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Type of graph
log ( y ) = ( γ log ( a ) ) x + log ( λ ) . {\displaystyle \log(y)=(\gamma \log(a))x+\log(\lambda ).} A log–linear (sometimes log–lin) plot has the
Semi-log_plot
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
k log x + log a . {\displaystyle \log y=k\log x+\log a.} Setting X = log x {\displaystyle X=\log x} and Y = log y , {\displaystyle Y=\log y,}
Log–log_plot
Mathematical model
A 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
Technique used in statistics
Log-linear analysis is a technique used in statistics to examine the relationship between more than two categorical variables. The technique is used for
Log-linear_analysis
Class of statistical models
log(μ) be a linear model. This produces the "cloglog" transformation log ( − log ( 1 − p ) ) = log ( μ ) . {\displaystyle \log(-\log(1-p))=\log(\mu
Generalized_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
Method used in statistics, pattern recognition, and other fields
Linear discriminant analysis (LDA), normal discriminant analysis (NDA), canonical variates analysis (CVA), or discriminant function analysis is a generalization
Linear_discriminant_analysis
Type of data analysis
distinguish independent and dependent variables. Unlike logit models, log-linear models do not distinguish between categories of variables. Probit models
Multivariate logistic regression
Multivariate_logistic_regression
Estimate of time taken for running an algorithm
quasilinear time (also referred to as log-linear time) if T ( n ) = O ( n log k n ) {\displaystyle T(n)=O(n\log ^{k}n)} for some positive constant k;
Time_complexity
Growth of quantities at rate proportional to the current amount
a log-linear model. For example, if one wishes to empirically estimate the growth rate from intertemporal data on x, one can linearly regress log x on
Exponential_growth
Regression for more than two discrete outcomes
descent algorithms. The formulation of binary logistic regression as a log-linear model can be directly extended to multi-way regression. That is, we model
Multinomial logistic regression
Multinomial_logistic_regression
Statistical test that compares goodness of fit
likelihood-ratio test statistic is expressed as a difference between the log-likelihoods λ LR = − 2 [ ℓ ( θ 0 ) − ℓ ( θ ^ ) ] {\displaystyle \lambda
Likelihood-ratio_test
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
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
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
Function related to statistics and probability theory
with: log L ( α , β ∣ x ) = α log β − log Γ ( α ) + ( α − 1 ) log x − β x . {\displaystyle \log {\mathcal {L}}(\alpha ,\beta \mid x)=\alpha \log \beta
Likelihood_function
Statistical model for a binary dependent variable
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 variables. In regression
Logistic_regression
Mathematical function, inverse of an exponential function
formula: log b x = log 10 x log 10 b = log e x log e b . {\displaystyle \log _{b}x={\frac {\log _{10}x}{\log _{10}b}}={\frac {\log _{e}x}{\log _{e}b}}
Logarithm
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
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
Criterion for model selection
the number of parameters estimated by the model. For example, in multiple linear regression, the estimated parameters are the intercept, the q {\displaystyle
Bayesian information criterion
Bayesian_information_criterion
Function of the observed sample results
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
P-value
Middle quantile of a data set or probability distribution
they need to have the full sample (or a linear-sized portion of it) in memory. Because this, as well as the linear time requirement, can be prohibitive,
Median
Statistical linear model
The general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models
General_linear_model
Set of statistical processes for estimating the relationships among variables
common form of regression analysis is linear regression, in which one finds the line (or a more complex linear combination) that most closely fits the
Regression_analysis
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
Diagnostic plot of binary classifier ability
applications. Conversion to decibels is through X d B = 10 log 10 X {\displaystyle {\mathcal {X}}_{dB}=10\log _{10}{\mathcal {X}}} . From this figure, the common
Receiver operating characteristic
Receiver_operating_characteristic
Collection of statistical models
most common of which uses a linear model that relates the response to the treatments and blocks. Note that the model is linear in parameters but may be nonlinear
Analysis_of_variance
Statistic quantifying the association between two events
odds ratio is log ( p 11 p 00 p 01 p 10 ) = log ( p 11 ) + log ( p 00 ) − log ( p 10 ) − log ( p 01 ) . {\displaystyle {\log \left({\frac
Odds_ratio
