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Advanced or applied statistics is a study that is conducted to collect, organize, analyze, interpret, and present the data using the tools and software. This also includes planning the data that is collected from surveys and experiments. This comprises various topics such as Decision trees, biostatistics, statistical computing, etc. The application of statistics is used in a wide range of areas including social sciences, mathematics, business, pure science, etc. The main job of the person studying applied statistics is to produce and present the statistics.

Many students would look for help to compose applied statistics assignments. This is an interesting yet challenging task for students of different academic levels. We offer statistics assignment help to students across the globe at a fair price. We help the students to solve statistical problems using SPSS and MS Excel software. If you want to get instant help, without hesitation you can seek the help of our statisticians.

Applied statistics would deal with theorems and concepts related to the business along with research and development of statistics. Statistics is used to categorize the data sets. There are various methods that are used to solve mathematical problems, including mean, median, and mode methods.

Statistics are classified into two types. These include theoretical statistics and the other is applied statistics. Theoretical statistics also called mathematical statistics involves statistical theorems, the development of key formulas, rules, and laws that are used to solve problems in real time. Applied statistics would deal with the application of theorems and concepts that are developed in theoretical statistics, especially in research, development, business, government, marketing, and clinical trials. As per the theory, applied statistics is divided into two categories. These include:

**Descriptive statistics:**The datasets that are available in the original form are very huge and are challenging to use directly to jump to a conclusion or make any informed decisions. To draw conclusions from huge chunks of information, you need to calculate descriptive statistics to explain the key traits of the data. The**descriptive statistics**would have summary tables, charts, and graphs and these summary table would comprise of numerical measures like mean, median, mode, standard deviation, etc. The graphs or charts would comprise of pie chart, bar chart, histogram, line plot, scatter plot, etc.**Inferential statistics:**If there are a series of measurements that are given in the dataset of the population, then you can easily draw a conclusion about the dataset depending on the descriptive statistics. It is very expensive and time-consuming to examine the entire population at a time. For this reason, the population is divided into small parts that would answer all the queries about the whole population. The key branch of statistics that allows you to draw a conclusion about the population based on the sample data is known as inferential statistics. The main aim of inferential statistics is to infer the population traits by analyzing the sample data collected from the population.

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Statistics using SPSS, SAS, STATA, R | Statistics using SPSS, SAS, STATA, R |

Comparing groups means using the independent samples t-test | One-way between-subjects analysis of variance |

Bivariate Pearson correlation | Multiple regression |

Factorial analysis of variance | Analysis of covariance |

Discriminant analysis | Multivariate analysis of variance |

Principal components and factor analysis | Reliability, validity, and multiple-item scales |

Analysis of repeated measures | Binary logistic regression |

Biostatistics | Chebychev’s inequality |

Decision tree | Statistical computing |

Statistical computing | Categorical analysis |

Statistical methods in genetics, public health, biomedical research, etc | Reliability engineering |

Chemometrics | Bayesian statistics |

Grid-based estimators | Robotics |

Approximation theory | Fiducial inference |

Generalized additive models | Kernel density estimation |

Martingale difference | Lyapunov |