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Welcome to the best website for statistics assignment help, for college and university homework. If you are looking for high quality statistical analysis assignment help for any statistics topic, you are in the right place. Contact us for guaranteed assignment help for all statistical tests & analysis. You might also be interested in high-quality Data Mining Assignment Help, SPSS Assignment Help, R Programming Assignment Help and Python Programming Assignment Help. We will deliver your statistics assignment exactly when you need it. Additionally, we also provide 100% accurate solutions for Business Statistics, Biostatistics, Probability Statistics, AP Statistics, Psychology Statistics and Industrial Statistics including MAT10251 Statistical Analysis Project. Contact us any time of the night or day for instant assignment help for any college or university statistics assignment topic. In case you need to submit an assignment at a very short notice, we offer urgent assignment help where we deliver your fully completed statistics assignments within 24 hours, or even earlier, if necessary. In other words, if we accept your order, we will absolutely deliver it on the agreed date & time.  Visit 24 hours assignment help.

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BUSINESS STATISTICS ASSIGNMENT HELP

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Business statistics uses statistical data to make crucial business decisions. Business organizations depend on various forms of data analysis to provide them with a perspective regarding their operations and market conditions. Based on such analysis, they make informed business decisions. Business statistics is concerned with data collection, classification, summarization, organization, analysis, and interpretation. It employs probability and random variables to generate business decision models, and it also incorporates other statistical methods such as descriptive statistics, inferential statistics, time series analysis, correlation, linear regression, and others.

One of the primary steps involved in business data analysis is the sampling of business data. Sampling data for business statistics cannot be random; for optimized results one must ensure that the sample represents the entire population that is being considered for the test.

Multiple sampling methods are used in business statistics depending on the nature of the analysis, and some of those popular methods are snowball sampling and simple random sampling method. Once the sample is collected, it is described, and such description of the sample employs descriptive statistics. Some important elements of descriptive statistics used in business are the measures of central tendency, dispersion, and association.

Central tendency is focused on mean, median, mode and helps one to figure where the central data lies.t

Dispersions evaluates the spread of the data around mean value, taking into consideration the difference between highest and lowest values, and it calculates standard deviation, variance, and interquartile range to derive answers. Measure of Association reveals trends in data, such as correlation between cost and certain factors related to manufacturing.

Other forms of statistical analyses that are used in business statistics to predict trends are correlation and simple linear regression. For instance, one can use records of past sales as data to predict the sales trends for the future. While correlation can indicate the factors which might affect sales, regression analysis can provide a model for sales in future. Time series analysis can help to study changes over time, such as changes in sales pattern.

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BIOSTATISTICS ASSIGNMENT HELP

Biostatistics is the application of statistical studies to diverse biological topics, where data is collected and statistical analysis is performed to derive inferences regarding biological procedures, for instance, the estimated rate of spread of a virus within a stipulated period of time. Biostatistics mainly involves three steps: first an experiment is performed; secondly, data is collected from the experiment and analysed; finally, the information gained is used to derive inferences. Biostatistics play a notable role in the realm of public health, and the statistical study involved herein is primarily meant to solve health problems, formulate health policies, and develop medicines. Biostatistics has made significant contribution in improving our understanding of cancer, AIDS, chronic diseases, growth and development, genetics, and so on.

Statistical methods such as inferential statistics form an integral part of biostatistics since they are used to study selected populations through hypothesis testing. In inferential statistics, a sample is considered as a representation of a population since data availability can be limited. For instance, in case of clinical trials, to infer whether a vaccine is effective against a virus, it might be tested on a sample population, based on the results of which inferences can be made about its effectiveness on the population at large. While considering biostatistics, it is essential to mention bioinformatics which combines statistics, probability, mathematics, and science to make improvements and advancements in public health.

Recent developments of biological databases have facilitated the storage of biological information as Big Data and the same can be utilized by researchers worldwide to manage, acquire, and analyse data for their biostatistical studies. Programming languages and softwares that aid in biostatistical analysis include R programming language, SAS, CycDesigN, Weka, and others.

