cyanic-selkie/wikianc huggingface dataset:
Kusanthura-Maŵiro
Kanandi ndondomeko ya Kusanthura-Maŵiro kukukhwaska kutolela kwa uthenga kulazgiska ku kuyezga kwa ubali pakati pa vibwayila viŵiri vya Kusanthura-Maŵiro, panji vibwayila vyauthenga na mauthenga ghakuwumbika na ŵanthu kufuma ku chiyezgero chakuwumbika mwausuma. Chiloskero chikupelekeka kulongosola ubali wa Kusanthura-Maŵiro pakati pa vibwayila viŵiri vyauthenga, ndipo this is compared as an alternative to an idealized null hypothesis of no relationship between two data sets. Rejecting or disproving the null hypothesis is done using statistical tests that quantify the sense in which the null can be proven false, given the data that are used in the test. Working from a null hypothesis, two basic forms of error are recognized: Type I errors (null hypothesis is falsely rejected giving a "false positive") and Type II errors (null hypothesis fails to be rejected and an actual relationship between populations is missed giving a "false negative").
Vyakudanga
Statistics is a mathematical body of science that pertains to the collection, analysis, interpretation or explanation, and presentation of data, or as a branch of mathematics. Some consider statistics to be a distinct mathematical science rather than a branch of mathematics. While many scientific investigations make use of data, statistics is concerned with the use of data in the context of uncertainty and decision-making in the face of uncertainty.
In applying statistics to a problem, it is common practice to start with a population or process to be studied. Populations can be diverse topics, such as "all people living in a country" or "every atom composing a crystal". Ideally, statisticians compile data about the entire population (an operation called a census). This may be organized by governmental statistical institutes. Descriptive statistics can be used to summarize the population data. Numerical descriptors include mean and standard deviation for continuous data (like income), while frequency and percentage are more useful in terms of describing categorical data (like education).
When a census is not feasible, a chosen subset of the population called a sample is studied. Once a sample that is representative of the population is determined, data is collected for the sample members in an observational or experimental setting. Again, descriptive statistics can be used to summarize the sample data. However, drawing the sample contains an element of randomness; hence, the numerical descriptors from the sample are also prone to uncertainty. To draw meaningful conclusions about the entire population, inferential statistics are needed. It uses patterns in the sample data to draw inferences about the population represented while accounting for randomness. These inferences may take the form of answering yes/no questions about the data (hypothesis testing), estimating numerical characteristics of the data (estimation), describing associations within the data (correlation), and modeling relationships within the data (for example, using regression analysis). Inference can extend to the forecasting, prediction, and estimation of unobserved values either in or associated with the population being studied. It can include extrapolation and interpolation of time series or spatial data, as well as data mining.
Mathematical statistics
Mathematical statistics is the application of mathematics to statistics. Mathematical techniques used for this include mathematical analysis, linear algebra, stochastic analysis, differential equations, and measure-theoretic probability theory.
Mbiri
Formal discussions on inference date back to Arab mathematicians and cryptographers, during the Islamic Golden Age between the 8th and 13th centuries. Al-Khalil (717–786) wrote the Book of Cryptographic Messages, which contains one of the first uses of permutations and combinations, to list all possible Arabic words with and without vowels. Al-Kindi's Manuscript on Deciphering Cryptographic Messages gave a detailed description of how to use frequency analysis to decipher encrypted messages, providing an early example of statistical inference for decoding. Ibn Adlan (1187–1268) later made an important contribution on the use of sample size in frequency analysis.
The term 'statistic' was introduced by the Italian scholar Girolamo Ghilini in 1589 with reference to this science. The earliest writing containing statistics in Europe dates back to 1663, with the publication of Natural and Political Observations upon the Bills of Mortality by John Graunt. Early applications of statistical thinking revolved around the needs of states to base policy on demographic and economic data, hence its stat- etymology. The scope of the discipline of statistics broadened in the early 19th century to include the collection and analysis of data in general. Today, statistics is widely employed in government, business, and natural and social sciences.
The mathematical foundations of statistics developed from discussions concerning games of chance among mathematicians such as Gerolamo Cardano, Blaise Pascal, Pierre de Fermat, and Christiaan Huygens. Although the idea of probability was already examined in ancient and medieval law and philosophy (such as the work of Juan Caramuel), probability theory as a mathematical discipline only took shape at the very end of the 17th century, particularly in Jacob Bernoulli's posthumous work Ars Conjectandi. This was the first book where the realm of games of chance and the realm of the probable (which concerned opinion, evidence, and argument) were combined and submitted to mathematical analysis. The method of least squares was first described by Adrien-Marie Legendre in 1805, though Carl Friedrich Gauss presumably made use of it a decade earlier in 1795.
The modern field of statistics emerged in the late 19th and early 20th century in three stages. The first wave, at the turn of the century, was led by the work of Francis Galton and Karl Pearson, who transformed statistics into a rigorous mathematical discipline used for analysis, not just in science, but in industry and politics as well. Galton's contributions included introducing the concepts of standard deviation, correlation, regression analysis and the application of these methods to the study of the variety of human characteristics—height, weight and eyelash length among others. Pearson developed the Pearson product-moment correlation coefficient, defined as a product-moment, the method of moments for the fitting of distributions to samples and the Pearson distribution, among many other things. Galton and Pearson founded Biometrika as the first journal of mathematical statistics and biostatistics (then called biometry), and the latter founded the world's first university statistics department at University College London.
