Users enjoy optimum efficiency for best output in Mathematica because it supports a high level platform in which most processes are automated. Mathematica can perform the following features:
- It automatically translates English sentences into Mathematica codes
- Mathematica has an elementary mathematical function library
- Mathematica supports visualization and animation in 2D and 3D
- Mathematica can perform approximate accuracy, interval math, symbolic calculations and work with intricate numbers
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Matematica Homework Help
Right from the inception of modern civilizations, mathematics has been considered as one of the many important subjects. It is applied in almost everything we do in our daily lives. Mathematics has advanced in many ways, and many formulas have been developed to simplify calculations. Softwares such as Mathematica have been developed to integrate mathematics into technical computing. Mathematica uses a notebook interface which allows the user to organize their work into text, runnable code and dynamic graphic user interface.
Mathematica can be used in all the following topics in mathematics:
- Numeric
- Data analysis
- Graph computation
- Visualization
- Interactive computation
- Mathematical computation
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ANOVA Homework Help in Mathematica
Analysis of variance is a statistical tool used to analyse the difference among group means. It provides a test for the statistical significance of three or more variables means. ANOVA tests for general rather than specific differences among means. It is used to test the null hypothesis, sometimes called the omnibus null hypothesis. When one mean is different from at least one other mean of the population, the null hypothesis is rejected. Since ANOVA does not offer specific information, it is not possible to know which means are different from the other.
ANOVA makes the following assumptions when performing a hypothesis testing;
- The assumption of homogeneity
- The population must be normally distributed
- Each subject must be independent and produce only one value.
ANOVA performs the following statistical tests which are all part of general linear model;
- T-tests-This tests involve comparing the means of independent or dependent samples across two groups.
- One-way ANOVA- This test compares the means of three or more independent groups.
- One-way repeated measures ANOVA- It compares the means of three or more groups within subject variables
- Factorial ANOVA- Compares the means of two or more cells
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CHI SQUARE Homework Help Mathematica
Chi square is a statistical tool that measures the comparison between actual and expected results. The data to be used in Chi square analysis must be;
- Raw and random
- Mutually exclusive
- Drawn from independent variables and from a large enough sample.
Chi square applies regression analysis to explain how a data series was generated and dispersion of data points. A sum of squares technique is applied as a mathematical way of finding the function which varies least. Various forms of hypothesis testing in statistics are discussed in relation to degrees of freedom. Degrees of freedom are the values in a study that have the freedom to vary.
Chi square performs two different kinds of tests;
- The test of independence
- Goodness of fit test
The least square method which is a regression analysis tool is used to determine the line of best fit. This provides a visual demonstration of the relationship between data points. A statistical significance test is carried out to determine if the results of a test occurred randomly or by a specific cause.
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Non-Parametric Test in Mathematica
Non-parametric test is a statistical method that does not require data to fit a normal distribution. The data used for non-parametric tests does not rely on numbers thus the name ordinal tests. Non-parametric tests apply the following methods;
- Descriptive statistics
- Statistical models
- Inference
- Statistical tests
Non-parametric tests are more applicable to a larger variety because of its ease of use and the removal of parameters. Non-parametric test does not require the researcher to make any assumptions about the distribution of the population.
Examples of non-parametric tests include;
- Wilcoxon test
- Friedman test
- Kruskal-Wallis test
- Mann-Whitney test
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Stats Assignment Help in Mathematica
Statistics is a branch of mathematics that deals with collection, analysis, interpretation, presentation and organization of data. Statistics require students to be well versed with formulas, concepts and use of sophisticated statistical software like Mathematica. Statistics is applied across many disciplines, for example in government, health, business, military and research. Statistics applies several mathematical techniques like;
- Mathematical Analysis
- Linear algebra
- Stochastic analysis
- Differential equations
- Measure of theoretic probability
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Sample Assignment Help in Mathematica
Sampling is the art of selecting a sample from a target population. In statistics, sampling is the collecting data from a sample representing the population of interest, by a defined procedure. Various types of research analysis require different sampling methods.
Sampling is applied in many disciplines such as in research, where before the elections, an opinion poll is conducted on a sample of the citizens to predict who will win the elections. The sample should always be a representation of the population of interest. A population of interest is a group of people with the same characteristics that we are interested in studying.
The following are some of the commonly used methods in sampling;
- Purposive sampling- In this type of sampling, the researcher selects the sample units with a definite purpose in view.
- Random sampling- This method gives every member of the population an equal chance of being selected into the sample. It is the best method because it ensures the sample is a great representation of the population
- Stratified sampling-It is also known as quota sampling or proportional sampling. The researcher divides the population into groups called strata. The strata are made up of members who share the same characteristics and attributes. This method is used when the population is too large to run a research on.
- Cluster sampling- This method is quick, cheap and easy. It involves the researcher dividing the group into clusters. He then again picks individual subjects from the clusters to form the sample.
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