PSYCHOLOGY STATISTICS ASSIGNMENT HELP

Psychology employs statistics to validate subjective or empirical measurements and meanings that are derived from psychological experiments. Statistical analysis in such cases might comprise of an evaluation of data collected through survey, case study, or psychological tests. Statistical results also serve as the basis to define certain characteristics or behavioural traits of individuals and animals. Statistical techniques and tools related to psychology include psychometrics, factor analysis, and multivariate behavioural research.

Psychometrics is concerned with the development and application of statistical methods for the measurement of psychological factors. It employs two significant measurement theories which are item response theory and classical test theory. In exploratory factor analysis (which is a technique used in psychological statistical analysis) the study begins without a preconceived theory or a relatively loose theory and it is useful in psychometrics as well as multivariate data analysis. Another analytic technique that is used in psychology is confirmatory factor analysis that has a preconceived theory and the theory is tested through factor analysis. Experimental methods are quite popular in psychology statistics, and such methods with are useful in designing/ analyzing statistical data are MANOVA, ANOVA, t-test, chi-square test, binomial test, and others.

Yet another significant component of psychology statistics is Multivariate Behavioural Research. Statistical methods such as multiple regression & prediction, logistic regression, cluster analysis, moderated & mediated regression analysis, correlation, and hierarchical linear modelling all play significant roles in statistical studies in psychology. For instance, a method like logistic regression, which is a type of predictive analysis, is employed when outcomes or dependent variables are likely to acquire one of two possible values (like success or fail).

Logistic regression can be employed in such psychological experiments where researchers are trying to study whether a patient is schizophrenic or not schizophrenic. Similarly, if a psychologist wants to explore how students studying in public schools differ from catholic school students in terms of their performance in 3 subjects, namely, mathematics, science, and ethics, they could use a MANOVA test to find answers. Researchers usually depend on well-known statistical softwares such as SAS to perform such analysis.

PROBABILITY STATISTICS ASSIGNMENT HELP

Probability can be defined as the chance of occurrence of a particular result in a random experiment. In statistics, the sum of all possible results adds up to 1. The greater the probability of the occurrence of a result, the closer it is to number 1, and the probability of the occurrence of a result always resides between the numbers 0 and 1.

Elements of probability are also integral elements of statistics, such as a random experiment, where the outcome cannot be predicted but must be observed, or sample space. Statistical analysis methods such as logistic regression is concerned with sample space, that is, the set of all probable outcomes of an experiment. In case of logistic regression, this value for set space is two since there are usually two possible outcomes (like pass or fail, yes or no), and the probability of occurrence of each of these outcomes are 50% or ½. Here, it is important to mention that the probability of the occurrence of an outcome is calculated by dividing the number of favourable outcomes by number of possible outcomes.

Statistics plays a huge role in predicting trends by using such methods as predictive analysis. In statistics, probability is useful is estimating such factors as the probable growth in sales, chances of acquiring new clients with a specific marketing strategy, or the chances of occurrence of a disaster. In statistical analysis methods such as ANOVA and MANOVA, where one or more variables are dependent on an independent variable, theory of conditional probability is deeply embedded since conditional probability involves estimation of the chances of occurrence of an event, provided that another event has already taken place. Probability and statistics are thus often inseparably related.

BUSINESS STATISTICS ASSIGNMENT HELP

Business statistics uses statistical data to make crucial business decisions. Business organizations depend on various forms of data analysis to provide them with a perspective regarding their operations and market conditions. Based on such analysis, they make informed business decisions. Business statistics is concerned with data collection, classification, summarization, organization, analysis, and interpretation. It employs probability and random variables to generate business decision models, and it also incorporates other statistical methods such as descriptive statistics, inferential statistics, time series analysis, correlation, linear regression, and others.

One of the primary steps involved in business data analysis is the sampling of business data. Sampling data for business statistics cannot be random; for optimized results one must ensure that the sample represents the entire population that is being considered for the test. Multiple sampling methods are used in business statistics depending on the nature of the analysis, and some of those popular methods are snowball sampling and simple random sampling method. Once the sample is collected, it is described, and such description of the sample employs descriptive statistics.