The second wave of the 1910s and 20s was initiated by William Sealy Gosset, and reached its culmination in the insights of Ronald Fisher, who wrote the textbooks that were to define the academic discipline in universities around the world. Fisher's most important publications were his 1918 seminal paper The Correlation between Relatives on the Supposition of Mendelian Inheritance (which was the first to use the statistical term, variance), his classic 1925 work Statistical Methods for Research Workers and his 1935 The Design of Experiments,  where he developed rigorous design of experiments models. He originated the concepts of sufficiency, ancillary statistics, Fisher's linear discriminator and Fisher information. He also coined the term null hypothesis during the Lady tasting tea experiment, which "is never proved or established, but is possibly disproved, in the course of experimentation". In his 1930 book The Genetical Theory of Natural Selection, he applied statistics to various biological concepts such as Fisher's principle (which A. W. F. Edwards called "probably the most celebrated argument in evolutionary biology") and Fisherian runaway,     a concept in sexual selection about a positive feedback runaway effect found in evolution.
The final wave, which mainly saw the refinement and expansion of earlier developments, emerged from the collaborative work between Egon Pearson and Jerzy Neyman in the 1930s. They introduced the concepts of "Type II" error, power of a test and confidence intervals. Jerzy Neyman in 1934 showed that stratified random sampling was in general a better method of estimation than purposive (quota) sampling.
Kutolela Viyezgero
When full census data cannot be collected, statisticians collect sample data by developing specific experiment designs and survey samples. Statistics itself also provides tools for prediction and forecasting through statistical models.
To use a sample as a guide to an entire population, it is important that it truly represents the overall population. Representative sampling assures that inferences and conclusions can safely extend from the sample to the population as a whole. A major problem lies in determining the extent that the sample chosen is actually representative. Statistics offers methods to estimate and correct for any bias within the sample and data collection procedures. There are also methods of experimental design that can lessen these issues at the outset of a study, strengthening its capability to discern truths about the population.
Experimental and observational studies
A common goal for a statistical research project is to investigate causality, and in particular to draw a conclusion on the effect of changes in the values of predictors or independent variables on dependent variables. There are two major types of causal statistical studies: experimental studies and observational studies. In both types of studies, the effect of differences of an independent variable (or variables) on the behavior of the dependent variable are observed. The difference between the two types lies in how the study is actually conducted. Each can be very effective. An experimental study involves taking measurements of the system under study, manipulating the system, and then taking additional measurements using the same procedure to determine if the manipulation has modified the values of the measurements. In contrast, an observational study does not involve experimental manipulation. Instead, data are gathered and correlations between predictors and response are investigated. While the tools of data analysis work best on data from randomized studies, they are also applied to other kinds of data—like natural experiments and observational studies —for which a statistician would use a modified, more structured estimation method (e.g., Difference in differences estimation and instrumental variables, among many others) that produce consistent estimators.
Vifukufuku
The basic steps of a statistical experiment are:
Experiments on human behavior have special concerns. The famous Hawthorne study examined changes to the working environment at the Hawthorne plant of the Western Electric Company. The researchers were interested in determining whether increased illumination would increase the productivity of the assembly line workers. The researchers first measured the productivity in the plant, then modified the illumination in an area of the plant and checked if the changes in illumination affected productivity. It turned out that productivity indeed improved (under the experimental conditions). However, the study is heavily criticized today for errors in experimental procedures, specifically for the lack of a control group and blindness. The Hawthorne effect refers to finding that an outcome (in this case, worker productivity) changed due to observation itself. Those in the Hawthorne study became more productive not because the lighting was changed but because they were being observed.
Chongeya, Mzimba
Chongeya  ni malo agho ghakusangika ku mmwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.
Kusanthura-Maŵiro
Kuŵazga Kwakulaŵilila
An example of an observational study is one that explores the association between smoking and lung cancer. This type of study typically uses a survey to collect observations about the area of interest and then performs statistical analysis. In this case, the researchers would collect observations of both smokers and non-smokers, perhaps through a cohort study, and then look for the number of cases of lung cancer in each group. A case-control study is another type of observational study in which people with and without the outcome of interest (e.g. lung cancer) are invited to participate and their exposure histories are collected.
Mitundu ya uthenga
Various attempts have been made to produce a taxonomy of levels of measurement. The psychophysicist Stanley Smith Stevens defined nominal, ordinal, interval, and ratio scales. Nominal measurements do not have meaningful rank order among values, and permit any one-to-one (injective) transformation. Ordinal measurements have imprecise differences between consecutive values, but have a meaningful order to those values, and permit any order-preserving transformation. Interval measurements have meaningful distances between measurements defined, but the zero value is arbitrary (as in the case with longitude and temperature measurements in Celsius or Fahrenheit), and permit any linear transformation. Ratio measurements have both a meaningful zero value and the distances between different measurements defined, and permit any rescaling transformation.
Because variables conforming only to nominal or ordinal measurements cannot be reasonably measured numerically, sometimes they are grouped together as categorical variables, whereas ratio and interval measurements are grouped together as quantitative variables, which can be either discrete or continuous, due to their numerical nature. Such distinctions can often be loosely correlated with data type in computer science, in that dichotomous categorical variables may be represented with the Boolean data type, polytomous categorical variables with arbitrarily assigned integers in the integral data type, and continuous variables with the real data type involving floating-point arithmetic. But the mapping of computer science data types to statistical data types depends on which categorization of the latter is being implemented.
