson & Cao (1991) is another form of the GARCH model. Formally, an EGARCH(p,q): log σ t 2 = ω + ∑ k = 1 q β k
Autoregressive conditional heteroskedasticity
Autoregressive_conditional_heteroskedasticity
Number of values in the final calculation of a statistic that are free to vary
the context of linear models (linear regression, analysis of variance), where certain random vectors are constrained to lie in linear subspaces, and the
Degrees of freedom (statistics)
Degrees_of_freedom_(statistics)
Measure of variation in statistics
the case of the log-normal distribution with parameters μ and σ2 for the underlying normal distribution, the standard deviation of the log-normal variable
Standard_deviation
Unit of information
"Evidence of unreliable data and poor data provenance in clinical prediction model research and clinical practice". BMC Medicine. doi:10.1186/s12916-026-04981-y
Data
Value that appears most often in a set of data
concept of median does not apply. The median makes sense when there is a linear order on the possible values. Generalizations of the concept of median to
Mode_(statistics)
Probabilistic problem-solving algorithm
space models". Journal of Computational and Graphical Statistics. 5 (1): 1–25. doi:10.2307/1390750. JSTOR 1390750. Del Moral, Pierre (1996). "Non Linear Filtering:
Monte_Carlo_method
parameterized with data using linear least squares, and subsumes the log-logistic distribution as a special case. The log-normal distribution, describing
List of probability distributions
List_of_probability_distributions
Position that there is no relationship between two phenomena
2019. Zhao, Guolong (18 April 2015). "A Test of Non Null Hypothesis for Linear Trends in Proportions". Communications in Statistics – Theory and Methods
Null_hypothesis
Statistic which divides a data set into 100 parts and analyzes it as a percentage
subscript i, linearly interpolating v between adjacent nodes. There are two ways in which the variant approaches differ. The first is in the linear relationship
Percentile
Statistical hypothesis test
data set in a regression analysis follows the simpler of two proposed linear models that are nested within each other. Multiple-comparison testing is conducted
F-test
Type of chart
in, because the area contained becomes proportional to the square of the linear measures. For example, in a chart with 5 variables that range from 1 to
Radar_chart
Measure of goodness of fit for a statistical model
deviance used in the context of generalized linear modelling, − 2 log [ p ( y ∣ θ ^ 0 ) ] {\displaystyle -2\log {\big [}p(y\mid {\hat {\theta }}_{0}){\big
Deviance_(statistics)
Statistical matching technique
estimation for the propensity score: predicted probability p or the log odds, log[p/(1 − p)]. 2. Match each participant to one or more nonparticipants
Propensity_score_matching
Non-parametric statistic used to estimate the survival function
the log likelihood will be: log ( L ) = ∑ j = 1 i ( d j log ( h j ) + ( n j − d j ) log ( 1 − h j ) + log ( n j d j ) ) {\displaystyle \log({\mathcal
Kaplan–Meier_estimator
Statistical model used in time series analysis
"Recent results for linear time series models with non independent innovations", in Duchesne, P.; Remillard, B. (eds.), Statistical Modeling and Analysis for
Autoregressive moving-average model
Autoregressive_moving-average_model
Statistical relationship
data. It usually refers to the extent to which a pair of quantities are linearly related. More generally, an arbitrary relationship between variables is
Correlation
Statistical property
exhibit heteroscedasticity. One of the assumptions of the classical linear regression model is that there is no heteroscedasticity. Breaking this assumption
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Type of mathematical model
being 1.5 meters tall. We could formalize that relationship in a linear regression model, like this: heighti = b0 + b1agei + εi, where b0 is the intercept
Statistical_model
Relative measure of dispersion expressed as the ratio of standard deviation to the mean
error in the production process). However, data that are linear or even logarithmically non-linear and include a continuous range for the independent variable
Coefficient_of_variation
Statistical test comparing two probability distributions
Ord, Keith; Arnold, Steven [F.] (1999). Classical Inference and the Linear Model. Kendall's Advanced Theory of Statistics. Vol. 2A (Sixth ed.). London:
Kolmogorov–Smirnov_test
Function related to statistics and probability theory
