The R Programming language supports the following techniques in statistics;
- Linear and Non-linear modelling
- Classical statistical tests
- Time series analysis
- ANOVA and linear programming
It is efficient because it is highly extensible and it provides an open source route to participation in statistical methodology. R programming is highly compatible, it can run on a variety of platforms such us; UNIX, Linux, FreeBSD, Windows and MacOS.
Most students and researchers use the R language in their calculations and manipulation of data because it has an integrated suite of software facilities. These facilities include;
- Graphical facilities which are used for data analysis
- An integrated collection of intermediate tools
- A suite of operators for calculations
- The R programming software can handle and store data effectively
The R language has been criticized that it provides too many choices and not enough guidance; users must therefore share their work with others.
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R Programming Homework Help
The R programming language is so vital in many topics of statistics such as probability, regression analysis and hypothesis testing. Central limit theorem simulation and hypothesis testing. These topics are highly examinable and applicable to the contemporary world. Students are required to have in depth knowledge in the use and application of this software. Most of the time, students are forced to seek the help of the experts because;
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ANOVA Homework Help in R Programming
Analysis of variance is a statistical tool used to perform hypothesis tests on mean differences. In regression study, Analysis of Variance is used to determine the results that independent variable have on dependent variable. It is applied when data needs to be experimental in which the sample sizes have to be the same. You might be wondering why we use ANOVA table in econometrics analysis. Well, ANOVA is a unique statistical methodology. It’s used in econometrics analysis to show whether two or more groups are different from each other, assuming the population is normally distributed. Developed by Ronald Fisher in 1918, ANOVA is an extension of the t and z tests. The z and t test had a problem of only allowing nominal level variables to have two groups.
Researchers can use ANOVA in the following three ways depending on the research design;
- One-way ANOVA- this type of ANOVA has only one independent variable
- Two-way ANOVA- it is an expansion of the one way ANOVA. It has two independent variables. It is used to measure the relationship between the two variables which indicate that differences are not uniform across all categories.
- N-way ANOVA- this method allows the user to use more than two independent variables. The n denotes the number of independent variables.
The following assumptions must be considered when using ANOVA;
- Homogeneity of variance – This means that the variance among the groups should be equal
- The dependent variable should have an interval or ration measurement
- The independent variables must be categorical, nominal or ordinal.
- ANOVA assumes that data is normally distributed.
- Observations are also assumed to be independent
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Chi Square Homework Help in R Programming
Chi square is a statistical tool that compares expectations of an experiment against the results obtained. It requires the researcher to use random, raw and mutually exclusive data obtained from independent variables and a large enough sample. Chi square applies the Sum of Squares technique to determine how well a data series can be fit to a function. The sum of squares is used in regression analysis to showcase the dispersion of data points. Data EDA Temporary file creation is a universal application under chi-square.Here the researcher is obligated to use EDA techniques to learn more about the involved variables against the results.
Chi square is a form of hypothesis testing. The Degrees of freedom which is the values that have the freedom to vary in a study is vital when a validity of a null hypothesis needs to be understood in a Chi square.
Chi square performs two different kinds of tests, namely;
- The test of independence
- Goodness of fit test
The least of squares method is vital in Chi square in finding the line of best fit in a dataset. This method aims at creating a straight line that minimizes the sum of the squares of the errors generated.
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Hypothesis Testing Assignment Help in R Programming
Hypothesis testing is a statistical method used in testing assumptions regarding a parameter. The application of the different type of test greatly depends on the type of analysis and the nature of the data used. A hypothesis test is usually carried out with the aim of accepting or rejecting the null hypothesis. There are four steps involved when carrying out a hypothesis test;
- Stating the two hypothesis in which one can be right.
- Formulation of an analysis plan which shows how the data will be evaluated
- Carrying out the plan and physically analysing the sample data.
- Analysis of the results and either accepting or rejecting the null hypothesis
A Null hypothesis presumes that no statistical significance exists in a set of given observations between variables. It is always considered to be true until statistical evidence nullifies it for an alternative hypothesis. A null hypothesis may be rejected by the researcher although it is true, this leads to a type 1 error. Hypothesis testing is a common area of evaluation under R. Examinable topics under hypothesis testing are GARCH forecasting model, forecasting instruction sheet, forecasting principles, and practices and forecasting right-hand side variables. These topics are involving as you are supposed to use hypothesis testing as a tool to make predictions.
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Probability & Statistics Homework Help in R Programming
Probability is the chance of something that happens. It helps researchers and users to make logical and consistent decisions. Probability is highly applicable in the real business world. Areas such as business intelligence and BPM, analysis of loan dataset are the most common applications under probability. Probability is used in these areas to make predictions on business change in terms of profit and clients’ connection. Probability is used in many disciplines to forecast what to expect. For example, in business where risks are involved, managers use probability tools and concepts to manage expectations. There are five main types of probabilities;
Empirical probabilities- This method uses historical data to make forecasts. It assumes that the past happenings can guarantee future results
Subjective probabilities- This type of probability depend on good judgement and experience to make forecasts. It is unique and greatly depends on the talent of the person making it.
Priori probabilities- this type of probability depends on deduced reasoning about a particular case.
Unconditional probabilities- this type of probability answers the question of the probability of one event occurring
Conditional probability- it answers the question of the probability of an event occurring given that another event had already taken place.
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Sampling Homework Help in R Programming
Sampling is a method used to make a general judgement on a huge population by collecting data from representatives of the population. It is one of the many topics in statistics that require a student to be knowledgeable in methods of collecting, analysis, interpreting and presentation of data. The methods employed by a researcher when conducting a sampling experiment depends on the type of research. Inferential statistics computation is a popular topic under sampling using R. A researcher is obligated to take data from samples and make generalizations about the affected population. The following are the methods of sampling commonly used by researcher;
- Simple sampling
- Random sampling
- Stratified sampling
- Clustered sampling
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