Other categorizations have been proposed. For example, Mosteller and Tukey (1977) distinguished grades, ranks, counted fractions, counts, amounts, and balances. Nelder (1990) described continuous counts, continuous ratios, count ratios, and categorical modes of data. (See also: Chrisman (1998), van den Berg (1991). )
The issue of whether or not it is appropriate to apply different kinds of statistical methods to data obtained from different kinds of measurement procedures is complicated by issues concerning the transformation of variables and the precise interpretation of research questions. "The relationship between the data and what they describe merely reflects the fact that certain kinds of statistical statements may have truth values which are not invariant under some transformations. Whether or not a transformation is sensible to contemplate depends on the question one is trying to answer."
Descriptive statistics
A descriptive statistic (in the count noun sense) is a summary statistic that quantitatively describes or summarizes features of a collection of information, while descriptive statistics in the mass noun sense is the process of using and analyzing those statistics. Descriptive statistics is distinguished from inferential statistics (or inductive statistics), in that descriptive statistics aims to summarize a sample, rather than use the data to learn about the population that the sample of data is thought to represent.
Inferential statistics
Statistical inference is the process of using data analysis to deduce properties of an underlying probability distribution. Inferential statistical analysis infers properties of a population, for example by testing hypotheses and deriving estimates. It is assumed that the observed data set is sampled from a larger population. Inferential statistics can be contrasted with descriptive statistics. Descriptive statistics is solely concerned with properties of the observed data, and it does not rest on the assumption that the data come from a larger population.
Statistics, estimators and pivotal quantities
Consider independent identically distributed (IID) random variables with a given probability distribution: standard statistical inference and estimation theory defines a random sample as the random vector given by the column vector of these IID variables. The population being examined is described by a probability distribution that may have unknown parameters.
A statistic is a random variable that is a function of the random sample, but. The probability distribution of the statistic, though, may have unknown parameters. Consider now a function of the unknown parameter: an estimator is a statistic used to estimate such function. Commonly used estimators include sample mean, unbiased sample variance and sample covariance.
A random variable that is a function of the random sample and of the unknown parameter, but whose probability distribution does not depend on the unknown parameter is called a pivotal quantity or pivot. Widely used pivots include the z-score, the chi square statistic and Student's t-value.
Between two estimators of a given parameter, the one with lower mean squared error is said to be more efficient. Furthermore, an estimator is said to be unbiased if its expected value is equal to the true value of the unknown parameter being estimated, and asymptotically unbiased if its expected value converges at the limit to the true value of such parameter.
Other desirable properties for estimators include: UMVUE estimators that have the lowest variance for all possible values of the parameter to be estimated (this is usually an easier property to verify than efficiency) and consistent estimators which converges in probability to the true value of such parameter.
This still leaves the question of how to obtain estimators in a given situation and carry the computation, several methods have been proposed: the method of moments, the maximum likelihood method, the least squares method and the more recent method of estimating equations.
Null hypothesis and alternative hypothesis
Interpretation of statistical information can often involve the development of a null hypothesis which is usually (but not necessarily) that no relationship exists among variables or that no change occurred over time.
What statisticians call an alternative hypothesis is simply a hypothesis that contradicts the null hypothesis.
Error
Working from a null hypothesis, two broad categories of error are recognized:
Standard deviation refers to the extent to which individual observations in a sample differ from a central value, such as the sample or population mean, while Standard error refers to an estimate of difference between sample mean and population mean.
Mean squared error is used for obtaining efficient estimators, a widely used class of estimators. Root mean square error is simply the square root of mean squared error.
Many statistical methods seek to minimize the residual sum of squares, and these are called "methods of least squares" in contrast to Least absolute deviations. The latter gives equal weight to small and big errors, while the former gives more weight to large errors. Residual sum of squares is also differentiable, which provides a handy property for doing regression. Least squares applied to linear regression is called ordinary least squares method and least squares applied to nonlinear regression is called non-linear least squares. Also in a linear regression model the non deterministic part of the model is called error term, disturbance or more simply noise. Both linear regression and non-linear regression are addressed in polynomial least squares, which also describes the variance in a prediction of the dependent variable (y axis) as a function of the independent variable (x axis) and the deviations (errors, noise, disturbances) from the estimated (fitted) curve.
Measurement processes that generate statistical data are also subject to error. Many of these errors are classified as random (noise) or systematic (bias), but other types of errors (e.g., blunder, such as when an analyst reports incorrect units) can also be important. The presence of missing data or censoring may result in biased estimates and specific techniques have been developed to address these problems.
Interval estimation
Most studies only sample part of a population, so results do not fully represent the whole population. Any estimates obtained from the sample only approximate the population value. Confidence intervals allow statisticians to express how closely the sample estimate matches the true value in the whole population. Often they are expressed as 95% confidence intervals. Formally, a 95% confidence interval for a value is a range where, if the sampling and analysis were repeated under the same conditions (yielding a different dataset), the interval would include the true (population) value in 95% of all possible cases. This does not imply that the probability that the true value is in the confidence interval is 95%. From the frequentist perspective, such a claim does not even make sense, as the true value is not a random variable. Either the true value is or is not within the given interval. However, it is true that, before any data are sampled and given a plan for how to construct the confidence interval, the probability is 95% that the yet-to-be-calculated interval will cover the true value: at this point, the limits of the interval are yet-to-be-observed random variables. One approach that does yield an interval that can be interpreted as having a given probability of containing the true value is to use a credible interval from Bayesian statistics: this approach depends on a different way of interpreting what is meant by "probability", that is as a Bayesian probability.
In principle confidence intervals can be symmetrical or asymmetrical. An interval can be asymmetrical because it works as lower or upper bound for a parameter (left-sided interval or right sided interval), but it can also be asymmetrical because the two sided interval is built violating symmetry around the estimate. Sometimes the bounds for a confidence interval are reached asymptotically and these are used to approximate the true bounds.