given the model. A logarithm of a likelihood ratio is equal to the difference of the log-likelihoods: log L ( A ) L ( B ) = log L ( A ) − log L ( B
Likelihood_function
Statistical property of collections of time series data
trends). In such cases, the variables may drift in the short run, but their linear combination is stationary, implying that they move together over time and
Cointegration
Distinction between nominal, ordinal, interval and ratio variables
example, applications of measurement models in educational contexts often indicate that total scores have a fairly linear relationship with measurements across
Level_of_measurement
Statistics concept
nonlinear model to the data, as a statistical estimation problem it is linear, in the sense that the regression function E(y | x) is linear in the unknown
Polynomial_regression
Nonparametric measure of rank correlation
Pearson's correlation assesses linear relationships, Spearman's correlation assesses monotonic relationships (whether linear or not). If there are no repeated
Spearman's rank correlation coefficient
Spearman's_rank_correlation_coefficient
Regression analysis
modeling see least squares and non-linear least squares. The assumption underlying this procedure is that the model can be approximated by a linear function
Nonlinear_regression
Statistic for rank correlation
algorithm can be used to compute the numerator in O ( n ⋅ log n ) {\displaystyle O(n\cdot \log {n})} time. Begin by ordering your data points sorting by
Kendall rank correlation coefficient
Kendall_rank_correlation_coefficient
Statistical test
hypothesis; in other words, algebraically equivalent expressions of non-linear parameter restriction can lead to different values of the test statistic
Wald_test
Graphical representation of the distribution of numerical data
performance with non-normal data. k = 1 + log 2 ( n ) + log 2 ( 1 + | g 1 | σ g 1 ) {\displaystyle k=1+\log _{2}(n)+\log _{2}\left(1+{\frac {|g_{1}|}{\sigma
Histogram
Measure of linear correlation
unqualified correlation coefficient, is a correlation coefficient that measures linear correlation between two sets of data. It is the ratio between the covariance
Pearson correlation coefficient
Pearson_correlation_coefficient
Middle quantile of a data set or probability distribution
the model Y = X + Z {\displaystyle Y=X+Z} where Z {\displaystyle Z} is standard normal independent of X {\displaystyle X} , the estimator is linear if
Median
Model for generating observable data in probability and statistics
generative model Energy based model Diffusion model Linear discriminant analysis If the observed data are truly sampled from the generative model, then fitting
Generative_model
Measure of the joint variability
random variables. The sign of the covariance shows the tendency in the linear relationship between the variables. Covariance is positive when variables
Covariance
Family of statistical methods based on sampling of available data
The bootstrap estimate of model prediction bias is more precise than jackknife estimates with linear models such as linear discriminant function or multiple
Resampling_(statistics)
Statistical hypothesis test
the Pearson distribution to model the observation and performing a test of goodness of fit to determine how well the model really fits to the observations
Chi-squared_test
Numerical measure of a statistical relationship between variables
correlation coefficient is a numerical measure of some type of linear correlation, meaning a linear function between two variables. The variables may be two
Correlation_coefficient
Fourth standardized moment in statistics
)^{4}\right].} Assume we sample n = 2 3 + 3 3 κ log 1 δ {\displaystyle n={\tfrac {2{\sqrt {3}}+3}{3}}\kappa \log {\tfrac {1}{\delta }}} many independent copies
Kurtosis
Probability of survival beyond any specified time
survival analysis, including the exponential, Weibull, gamma, normal, log-normal, and log-logistic. These distributions are defined by parameters. The normal
Survival_function
Range to estimate an unknown parameter
distribution (also here) Confidence interval for the parameters of a simple linear regression Confidence interval for the difference of means (based on data
Confidence_interval
Method of statistical inference
Kiona; Tucker, Colin; Cable, Jessica M. (2014-01-01). "Beyond simple linear mixing models: process-based isotope partitioning of ecological processes". Ecological
Bayesian_inference
Statistical hypothesis test for the presence of serial correlation