Some important elements of descriptive statistics used in business are the measures of central tendency, dispersion, and association. Central tendency is focused on mean, median, mode and helps one to figure where the central data lies. Dispersions evaluates the spread of the data around mean value, taking into consideration the difference between highest and lowest values, and it calculates standard deviation, variance, and interquartile range to derive answers. Measure of Association reveals trends in data, such as correlation between cost and certain factors related to manufacturing. Other forms of statistical analyses that are used in business statistics to predict trends are correlation and simple linear regression. For instance, one can use records of past sales as data to predict the sales trends for the future. While correlation can indicate the factors which might affect sales, regression analysis can provide a model for sales in future. Time series analysis can help to study changes over time, such as changes in sales pattern.

INDUSTRIAL STATISTICS ASSIGNMENT HELP

Industrial Statistics is a science which uses methods of general statistics to create a system of indexes that can study the composition/ distribution of industries, study the work done by industrial sectors (and also by individual units), and gauge the results of such industrial activities. Researchers in the field of industrial statistics collect, organize, and analyse industrial data to evaluate improvement in industrial production, economic growth, success in the implementation of industrial plans, and so on.

Indexes used in industrial statistics reveal composition and availability of human resources, work efficiency, and time taken to complete certain tasks. Factors and dynamics which affect labour productivity or wages of labours receive attention in the study of industrial statistics. Such indexes also underline the composition, size, and use of industrial capital stock. Statistics of production capacity and statistics related to natural environment are also significant aspects of industrial statistics.

There are several prerogatives of industrial statistics since it helps industries to design and deliver products and services that enhance customers’ satisfaction and expand the industry’s market share as well as profit percentage. Industrial statistics is of help in implementing quality control measures. Dataset analysis, analysis of logistics and production, spatial statistics are all parts of industrial statistics, and statistical methods employed in industries comprise of both descriptive and inferential statistics. For instance, SPC or Statistical Process Control in used for improvement of manufacturing processes, and statistical tools used here include pre-control charts, run charts, attribute and variable chart, and scatter diagrams to name a few. Statistical methods that are widely used in this realm include process capability analysis, pareto analysis, cusum, gage (gauge) repeatability and reproducibility analysis, and others.

UNDERSTANDING STATISTICS ASSIGNMENTS

Our statistics assignment experts review each assignment thoroughly before starting to work on it & we can handle basic as well as advanced statistics assignment topics.

Statistics is defined as:

the science which deals with collection, classification, tabulation, analysis and interpretation of numerical data. The term Statistics is derived from the Latin word status and the Italian word 'statista'.

First used in the 18th century by Professor Gottfried Achenwall, statistics was initially used to refer to demographic information about a population or information about a state - like census data, state of wealth of the state etc.

Did you know the term 'statistics' has different meanings when used in singular and plural forms? Well, when used in singular form, the meaning of statistics is as already described above. In the plural form, the word 'statistics' refers to statistical information called 'data'.

Whichever subject you are learning, it pays to fully understand the meaning, background and scope so that you can do full justice to it. If you are looking for immediate help with a business statistics assignment, you can chat with us anytime right here on our website, 24x7.

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    STATISTICS ASSIGNMENT HELP-ALL TOPICS COVERED

    We cover advanced and regular topics for statistics assignment help. Here are some of them:

    ANALYSIS OF VARIANCE (ANOVA) ASSIGNMENT HELP: ANOVA is the acronym for 'Analysis of Variance'. ANOVA is a statistical technique used to check the difference between two or more means. So why is it called 'Analysis of variance' instead of 'Analysis of means'? Simple answer is-results are derived by analysing variance. Our statistics assignment experts will delve into the depths of every single assignment topic to understand it thoroughly and provide accurate answers.

    PROBABILITY STATISTICS ASSIGNMENT HELP: In statistics, Probability is defined as the possibility of an event occurring, in a random experiment. Probability is measured on a scale of 0 to 1, with 0 being the least probability and 1 indicating a strong probability that an event will occur. Probability and Statistics are two distinctly different domains but are often studied together because the study of statistical analysis often involves the use of Probability distributions. For ease of reference, we just refer to both as Probability Statistics.