Significance
Statistics rarely give a simple Yes/No type answer to the question under analysis. Interpretation often comes down to the level of statistical significance applied to the numbers and often refers to the probability of a value accurately rejecting the null hypothesis (sometimes referred to as the p-value).
The standard approach is to test a null hypothesis against an alternative hypothesis. A critical region is the set of values of the estimator that leads to refuting the null hypothesis. The probability of type I error is therefore the probability that the estimator belongs to the critical region given that null hypothesis is true (statistical significance) and the probability of type II error is the probability that the estimator does not belong to the critical region given that the alternative hypothesis is true. The statistical power of a test is the probability that it correctly rejects the null hypothesis when the null hypothesis is false.
Referring to statistical significance does not necessarily mean that the overall result is significant in real world terms. For example, in a large study of a drug it may be shown that the drug has a statistically significant but very small beneficial effect, such that the drug is unlikely to help the patient noticeably.
Although in principle the acceptable level of statistical significance may be subject to debate, the significance level is the largest p-value that allows the test to reject the null hypothesis. This test is logically equivalent to saying that the p-value is the probability, assuming the null hypothesis is true, of observing a result at least as extreme as the test statistic. Therefore, the smaller the significance level, the lower the probability of committing type I error.
Some problems are usually associated with this framework (See criticism of hypothesis testing):
Examples
Some well-known statistical tests and procedures are:
• Chi-squared test
• Factor analysis
• Mann–Whitney U
• Mean square weighted deviation (MSWD)
• Pearson product-moment correlation coefficient
• Regression analysis
• Spearman's rank correlation coefficient
• Student's t-test
• Time series analysis
• Conjoint Analysis
Exploratory data analysis
Exploratory data analysis (EDA) is an approach to analyzing data sets to summarize their main characteristics, often with visual methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell us beyond the formal modeling or hypothesis testing task.
Misuse
Misuse of statistics can produce subtle but serious errors in description and interpretation—subtle in the sense that even experienced professionals make such errors, and serious in the sense that they can lead to devastating decision errors. For instance, social policy, medical practice, and the reliability of structures like bridges all rely on the proper use of statistics.
Even when statistical techniques are correctly applied, the results can be difficult to interpret for those lacking expertise. The statistical significance of a trend in the data—which measures the extent to which a trend could be caused by random variation in the sample—may or may not agree with an intuitive sense of its significance. The set of basic statistical skills (and skepticism) that people need to deal with information in their everyday lives properly is referred to as statistical literacy.
There is a general perception that statistical knowledge is all-too-frequently intentionally misused by finding ways to interpret only the data that are favorable to the presenter. A mistrust and misunderstanding of statistics is associated with the quotation, "There are three kinds of lies: lies, damned lies, and statistics". Misuse of statistics can be both inadvertent and intentional, and the book How to Lie with Statistics, by Darrell Huff, outlines a range of considerations. In an attempt to shed light on the use and misuse of statistics, reviews of statistical techniques used in particular fields are conducted (e.g. Warne, Lazo, Ramos, and Ritter (2012)).
Ways to avoid misuse of statistics include using proper diagrams and avoiding bias. Misuse can occur when conclusions are overgeneralized and claimed to be representative of more than they really are, often by either deliberately or unconsciously overlooking sampling bias. Bar graphs are arguably the easiest diagrams to use and understand, and they can be made either by hand or with simple computer programs. Unfortunately, most people do not look for bias or errors, so they are not noticed. Thus, people may often believe that something is true even if it is not well represented. To make data gathered from statistics believable and accurate, the sample taken must be representative of the whole. According to Huff, "The dependability of a sample can be destroyed by [bias]... allow yourself some degree of skepticism."
To assist in the understanding of statistics Huff proposed a series of questions to be asked in each case:
Misinterpretation: correlation
The concept of correlation is particularly noteworthy for the potential confusion it can cause. Statistical analysis of a data set often reveals that two variables (properties) of the population under consideration tend to vary together, as if they were connected. For example, a study of annual income that also looks at age of death, might find that poor people tend to have shorter lives than affluent people. The two variables are said to be correlated; however, they may or may not be the cause of one another. The correlation phenomena could be caused by a third, previously unconsidered phenomenon, called a lurking variable or confounding variable. For this reason, there is no way to immediately infer the existence of a causal relationship between the two variables.
Applied statistics, theoretical statistics and mathematical statistics
Applied statistics, sometimes referred to as Statistical science, comprises descriptive statistics and the application of inferential statistics. Theoretical statistics concerns the logical arguments underlying justification of approaches to statistical inference, as well as encompassing mathematical statistics. Mathematical statistics includes not only the manipulation of probability distributions necessary for deriving results related to methods of estimation and inference, but also various aspects of computational statistics and the design of experiments.
Kabena, Mzimba
Kabena ni malo agho ghakusangika ku mmwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.
Kusanthura-Maŵiro
Applied statistics, theoretical statistics and mathematical statistics
Statistical consultants can help organizations and companies that do not have in-house expertise relevant to their particular questions.
Machine learning and data mining
Machine learning models are statistical and probabilistic models that capture patterns in the data through use of computational algorithms.
Statistics in academia
Statistics is applicable to a wide variety of academic disciplines, including natural and social sciences, government, and business. Business statistics applies statistical methods in econometrics, auditing and production and operations, including services improvement and marketing research. A study of two journals in tropical biology found that the 12 most frequent statistical tests are: analysis of variance (ANOVA), chi-squared test, Student's t-test, linear regression, Pearson’s correlation coefficient, Mann-Whitney U test, Kruskal-Wallis test, Shannon’s diversity index, Tukey's range test, cluster analysis, Spearman's rank correlation coefficient and principal component analysis.