test Autoregressive-moving-average model Breusch, T. S. (1978). "Testing for Autocorrelation in Dynamic Linear Models". Australian Economic Papers. 17 (31):
Breusch–Godfrey_test
Term in statistical hypothesis testing
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Power_(statistics)
Mathematical relation assigning a probability event to a cost
including t-tests, regression models, design of experiments, and much else, use least squares methods applied using linear regression theory, which is based
Loss_function
Generates a forecast of future values of a time series
Seasonal, Holt's Linear Trend, Brown's Linear Trend, Damped Trend, Winters' Additive, and Winters' Multiplicative in the Time-Series modeling procedure within
Exponential_smoothing
Branch of statistics
to survival estimation. The DeepSurv model proposes to replace the log-linear parameterization of the CoxPH model with a multi-layer perceptron. Further
Survival_analysis
Measure of covariance of components of a random vector
\mathbb {R} ^{n}} Proof Indeed, from the property 4 it follows that under linear transformation of random variable X {\displaystyle \mathbf {X} } with covariation
Covariance_matrix
Statistical method for handling multiple comparisons
{\displaystyle q=5\%} ) may still not be very costly. Controlling the FDR using the linear step-up BH procedure, at level q, has several properties related to the
False_discovery_rate
How many standard deviations apart from the mean an observed datum is
Michael; Nachtsheim, Christopher; Neter, John (204), Applied Linear Regression Models (Fourth ed.), McGraw Hill, ISBN 978-0073014661 {{citation}}: ISBN
Standard_score
Experimental design in statistics
of the Linear Model. Pacific Grove, CA: Wadsworth & Brooks/Cole. ISBN 0-87872-108-8. Hocking, Ronald R. (1985). The Analysis of Linear Models. Pacific
Factorial_experiment
Set of statistical processes for estimating the relationships among variables
estimate the conditional expectation across a broader collection of non-linear models (e.g., nonparametric regression). Regression analysis is primarily used
Regression_analysis
Collection of statistical models
produce a derived linear model, very similar to the textbook model discussed previously. The test statistics of this derived linear model are closely approximated
Analysis_of_variance
Theory and technique of psychological measurement
individuals on nonobservable latent variables are inferred through mathematical modeling based on what is observed from individuals' responses to items on tests
Psychometrics
Bias in causal inference
internal validity. Confounding is defined in terms of the data generating model. Let X be an exposure (or independent variable), and let Y be the outcome
Confounding
Plot using the dispersal of scattered dots to show the relationship between variables
determined by established best-fit procedures. For a linear correlation, the best-fit procedure is known as linear regression and is guaranteed to generate a correct
Scatter_plot
Concept in inferential statistics
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Statistical_significance
Unbiased statistical estimator minimizing variance
− x exp ( − θ log ( 1 + e − x ) + log ( θ ) ) {\displaystyle {\frac {e^{-x}}{1+e^{-x}}}\exp \left(-\theta \log(1+e^{-x})+\log(\theta )\right)}
Minimum-variance unbiased estimator
Minimum-variance_unbiased_estimator
Scientific procedure performed to validate a hypothesis
statistical model that reflects an objective randomization, the statistical analysis relies on a subjective model. Inferences from subjective models are unreliable
Experiment
Criterion for model selection
{\displaystyle k} = the number of parameters estimated by the model. For example, in multiple linear regression, the estimated parameters are the intercept,
Bayesian information criterion
Bayesian_information_criterion
Non-parametric method for testing whether samples originate from the same distribution
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Kruskal–Wallis_test
Probability distribution
confidence intervals for the difference between two population means, and in linear regression analysis. In the form of the location-scale t distribution ℓ
Student's_t-distribution
Statistical measure of how far values spread from their average
S {\displaystyle {\mathit {MS}}} refers to the Mean of the Squares. In linear regression analysis the corresponding formula is M S total = M S regression
Variance
Concept in statistics
