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# Best Statistics Research Topics & Ideas For 2021-22

Date published October 7 2021 by Jacob Miller Statistics is the elegant discipline to organise, collect, analyse and present data. To do so, one must follow the standard protocols of statistics. This study can be utilised in many areas such as the industrial and social sectors.

The two fundamental ideas of statistics are variation and uncertainty. Certain statistical frameworks and methods are applied to get the results. Statistical methods come in handy when the user has to collect data.

## Subfields of Statistics

The subfields of statistics are Probability Theory and Mathematical Statistics, Design of Experiments, Sampling, Classification, and Time Series.

All of these subfields are used in everyday life. The probability theory is applied in many studies, it is the mathematical language that is also the building block of many research topics. Statistics helpers understand the research topic and control the variations to get the desired results. Well-done research uses statistics to get their data from participants.

## Importance of Statistics Subject in Student Life

Students that take statistics as their major for bachelors, master’s, and Ph.D. are expected to submit a dissertation at the end of their academic year. The statistics research topics contain the deep study of how the fundamentals and rules of said study are made.

Good statistics dissertation topics are hard to find since the mathematical depth of the study does not allow a lot of space to research your choice.

## List of Best Statistics Research Topics with Objectives

As a statistic student, you must always be on a search for remarkable statistics topics for your dissertation. Well, we have that covered for you, read further to get the right topic for you.

Objectives:

1. To explore all new bayesian methods which are used in statistical analysis.
2. To introduce new methodology of bayesian which are suitable  for functional and time series data.
3. To exhibit the functional challenges provided by the methodology.

Objectives:

1. To explore the methods of kernel  regression

2. To demonstrate  the method  of speeding up the computation of kernel.

3. To analyse the FFT to improve the computation of kernel.

Objectives:

1. To explore the importance of statistics and probability.

2. To examine the different methods of statistics and probability used in education system.

3. To provide the need for collaborative and cross-disciplinary in researches.

Objectives:

1. To explore the concepts behind the usage of statistics in different domains.

2. To examine the concept of statistics in Second Language.

3. To study and implement the SPSS software in statistics.

Objectives:

1. To study the importance of Prediction in statistics.

2. To analyse the statistical Prediction methods in statistics theory.

3. To examine the different methods of Prediction interval under the parametric framework.

Objectives:

1. To study the importance of statistical tools and significance test both in parametric and nonparametric test.

2. To examine the statistical tools significance in decision making.

3. To evaluate the statistical significance test in information retrieval.

Objectives:

1. To study the statistical methods for the variable selection in ultra-high dimensional functional linear models.

2. To propose two forward selection procedures on the basis of coefficients approximation.

3. To demonstrate the application of the proposed methodologies.

Objectives:

1. To explore the different method of Bayes and its applications.

2. To examine the Bayes method for the purpose of biclustering and inference for mixture models.

3. To represent the performance of model through the simulation and applications to real datasets.

Objectives:

1. To study the concept behind the RNA- sequence data analysis and its procedure.

2. To examine the papers on the analysis of RNA- sequence data analysis.

3. To perform a simulation and validate the proposed methods on the basis of results.

Objectives:

1. To explore the techniques used in data analytics used for various purposes in order to produce visual charts.

2. To demonstrate the use of python language as a main feature in Data analytics.

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