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)
How many standard deviations apart from the mean an observed datum is
ISBN 978-1439816806 Kutner, Michael; Nachtsheim, Christopher; Neter, John (204), Applied Linear Regression Models (Fourth ed.), McGraw Hill, ISBN 978-0073014661 {{citation}}:
Standard_score
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
Parametric model in survival analysis
time model to regression analysis (typically a linear model) where − log ( θ ) {\displaystyle -\log(\theta )} represents the fixed effects, and ϵ {\displaystyle
Accelerated failure time model
Accelerated_failure_time_model
Statistical hypothesis test
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Chi-squared_test
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
Relative measure of dispersion expressed as the ratio of standard deviation to the mean
follow an approximately log-normal distribution. In such cases, a more accurate estimate, derived from the properties of the log-normal distribution, is
Coefficient_of_variation
Concept in machine learning
techniques tend to perform better with larger models. Double descent occurs in linear regression with isotropic Gaussian covariates and isotropic Gaussian noise
Double_descent
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
Unit of information
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Data
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
Probabilistic problem-solving algorithm
analysis in process design. The need arises from the interactive, co-linear and non-linear behavior of typical process simulations. For example: In microelectronics
Monte_Carlo_method
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
Data visualization
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Box_plot
Statistics concept
Applied linear models with SAS (Online-Ausg. ed.). Cambridge: Cambridge University Press. ISBN 9780521761598. "7.3: Types of Outliers in Linear Regression"
Errors_and_residuals
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
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
Specialized form of regression analysis, in statistics
estimator, a method for robust simple linear regression Liu, J.; Cosman, P. C.; Rao, B. D. (2018). "Robust Linear Regression via L0 Regularization". IEEE
Robust_regression
Experiment methodology
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
A/B_testing
Statistical property
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Standard_error
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
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
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
Statistical test comparing two probability distributions
Alan; 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
Experimental design in statistics
Application 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
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
Measure of statistical dispersion
data set is divided into quartiles, or four rank-ordered even parts via linear interpolation. These quartiles are denoted by Q1 (also called the lower
Interquartile_range
Statistical hypothesis test
Special Case of Linear Regression Independent t-test as a linear model in R 2.9 Building Connections Between The 2-Sample t-test and Linear Regression Shieh
Student's_t-test
Statistical property
conditional heteroscedasticity (ARCH) modeling technique. Consider the linear regression equation y i = x i β i + ε i , i = 1 , … , N , {\displaystyle
Homoscedasticity and heteroscedasticity
Homoscedasticity_and_heteroscedasticity
Study of collection and analysis of data
applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares
Statistics
Statistical measure of association
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Cramér's_V
Statistical hypothesis test
a data set in a regression analysis follows the simpler of two proposed linear models that are nested within each other. Multiple-comparison testing is
F-test
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
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)
Generates a forecast of future values of a time series
presence of b t {\displaystyle b_{t}} as the sequence of best estimates of the linear trend. The use of the exponential window function is first attributed to
Exponential_smoothing
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
Statistical methods for comparing samples
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Two-proportion_Z-test
Fundamental theorem in probability theory and statistics
central limit theorem. Specifically it says that the normalizing function √n log log n, intermediate in size between n of the law of large numbers and √n of
Central_limit_theorem
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
Time series model
Formally, an EGARCH(p,q): log σ t 2 = ω + ∑ k = 1 q β k g ( Z t − k ) + ∑ k = 1 p α k log σ t − k 2 {\displaystyle \log \sigma _{t}^{2}=\omega +\sum