Statistical computing
The rapid and sustained increases in computing power starting from the second half of the 20th century have had a substantial impact on the practice of statistical science. Early statistical models were almost always from the class of linear models, but powerful computers, coupled with suitable numerical algorithms, caused an increased interest in nonlinear models (such as neural networks) as well as the creation of new types, such as generalized linear models and multilevel models.
Business statistics
In business, "statistics" is a widely used management- and decision support tool. It is particularly applied in financial management, marketing management, and production, services and operations management. Statistics is also heavily used in management accounting and auditing. The discipline of Management Science formalizes the use of statistics, and other mathematics, in business. (Econometrics is the application of statistical methods to economic data in order to give empirical content to economic relationships.)
Afilika
Chilwa cha Afilika ndicho chili na ŵanthu ŵanandi chomene pa charu chose chapasi. Chigaŵa ichi chili na malo ghakukwana makilomita pafupifupi 30.300,000, kusazgapo virwa vinyake. Ŵanthu ŵakukwana 1.4 biliyoni Kufika chaka cha 2021, chiŵelengero ichi chikaŵa pafupifupi pa ŵanthu wose pa caru capasi. Ŵanthu ŵa mu Africa ndiwo mbaciwukirano comene pa vyaru vyose; mu 2012, ŵanthu awo ŵakaŵa na vyaka vyapakati pa 19.7 na 30.4 pa charu chose. Nangauli charu cha Africa chili na usambazi unandi chomene, kweni chili na ŵanthu ŵachoko chomene pa charu chose chapasi. Ŵasayansi ŵakuti vinthu ivi vikupangiska kuti ŵanthu ŵambenge kusuzgika maghanoghano, chikoloni, Nkhondo Yakuzizima, Nkhondo ya ku caru ciphya, kusowa kwa demokilase, na vimbundi.Nangauli usambazi uli kupangika makora yayi, kweni chifukwa cha kusazgikira kwa vinthu mu vyaru ivi, kweniso ŵanthu ŵanandi awo mbawukirano, charu cha Africa ni msika wakuzirwa pa caru cose.
Kusanthura-Maŵiro
Business statistics
A typical "Business Statistics" course is intended for business majors, and covers descriptive statistics (collection, description, analysis, and summary of data), probability (typically the binomial and normal distributions), test of hypotheses and confidence intervals, linear regression, and correlation; (follow-on) courses may include forecasting, time series, decision trees, multiple linear regression, and other topics from business analytics more generally. See also. Professional certification programs, such as the CFA, often include topics in statistics.
Afilika
Charu ichi chili kuzingilizgika na Nyanja ya Mediterranean kumpoto, Suez Isthmus na Nyanja Yiswesi kumpoto ca kumafumiro gha dazi, Indian Ocean kumwera kwa kumafumiro gha dazi na Atlantic Ocean kumanjiliro gha dazi. Charu ichi chili na vyaru vinandi nga ni Madagascar. Mu vyaru ivi muli vyaru 54 ivyo vili na mazaza, vigaŵa 8, na vyaru viŵiri vyambura mazaza. Charu cha Algeria ndicho ntchikuru chomene pa vyaru vyose vya mu Africa. Vyaru vya mu Africa vikukolerana pakuzenga wupu wa African Union, uwo uli na ofesi yake yikuru mu Addis Ababa.
Charu cha Africa chili pafupi na equator na prime meridian. Ni caru cimoza pera ico cili kufuma kumpoto kukafika kumwera. Chigaŵa chikuru cha charu ichi chili kumpoto kwa charu, ndipo chigaŵa chikuru chili kumwera kwa charu. Chigaŵa chikuru cha charu ichi chili mu vigaŵa vyakututuka, kupaturako chigaŵa chikuru cha Western Sahara, Algeria, Libya, na Egypt, kumpoto kwa Mauritania, na vigaŵa vyose vya Morocco, Ceuta, Melilla, na Tunisia ivyo vili pachanya pa chigaŵa cha Cancer, kumpoto kwa chigaŵa chakututuka. Ku chigaŵa chinyake cha charu ichi, kumwera kwa Namibia, kumwera kwa Botswana, vigaŵa vinandi vya South Africa, vigaŵa vyose vya Lesotho na Eswatini, kweniso kumwera kwa Mozambique na Madagascar, vili kusi kwa malo ghakuchemeka Tropic of Capricorn.
Gutenstein, Austria
Gutenstein, Austria ni msumba mu boma la Lower Austria mu charu cha Austria mu chilwa chikulu cha Ulaya (Europe).
Afilika
Ku Africa kuli vyamoyo vinandi chomene, ndipo kuli vyamoyo vinandi chomene. Ndipouli, mu vyaru vya ku Africa muli masuzgo ghakupambanapambana, nga ni kuzgoka mapopa, kukomeka kwa makuni, kusoŵa maji, na kunangika kwa vinthu. Vinthu ivi vikwenera kulutilira kutikhuŵazga chomene chifukwa cha kusintha kwa nyengo. Wupu wa United Nations Intergovernmental Panel on Climate Change (UNIPCC) ukati Africa ni caru ico cikusuzgika comene na kusintha kwa nyengo.