of vector generalized linear models (VGLMs) was proposed to enlarge the scope of models catered for by generalized linear models (GLMs). In particular
Vector generalized linear model
Vector_generalized_linear_model
Approximation method in statistics
linear or ordinary least squares and nonlinear least squares, depending on whether or not the model functions are linear in all unknowns. The linear least-squares
Least_squares
Processes that maintain quality at a constant level
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Quality_control
Method of statistical analysis
Bayesian linear regression is a type of conditional modeling in which the mean of one variable is described by a linear combination of other variables
Bayesian_linear_regression
Probability distribution
incidence of outliers in (just) one direction. Generalized normal distribution Log-normal distribution O'Hagan, A.; Leonard, Tom (1976). "Bayes estimation subject
Skew_normal_distribution
Type of statistical measure over subsets of a dataset
applications in image signal processing. In a moving average regression model, a variable of interest is assumed to be a weighted moving average of unobserved
Moving_average
Algorithmically generated data that have a similar distribution as sampled data
constructing a statistical model. In a linear regression line example, the original data can be plotted, and a best fit linear line can be created from
Synthetic_data
Diagnostic plot of binary classifier ability
z-score of an ROC curve is always linear, as assumed, except in special situations. The Yonelinas familiarity-recollection model is a two-dimensional account
Receiver operating characteristic
Receiver_operating_characteristic
Relationship between items in a set
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Ranking
Hypothesis test to compare the survival distributions of two samples
The logrank test, or log-rank test, is a hypothesis test to compare the survival distributions of two samples. It is a nonparametric test and appropriate
Logrank_test
Method of data analysis
linear dimensionality reduction technique with applications in exploratory data analysis, visualization and data preprocessing. The data are linearly
Principal_component_analysis
Statistical methods to build mathematical models of dynamical systems from measured data
the case. Alternatively, the structure or model terms for both linear and highly complex nonlinear models can be identified using NARMAX methods. This
System_identification
Statistical property quantifying how much a collection of data is spread out
location-invariant and linear in scale. This means that if a random variable X {\displaystyle X} has a dispersion of S X {\displaystyle S_{X}} then a linear transformation
Statistical_dispersion
Comparison of two distributions
approximately lie on the identity line y = x. If the distributions are linearly related, the points in the Q–Q plot will approximately lie on a line, but
Q–Q_plot
Condition in which the value of a measurement or observation is only partially known
as follows: ℓ ( λ ) = log ( L ( λ ) ) = k log ( λ ) − λ ∑ i u i . {\displaystyle \ell (\lambda )=\log(L(\lambda ))=k\log(\lambda )-\lambda \sum _{i}u_{i}
Censoring_(statistics)
Type of chart
where zero is a somewhat arbitrary value, and with logarithmic charts where "log(0)" would be infinitely far away. Bar graphs can also be used for more complex
Bar_chart
Function of the observed sample results
a result", and "does not provide a good measure of evidence regarding a model or hypothesis" without "context or other evidence". That said, a 2019 task
P-value
LOG LINEAR-MODEL
LOG LINEAR-MODEL
Surname or Lastname
English
English : habitational name from Lingart, Lancashire, or Lingards Wood in Marsden, West Yorkshire, both named from Old English līn ‘flax’ + garðr ‘enclosure’.
Male
French
 French form of Latin Eligius, ÉLOY means "to choose."
Surname or Lastname
English
English : variant of Lingard.French : occupational name for a maker of or dealer in linen goods, from Old French linge ‘linen (goods)’ (see Linge 1).
Boy/Male
French, German, Polish
Long
Girl/Female
Biblical
The multitude of Gog.
Male
Yiddish
 Variant spelling of Yiddish Lieber, LIBER means "beloved." Compare with another form of Liber.
Female
Scottish
Variant spelling of Scottish Lilias, LILEAS means "lily."
Male
English
Irish Anglicized form of Gaelic Fionnbarr, FINBAR means "fair-headed."
Male
French
French form of Latin Eligius, ÉLOI means "to choose."
Male
Scandinavian
Scandinavian form of Old Norse Einarr, EINAR means "lone warrior."