Autoregressive conditional heteroskedasticity
Autoregressive_conditional_heteroskedasticity
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
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
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
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
Statistical measure of variability
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Median_absolute_deviation
Type of statistical measure over subsets of a dataset
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Moving_average
Estimator for quality of a statistical model
density function for the log-normal distribution. We then compare the AIC value of the normal model against the AIC value of the log-normal model. For misspecified
Akaike_information_criterion
Correlation of a signal with a time-shifted copy of itself, as a function of shift
efficient algorithms exist which can compute the autocorrelation in order n log(n). For example, the Wiener–Khinchin theorem allows computing the autocorrelation
Autocorrelation
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
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
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
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
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
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
Categorization of data using statistics
undertaken by Fisher, in the context of two-group problems, leading to Fisher's linear discriminant function as the rule for assigning a group to a new observation
Statistical_classification
Concepts from statistical hypothesis testing
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Type_I_and_type_II_errors
Model for generating observable data in probability and statistics
autoencoder Flow-based generative model Energy based model Diffusion model Linear discriminant analysis If the observed data are truly sampled from the generative
Generative_model
In mathematics, a quantitative measure of the shape of a set of points
dimensionless quantities, which represent the distribution independently of any linear change of scale. The first raw moment is the mean, usually denoted μ ≡ E
Moment_(mathematics)
Variable representing a random phenomenon
) ) . {\displaystyle F_{Y}(y)=P(Y\leq y)=P(\mathrm {log} (1+e^{-X})\leq y)=P(X\geq -\mathrm {log} (e^{y}-1)).\,} The last expression can be calculated
Random_variable
Process of using data analysis for predicting population data from sample data
datasets are generated by 'simple' random sampling. The family of generalized linear models is a widely used and flexible class of parametric models. Non-parametric:
Statistical_inference
Numeric quantity representing the center of a collection of numbers
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Mean
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
Type of statistical model
In statistics, the term linear model refers to any model which assumes linearity in the system. The most common occurrence is in connection with regression
Linear_model
Statistical technique to aid interpretation of data
Linear trend estimation is a statistical technique used to analyze data patterns. Data patterns, or trends, occur when the information gathered tends to
Linear_trend_estimation
Regression analysis
rectangular hyperbola, is nonlinear because it cannot be expressed as a linear combination of the two β {\displaystyle \beta } s. Systematic error may
Nonlinear_regression
Table that displays the frequency of variables
Press. ISBN 978-0-262-02113-5. MR 0381130. Christensen, Ronald (1997). Log-linear models and logistic regression. Springer Texts in Statistics (Second ed
Contingency_table
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
Bias in causal inference
and Effect Measure Modification (Boston University School of Public Health) Linear Regression (Yale University) Tutorial by University of New England
Confounding
Design of tasks
theory of linear models have encompassed and surpassed the cases that concerned early writers. Today, the theory rests on advanced topics in linear algebra
Design_of_experiments
Metric for fit of statistical models
analysis Categorical Cohen's kappa Contingency table Graphical model Log-linear model McNemar's test Cochran–Mantel–Haenszel statistics Multivariate Regression
Goodness_of_fit
LOG LINEAR
LOG LINEAR
Male
English
Anglicized form of Hebrew Gowg, GOG means "mountain." In the bible, this is the name of a son of Shemaiah and the name of the prophetic prince of the land of Magog. In British legend, God and Magog are the names of two giant guardians of London. Geoffrey of Monmouth states that Gogmagog was one giant who was slain by the Cornish hero Corin.
Girl/Female
Spanish
Diminutive of Dolores: Sorrow. From Maria de los Dolores (the Virgin Mary, or Mary of the...
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.