Mbiri ya Africa njitali, njakusuzga kuyipulikiska, ndipo kanandi ŵanthu ŵakutondeka kuyizirwiska. Ŵanthu ŵanandi ŵakugomezga kuti ŵanthu ŵakafuma ku Africa, chomenechomene ku East Africa. Ŵanthu ŵakwambilira na ŵasekuru ŵawo ŵakaŵako vyaka pafupifupi 7 miliyoni ivyo vyajumpha, kusazgapo Sahelanthropus tchadensis, Australopithecus africanus, A. afarensis, Homo erectus, H. habilis na H. ergaster. Vinthu vyakwambilira ivyo vikukhalira vya ŵanthu ŵa Homo sapiens (ŵanthu ŵa mazuŵa ghano) ivyo vikusangika ku Ethiopia, South Africa, na Morocco, vikaŵako vyaka pafupifupi 233,000, 259,000, na 300,000 ivyo vyajumpha. Ŵasayansi ŵakuwona kuti mu Africa ndimo muli vyamoyo vyakupambanapambana chifukwa ndiko ŵanthu ŵakukhala nyengo yitali chomene.
Hadersdorf-Kammern
Hadersdorf-Kammern ni msumba mu boma la Lower Austria mu charu cha Austria mu chilwa chikulu cha Ulaya (Europe).
Hadres
Hadres ni msumba mu boma la Lower Austria mu charu cha Austria mu chilwa chikulu cha Ulaya (Europe).
Kusanthura-Maŵiro
Specialized disciplines
• Actuarial science (assesses risk in the insurance and finance industries)
Mkoma, Mzimba
Mkoma ni malo agho ghakusangika kummwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.
Haidershofen
Haidershofen ni msumba mu boma la Lower Austria mu charu cha Austria mu chilwa chikulu cha Ulaya (Europe).
Kabuku, Mzimba
Kabuku  ni malo agho ghakusangika ku mmwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.
Hainburg an der Donau
Hainburg an der Donau ni msumba mu boma la Lower Austria mu charu cha Austria mu chilwa chikulu cha Ulaya (Europe).
Chiputu, Lilongwe
Chiputu ni zina la sukulu yakwamba kweneso malo agho ghakusangika mu chigaŵa cha kumanjililo gha dazi gha boma la Lilongwe,mu chigaŵa cha Pakati,mu charu cha Malaŵi.
Kusanthura-Maŵiro
Specialized disciplines
• Applied information economics
Chilowa, Lilongwe
Chilowa ni zina la sukulu yakwamba kweneso malo agho ghakusangika mu chigaŵa cha kumanjililo gha dazi gha boma la Lilongwe,mu chigaŵa cha Pakati,mu charu cha Malaŵi.
Chitedze, Lilongwe
Chitedze ni zina la sukulu yakwamba kweneso malo agho ghakusangika mu chigaŵa cha kumanjililo gha dazi gha boma la Lilongwe,mu chigaŵa cha Pakati,mu charu cha Malaŵi.
Kusanthura-Maŵiro
Specialized disciplines
• Astrostatistics (statistical evaluation of astronomical data)
• Biostatistics
Kabiza, Mzimba
Kabiza ni malo agho ghakusangika ku mmwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.
Haag, Austria
Haag, Austria ni msumba mu boma la Lower Austria mu charu cha Austria mu chilwa chikulu cha Ulaya (Europe).
Mwitha, Mzimba
Mwitha ni malo agho ghakusangika kummwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.
Afilika
Zina
Afri likaŵa zina Lachilatini ilo ŵanthu ŵakakhaliranga kumanjiliro gha dazi kwa Mlonga wa Nayelo, mu chigaŵa icho kale chikachemekanga Northern Africa. Zina ili likwenera kuti likayowoyanga za fuko la ku Libya, ilo likaŵa sekuru wa ŵanthu ŵa ku Berber mazuŵa ghano. Kanandi zina ili likakolerananga na lizgu la Chifenike lakuti ʿafar ilo likung'anamura "fumbi", kweni mu 1981, ŵanthu ŵanyake ŵakaghanaghana kuti lizgu ili likufuma ku lizgu la Chiberberber lakuti ifri. Lizgu lenelili likusangikaso mu zina la ŵanthu ŵa mtundu wa Banu Ifran ŵakufuma ku Algeria na Tripolitania.
Mu nyengo ya Ŵaroma, msumba wa Carthage ndiwo ukaŵa msumba ukuru wa chigaŵa icho ŵakachichemanga kuti Africa Proconsularis, pamanyuma pakuti ŵathereska ŵanthu ŵa ku Carthage mu Nkhondo Yachitatu ya ku Punic mu 146 B.C.E., ndipo chigaŵa ichi chikaŵa ku Libya. Nyengo zinyake lizgu Lachilatini lakuti -ica likuyowoyeka kuti likung'anamura charu (kuyelezgera kuti, mu Chiceltic kufuma ku Celtae, nga umo Julius Caesar wakayowoyera). Chigaŵa cha Ifriqiya icho chikaŵa cha Ŵasilamu, chikati chawukira Ufumu wa Byzantine (Eastern Roman), chikaŵa na zina lakuti Exarchatus Africae.
Kuyana na Ŵaroma, caru ca Africa cikaŵa kumanjiliro gha dazi kwa Egupto, apo "Asia" cikaŵa Anatolia na vyaru vinyake vya kumafumiro gha dazi. Munthu munyake uyo wakalemba vya malo zina lake Ptolemy (85-165 C.E.) wakajambura mphaka pakati pa vyaru viŵiri ivi, ndipo wakalongosora kuti msumba wa Alekizandiriya ndiwo uli pa mphaka pakati pa Asia na Africa. Apo ŵanthu ŵa ku Europe ŵakamanya ukuru wa charu ichi, ŵakamba kumanya vinandi vya "Africa".