Surname or Lastname
English and French
English and French : nickname for a tall person, from Old English lang, long, Old French long ‘long’, ‘tall’ (equivalent to Latin longus).Irish (Ulster (Armagh) and Munster) : reduced Anglicized form of Gaelic Ó Longáin (see Langan).Chinese : from the name of an official treasurer called Long, who lived during the reign of the model emperor Shun (2257–2205 bc). his descendants adopted this name as their surname. Additionally, a branch of the Liu clan (see Lau 1), descendants of Liu Lei, who supposedly had the ability to handle dragons, was granted the name Yu-Long (meaning roughly ‘resistor of dragons’) by the Xia emperor Kong Jia (1879–1849 bc). Some descendants later simplified Yu-Long to Long and adopted it as their surname.Chinese : there are two sources for this name. One was a place in the state of Lu in Shandong province during the Spring and Autumn period (722–481 bc). The other source is the Xiongnu nationality, a non-Han Chinese people.Chinese : variant of Lang.Cambodian : unexplained.
Male
Greek
(Λώτ) Greek form of Hebrew Lowt, LOT means "covering, veil." In the bible, this is the name of a nephew of Abraham and father of Moab.
Female
English
Variant spelling of English Linsey, LINSAY means "Lincoln's wetlands."
Male
English
English unisex short form of French Louis and Louise, both LOU means "famous warrior."Â
Female
Spanish
Spanish form of Greek Lois, possibly LOÃDA means "agreeable."
Boy/Male
Hindu
Lingam
Male
English
 English short form of Spanish Alonso, LON means "noble and ready." Compare with another form of Lon.
Male
Greek
(ΑἰνÎας) Variant spelling of Greek AineÃas, AINEAS means "praiseworthy."
Biblical
the multitude of Gog
Surname or Lastname
English
English : metronymic from Line.
LOG LINEAR-MODEL
LOG LINEAR-MODEL
Boy/Male
Hindu, Indian, Malayalam
Young Krishna
Boy/Male
Indian, Punjabi, Sikh
Victorious of the Right
Boy/Male
Tamil
Lord of men leader, Master of men
Surname or Lastname
English
English : variant spelling of Joyce.
Girl/Female
Muslim
Daughter of the Prophet Muhammad.
Boy/Male
Tamil
Best wishes, Offering to God
Boy/Male
Hindu, Indian, Jain, Marathi, Punjabi, Sikh
Revered; Deepness
Boy/Male
Tamil
Shrivatsa | à®·à¯à®°à¯€à®µà®¤à¯à®¸à®¾
Lord Vishnu
Girl/Female
Muslim
Ambitious
Girl/Female
Bengali, Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Sanskrit, Tamil, Telugu
Pure Gold; Beautifully Coloured; Purity of Gold; Moon
LOG LINEAR-MODEL
LOG LINEAR-MODEL
LOG LINEAR-MODEL
LOG LINEAR-MODEL
LOG LINEAR-MODEL
a.
Descending in a direct line from an ancestor; hereditary; derived from ancestors; -- opposed to collateral; as, a lineal descent or a lineal descendant.
a.
Of, pertaining to, or included by, two lines; as, bilinear coordinates.
adv.
In a linear manner; with lines.
a.
Like a line; narrow; of the same breadth throughout, except at the extremities; as, a linear leaf.
v. t.
To enter in a ship's log book; as, to log the miles run.
n.
One who adjusts things to a line or lines or brings them into line.
a.
Composed of lines; delineated; as, lineal designs.
n.
A thin, flat piece of board in the form of a quadrant of a circle attached to the log line; -- called also log-ship. See 2d Log, n., 2.
a.
Of or pertaining to a line; consisting of lines; in a straight direction; lineal.
adv.
In a low position or manner; not aloft; not on high; near the ground.
n.
One who lines, as, a liner of shoes.
n.
Hence: The record of the rate of ship's speed or of her daily progress; also, the full nautical record of a ship's cruise or voyage; a log slate; a log book.
adv.
To a great extent in apace; as, a long drawn out line.
a.
Last; long-delayed; -- obsolete, except in the phrase lag end.
a.
Linear.
v. t.
To mark with a line or lines; to cover with lines; as, to line a copy book.
a.
Of a linear shape.
v. i.
To engage in the business of cutting or transporting logs for timber; to get out logs.
a.
In the direction of a line; of or pertaining to a line; measured on, or ascertained by, a line; linear; as, lineal magnitude.
n.
A part of the log. See Log-chip, and 2d Log, n., 2.