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In order to improve the effect of agricultural data mining and statistics in the Internet era, based on data mining technology, this paper combines data mining technology with modern agricultural information to solve the problems of poor timeliness and incomplete information in the collection and sorting of agricultural information. Moreover, this paper makes innovations in time series representation and time series measurement, and uses custom methods to improve agricultural data mining and statistical algorithms. In addition, this paper finds reasonable mining methods to acquire knowledge through data analysis. Finally, this paper combines the actual needs to construct a data mining and statistical analysis model of precision and intelligent agriculture based on big data analysis, and designs experiments to verify the performance of the model. The research results show that the model constructed in this paper has a certain effect. [ABSTRACT FROM AUTHOR]

We agree that converting data to similar resolutions would lose some statistical information from our data. We thank gratefully Professors Wei Tu, Bei Jiang, Linglong Kong, and Professor Sudipto Banerjee for their insightful comments and discussion of our paper. The inference of the occupancy status of houses sharing one location might require more information at the individual level, which is the primary data barrier in social science. [Extracted from the article]

This paper introduces a dataset and associated analysis of the scores of National Football League (NFL) games over the 2012, 2013, and first five weeks of the 2014 season. In the face of current media attention to “lopsided” scores in Thursday night games in the early part of the 2014 season,t-test results indicate no statistically significant difference between the winning margins in Sunday games vs. Thursday games during the 2012 and 2013 seasons. Interestingly, there is a statistically significant difference between the Sunday vs. Thursday game margins over the first five weeks of the 2014 season. Moreover, statistical process control methods suggest an “out of control” condition for Thursday night game margins during the first five weeks of the 2014 season. The exercise provides students with an opportunity to apply a variety of hypothesis testing and graphical analysis tools to a question of current interest in the popular media. [ABSTRACT FROM AUTHOR]

We review the most important statistical ideas of the past half century, which we categorize as: counterfactual causal inference, bootstrapping and simulation-based inference, overparameterized models and regularization, Bayesian multilevel models, generic computation algorithms, adaptive decision analysis, robust inference, and exploratory data analysis. We discuss key contributions in these subfields, how they relate to modern computing and big data, and how they might be developed and extended in future decades. The goal of this article is to provoke thought and discussion regarding the larger themes of research in statistics and data science. [ABSTRACT FROM AUTHOR]

The era of big data has witnessed an increasing availability of multiple data sources for statistical analyses. We consider estimation of causal effects combining big main data with unmeasured confounders and smaller validation data with on these confounders. Under the unconfoundedness assumption with completely observed confounders, the smaller validation data allow for constructing consistent estimators for causal effects, but the big main data can only give error-prone estimators in general. However, by leveraging the information in the big main data in a principled way, we can improve the estimation efficiencies yet preserve the consistencies of the initial estimators based solely on the validation data. Our framework applies to asymptotically normal estimators, including the commonly used regression imputation, weighting, and matching estimators, and does not require a correct specification of the model relating the unmeasured confounders to the observed variables. We also propose appropriate bootstrap procedures, which makes our method straightforward to implement using software routines for existing estimators. for this article are available online. [ABSTRACT FROM AUTHOR]

Although the use of tables, graphs, and figures to summarize information has long existed, the advent of the big data era and improved computing power has brought renewed attention to the field of data visualization. As such, it is crucial that introductory statistics courses train students to become critical authors and consumers of data visualizations. To that end, we have developed a semester-long, instructor-supported, group project that exposes students to this growing field. We have found this project to be an exciting and effective way to teach students the power of statistics and, more importantly, the critical role context plays when interpreting statistics. Among the many benefits of this project are hands-on learning, improved mathematical reasoning, and better collaboration and communication skills. In this article, we describe the project structure, project assessment, and techniques for facilitating effective group work. [ABSTRACT FROM AUTHOR]

The big data era demands new statistical analysis paradigms, since traditional methods often break down when datasets are too large to fit on a single desktop computer. Divide and Recombine (D&R) is becoming a popular approach for big data analysis, where results are combined over subanalyses performed in separate data subsets. In this article, we consider situations where unit record data cannot be made available by data custodians due to privacy concerns, and explore the concept of statistical sufficiency and summary statistics for model fitting. The resulting approach represents a type of D&R strategy, which we refer to assummary statistics D&R; as opposed to the standard approach, which we refer to ashorizontal D&R. We demonstrate the concept via an extended Gamma–Poisson model, where summary statistics are extracted from different databases and incorporated directly into the fitting algorithm without having to combine unit record data. By exploiting the natural hierarchy of data, our approach has major benefits in terms of privacy protection. Incorporating the proposed modelling framework into data extraction tools such as TableBuilder by the Australian Bureau of Statistics allows for potential analysis at a finer geographical level, which we illustrate with a multilevel analysis of the Australian unemployment data. Supplementary materials for this article are available online. [ABSTRACT FROM PUBLISHER]