Ŵanthu ŵanyake ŵakughanaghana kuti zina lakale lakuti "Afirika" likung'anamura kuti:
Mbili yakwamba
Ŵasayansi ŵanandi ŵakuwona kuti charu cha Africa ndicho chakukhala ŵanthu ŵanandi chomene pa charu chapasi. Pakati pa vyaka vya m'ma 1900, ŵasayansi ŵakasanga viswaswa vinandi na ukaboni wakuti ŵanthu ŵakakhalanga mu charu ichi vyaka 7 miliyoni ivyo vyajumpha. Vipasi vya ŵanthu ŵakwambilira awo ŵakaghanaghanika kuti ndiwo ŵakapangiska kuti ŵanthu ŵa mazuŵa ghano ŵaŵe ŵanthu, nga ni Australopithecus afarensis uyo wali na vyaka vyakujumpha 3.9 million. Ŵanthu ŵanyake awo ŵakaŵako kale chomene (ŵakaŵako kale chomene) ni Homo ergaster (c. Vinthu ivi vikawoneka mu vyaka vya m'ma 1800 B.C.E.
Pakati pajumpha vyaka 350,000 m'paka 260,000 kufuma apo ŵanthu ŵakamba kuŵako mu Africa,  Pa nyengo iyo, ŵanthu ŵanandi ŵakakhalanga mu charu ichi ŵakaŵa ŵalovi na ŵakuvuna vipasi. Ŵanthu ŵakwambilira aŵa ŵakafumako ku Africa na kwamba kukhala mu vigaŵa vinyake vya charu mu nyengo ya Out of Africa II migration iyo yikachitika vyaka pafupifupi 50,000 pambere Yesu wandize pa charu chapasi.
Ŵanthu ŵanyake awo ŵakasamukiranga ku vyaru vinyake mu nyengo yira, ŵakasanga ukaboni wakuti ŵanthu ŵakwambilira ŵakakhalanga mu vyaru vya kumwera kwa Africa, kumwera kwa Africa, kumpoto kwa Africa, na Sahara.
Kuwuka kwa maboma ghakubanuka
Kwamba kale, chigaŵa cha Sahara chikusintha chomene, ndipo nyengo zinyake chikusintha mwakuyana na umo nyengo yiliri pa charu chose. Paumaliro wa nyengo ya Ice Age, iyo yikwenera kuti yikaŵa mu 10,500 B.C.E., Sahara yikazgokaso chipalamba, ndipo ŵanthu ŵa mu Africa ŵakawerako ku vigaŵa vya mukati na mumphepete mwa nyanja ku Africa, ndipo ŵakalembapo vinthu vya mu jalawe ivyo vikulongora kuti Sahara yikaŵa na vyakumera vinandi. Ndipouli, chifukwa chakuti nyengo yikakwera kweniso kukawomira, mu 5000 B.C.E., chigaŵa cha Sahara chikaŵa chakomira chomene. Pafupifupi mu 3500 B.C.E., chifukwa cha kupindika kwa charu, Sahara yikazgoka mapopa mwaluŵiro. Ŵanthu aŵa ŵakafumanga mu chigaŵa cha Sahara na kuluta ku Dambo la Nayelo kusi ku Second Cataract uko ŵakakhalanga. Nyengo yikafika paheni comene, ndipo ici cikapangiska kuti ku Central na East Africa kuleke kuwa vula zinandi. Kufuma waka pa nyengo iyi, mu vyaru vya kumafumiro gha dazi kwa Africa mukaŵa chilangalanga, ndipo mu vyaka 200 ivyo vyajumpha, ku Ethiopia kukaŵa chilangalanga.
Mu vigaŵa vya mapiri na vyakumera vya mu Sahara na Sahel ku North West Africa, ŵanthu awo ŵakwenera kuti ŵakakhalanga mu vyaru vya Nilo-Saharan na Mandé ŵakamba kuwunjika mphoza za mu thondo, kuzingilizga 8000 m'paka 6000 B.C.E. Nyengo yikati yajumphapo, ŵanthu ŵakamba kuwunjika viŵiya nga ni nkhaka, mphoza, makungwa, na katoni. Nkhumba zinayamba kulima kum'mawa kwa Sudan cha m'ma 4000 B.C.E., ndipo iyi yikaŵa nyengo yakwambilira chomene ya ulimi. Nyengo yikati yajumphapo, vyakumera ivi vikamba kulima mu vyaru vya ku Africa, ndipo pamanyuma vikamba kulima mu vyaru vya ku India.
Msazi, Mzimba
Msazi ni malo agho ghakusangika kummwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.
Afilika
Kuwuka kwa maboma ghakubanuka
Nkhumba izi zikaŵako kakwamba mu charu cha Sudan. Kweniso ŵakamba kupanga viŵiya vya dongo na kuzenga malo gha malibwe (nga ni Tichitt, Oualata). Mu milonga na nyanja zinandi izo zikaŵako cifukwa ca vula, ŵanthu ŵakaloŵanga comene pa somba. Mu vyaru vya kumanjiliro gha dazi kwa Africa, nkhorongo yikakura chomene kufuma ku Senegal m'paka ku Cameroon. Pakati pa 9,000 na 5,000 B.C.E., ŵanthu awo ŵakayowoyanga chiyowoyero cha Niger na Congo ŵakamba kulima makuni gha maolive na rafia. Ŵakaŵaso na viŵiya vinyake vyakuchemeka black-eyed peas na voandzeia. Pakuti vyakumera vinandi vikakuliranga mu nkhorongo, ŵanthu awo ŵakayowoyanga chiyowoyero cha Niger na Congo ŵakagwiliskiranga ntchito nkhwantha zakusona malibwe.