Surname or Lastname
English and Scottish
English and Scottish : topographic name for someone who lived near a tumulus, mound or hill, Middle English lowe, from Old English hlÄw (see Law 2).Scottish and English : nickname for a short man, from Middle English lah, lowe (Old Norse lágr; the word was adopted first into the northern dialects of Middle English, where Scandinavian influence was strong, and then spread south, with regular alteration of the vowel quality).English and Scottish (of Norman origin) : nickname for a violent or dangerous person, from Anglo-Norman French lou, leu ‘wolf’ (Latin lupus). Wolves were relatively common in Britain at the time when most surnames were formed, as there still existed large tracts of uncleared forest.Scottish : from a pet form of Lawrence. Compare Lowry 1.Americanized spelling of Jewish Lowe.
Boy/Male
French, German, Polish
Long
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.
Boy/Male
Hindu
Lord Buddha
Boy/Male
Welsh
light'.
Male
English
 English short form of Spanish Alonso, LON means "noble and ready." Compare with another form of Lon.
Boy/Male
Arthurian Legend Biblical Hebrew
Name of a king.
Boy/Male
Hindu
Universe
Biblical
the multitude of Gog
Boy/Male
Biblical
Roof, covering.
Male
French
 French form of Latin Eligius, ÉLOY means "to choose."
Girl/Female
Biblical
The multitude of Gog.
Male
French
French form of Latin Eligius, ÉLOI means "to choose."
Girl/Female
Teutonic American Latin
Famous in war.
Boy/Male
French American
Famous warrior, from the Old German 'Chlodovech'. Eighteen kings of France have borne this name,...
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."
LOG LINEAR
LOG LINEAR
Boy/Male
Australian, Chinese, Irish
Courteous; Similar to Shea
Girl/Female
Hindu, Indian
Culture
Boy/Male
Hindu, Indian, Traditional
Lord Shiva
Boy/Male
Indian, Sanskrit
Protected by Bhima
Boy/Male
Arabic, Muslim, Pashtun
Sagacious; Penetrating; Sharp-witted; Astute; Acute; Shining Star; Piercing
Boy/Male
German
Soldier who wields an axe.
Female
English
Variant spelling of English Ceara, CEARRA means "little black one."
Boy/Male
French, German, Hindu, Indian
Guards Wisely; Wise Protector
Girl/Female
Indian, Modern, Tamil
Ocean
Boy/Male
Hindu
Near, Literature
LOG LINEAR
LOG LINEAR
LOG LINEAR
LOG LINEAR
LOG LINEAR
a.
Last; long-delayed; -- obsolete, except in the phrase lag end.
superl.
Wanting strength or animation; depressed; dejected; as, low spirits; low in spirits.
superl.
Depressed in the scale of sounds; grave; as, a low pitch; a low note.
adv.
At a point of duration far distant, either prior or posterior; as, not long before; not long after; long before the foundation of Rome; long after the Conquest.
superl.
Drawn out or extended in time; continued through a considerable tine, or to a great length; as, a long series of events; a long debate; a long drama; a long history; a long book.
v. t.
To pasture cattle on the fog, or aftergrass, of; to eat off the fog from.
superl.
Moderate; not intense; not inflammatory; as, low heat; a low temperature; a low fever.
n.
A fellow; -- used humorously or contemptuously; as, a sly dog; a lazy dog.
superl.
Mean; vulgar; base; dishonorable; as, a person of low mind; a low trick or stratagem.
superl.
Not rising to the usual height; as, a man of low stature; a low fence.
v. t.
To cause to jog; to drive at a jog, as a horse. See Jog, v. i.
v. t.
To enter in a ship's log book; as, to log the miles run.
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.
superl.
Not loud; as, a low voice; a low sound.
adv.
With a low voice or sound; not loudly; gently; as, to speak low.
n.
A part of the log. See Log-chip, and 2d Log, n., 2.
v. i.
To engage in the business of cutting or transporting logs for timber; to get out logs.
n.
That which resembles a leg in form or use; especially, any long and slender support on which any object rests; as, the leg of a table; the leg of a pair of compasses or dividers.
superl.
Deficient in vital energy; feeble; weak; as, a low pulse; made low by sickness.
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.