    PROBABILITY DISTRIBUTION: A probability distribution can be defined as a function, an equation, or a table which shows how every result of a statistical experiment relates to its probability of occurrence. For example, if a person chooses to note the heights of the subjects from a random sample, they can create a distribution for the heights. Such a distribution, called probability distribution, would be useful if someone wanted to observe which heights are most likely occur, the range or spread of the heights, and chances of occurrence of different results.

    DISCRETE PROBABILITY DISTRIBUTION: A discrete random variable is one which has a measurable value, and a discrete probability distribution defines the probability of occurrence of every single one of these countable values of a discrete random variable. An example would be a coin toss where there are two possible outcomes – heads or tail, and the roll of a dice, where there are six possible outcomes. Binomial distribution used in statistics falls in the category of discrete probability distribution. Discrete probability distribution is often contrasted with continuous probability distribution.

    CONTINUOUS PROBABILITY DISTRIBUTION: A continuous probability distribution is one where a random variable can assume any value because there are infinite number of possible outcomes, and therefore, the probability that the variable will assume a specific given value is estimated to be zero. Normal distribution used in statistics fall in the category of continuous probability distribution. A probability distribution that uses an infinite variable like future time could experience a continuous probability distribution. It is the exact opposite of discrete probability distribution where the number of possible outcomes is countable.

    CONDITIONAL PROBABILITY DISTRIBUTION: Conditional probability is the probability of the occurrence of one even, provided that another event has already occurred. In conditional probability distribution, the distribution of a random variable is changed depending upon the value that another variable has acquired. It is important to distinguish conditional probability distribution from joint probability distribution, because in case of the latter two events might occur simultaneously, without one’s occurrence being dependent upon the other. For instance, the probability of rain when the humidity falls within a certain range could be an example of conditional probability.

    JOINT PROBABILITY DISTRIBUTION: A discrete random variable is one which has countable values within a finite range, or might have a measurably infinite set of values, as is the case with integers. The probability of occurrence of each value for such a discrete random variable would fall between 0 and 1, and the sum of all such values would add up to 1. For instance, when a coin is tossed, there are only two possible outcomes of the event, which are countable, and thus it would be considered as a discrete random variable.

    RANDOM VARIABLES: A random variable is a variable whose values are not known, and whose values are observed through chance events. Such values of random variables are generally numerical figures i.e. real numbers. A random variable can have multiple values and is commonly categorized into two types. A discrete random variable is one that has a finite or countable number of values. Continuous random variables are ones that have infinite number of values, which fall within a continuous range. In statistics, random variables are represented in letters such as ‘x’ or ‘y.’

    DISCRETE RANDOM VARIABLES: A discrete random variable is one which has countable values within a finite range, or might have a measurably infinite set of values, as is the case with integers. The probability of occurrence of each value for such a discrete random variable would fall between 0 and 1, and the sum of all such values would add up to 1. For instance, when a coin is tossed, there are only two possible outcomes of the event, which are countable, and thus it would be considered as a discrete random variable.

    CONTINUOUS RANDOM VARIABLES: A continuous random variable is often defined in comparison to a discrete random variable since they are exact opposites. A random variable which might have an infinite number of values is considered as a continuous random variable. An example would be the time taken to complete a task, since there are infinite possibilities – there is an infinite range of possible times that can be taken. The same would apply in case of possible temperatures during a month since there are infinite number of possible values that the random variable temperature might assume.

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    Advanced Placement (AP) statistics prepares students for advanced placements. Students become acquainted with major statistical concepts and tools which they can use for collecting and analysing data, and for deriving inferences from the same. With a foundation in Advanced Placement statistics, students can pursue a college major not only in statistics but also in criminal justice, economics, environmental studies, aerospace engineering, business studies, and many other subjects. In AP Statistics, students learn to explore data, to model distributions of data, describe relationships between variables, and design studies. Some of the statistical topics and techniques covered in the course include sampling distributions, probability, random variables, distribution of categorical data, and regression analysis.

    Regression analysis is one of the most significant topics in AP statistics that is used at all levels and fields of statistical studies. Regression analysis is used to examine relationships between two or more variables. There are different types of regression analysis, but the simplified purpose is always to detect the impact of an independent variable on one or more dependent variables. Regression analysis is used everywhere, from business statistics to studies in the public health sector, which explains why they are prioritised in Advanced Placement statistics.

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