Pafupifupi mu 4000 B.C.E., mlengalenga wa Sahara ukamba kukavuka chomene. Ivi vikapangiska kuti nyanja na milonga vichepe chomene. Ivi vikapangiska kuti ŵanthu ŵaleke kukhalamo mu vyaru ivi, kweniso kuti ŵalimi ŵalute ku vyaru vya kumanjiliro gha dazi kwa Africa. Mu vyaka vya m'ma 1000 B.C.E., ŵanthu ŵanandi ŵakamba kulima mpunga pafupi na Mlonga wa Niger.
Mu vyaka vya m'ma 1000 B.C.E., ŵanthu ŵakamba kupanga visulo ku North Africa. Pa nyengo iyi, ŵanthu ŵakamba kugwiliskira ntchito visulo mu vigaŵa vinyake vya ku Africa, kwali ŵakavipanganga mwakujiyimira paŵekha panji ŵakavipharazga kufuma kumpoto. Mu vyaru vinandi vya kumafumiro gha dazi na kumanjiliro gha dazi kwa Africa, mulimo wa kupanga visulo ukambira cha m'ma 500 B.C.E., nangauli mu vyaru vinyake ŵakamba kupanga visulo yayi m'paka m'ma 100 C.E. Vinthu vya mkuwa vyakufuma ku Eguputo, kumpoto kwa Africa, Nubia, na Ethiopia ivyo vikalembeka m'ma 500 B.C.E. vikavumbukwa ku West Africa, ndipo vikulongora kuti pa nyengo iyi, ŵanthu ŵakendanga mu vyaru vya ku Sahara.
Ŵanthu ŵakwambilira
Pafupifupi mu 3300 B.C.E., mbiri yakale yikamba ku Northern Africa apo ŵanthu ŵakamba kusambira kulemba na kulemba mu nyengo ya Farawo. Boma la Eguputo likaŵa limoza mwa maboma ghakale chomene pa charu chose ndipo likalutilira kulamulira m'paka mu 343 B.C.E. Ŵaeguputo ŵakathereskanga ŵanthu ŵanandi chomene mu charu icho sono chikuchemeka Libya na Nubia.
Ŵafenikiya ŵa ku Ture ŵakakhazikiska msumba wa Carthage, ku mphepete mwa nyanja ya kumpoto ca kumanjiliro gha dazi kwa Africa, uwo ukaŵa malo ghakupambanapambana gha ŵanthu awo ŵakaguliskiranga vinthu ku Fonike.
Ŵagiriki na Ŵaroma ndiwo ŵakamba kusanda vyaru vya mu Africa. Mu 332 B.C.E., Alekizanda Mukuru wakapokelereka nga ni muwomboli mu Eguputo. Wakazenga msumba wa Alexandria ku Eguputo, uwo ukakhalako kwa nyengo yitali ndipo ukaŵa msumba ukuru wa ufumu wa Ptolemy.
Ufumu wa Ŵaroma ukati wapoka chigaŵa cha kumpoto kwa Africa icho chili mumphepete mwa nyanja ya Meditereniyani, chigaŵa ichi chikamba kulamulirika na Ŵaroma. Ŵaroma ŵakamba kukhala mu vyaru ivyo sono ni Tunisia na vinyake ivyo vili mumphepete mwa nyanja. Themba lakwamba la Ŵaroma ilo likafuma ku North Africa likaŵa Septimius Severus, uyo wakababikira ku Leptis Magna mu Libya.
Kusanthura-Maŵiro
Specialized disciplines
• Chemometrics (for analysis of data from chemistry)
Afilika
Ŵanthu ŵakwambilira
Chikhristu chikafika mu vigaŵa ivi kufuma ku Yudeya na ku Eguputo, kweniso kufuma ku charu cha Romu kuya ku Nubia. Mu 340 C.E., chikaŵa kuti chazgoka chisopa cha boma la Ufumu wa Aksumite. Ŵamishonale ŵa ku Siriya na Girisi awo ŵakafika pa Nyanja Yiswesi ndiwo ŵakambiska nkhani iyi.
Dzalanyama, Lilongwe
Dzalanyama ni zina la sukulu yakwamba kweneso malo agho ghakusangika mu chigaŵa cha kumanjililo gha dazi gha boma la Lilongwe,mu chigaŵa cha Pakati,mu charu cha Malaŵi.
Afilika
Ŵanthu ŵakwambilira
Kukwambilira kwa vyaka vya m'ma 700 C.E., ufumu wa Caliphate wa ku Arabia uwo ukaŵa kuti wapangika waka, ukafika ku Eguputo, ndipo pamanyuma ukafika ku North Africa. Pakati pajumpha nyengo yicoko waka, ŵanthu ŵa ku Berber ŵakasazgikana na mafuko gha Ciarabu. Apo msumba wa Umayyad ku Damaseko ukaparanyika mu ma 700 C.E., msumba wa chisopa cha Cisilamu uwo ukaŵa ku Meditereniyani ukafumako ku Siriya kuluta ku Qayrawan, kumpoto kwa Africa. Ku North Africa kukaŵa ŵanthu ŵanandi ŵa chisopa cha Ciisilamu, ndipo kukaŵa ŵanthu ŵavinjeru, ŵasambizgi, ŵa malango, na ŵavinjeru. Mu nyengo iyi, Chisilamu chikamba kuthandazgika chomene mu vyaru vya kumwera kwa Sahara.
Kalikumbi, Mzimba
Kalikumbi  ni malo agho ghakusangika ku mmwela kwa boma la Mzimba, chigaŵa cha kumpoto kwa charu cha Malaŵi.