the statistics homework help

the statistics homework help

The Importance and Application of Statistics in Various Fields

1. Introduction to Statistics

The term statistics is used in two senses today. It denotes quantities usually collected in the form of numerical data. It also denotes a methodological discipline where problems that involve quantitative aspects of the universe are studied systematically. In its first sense, statistics is used both in singular and plural forms; in the other sense, the plural form is always employed. Statistics is a social science. The rational methods by which specific kinds of statistics are obtained are also studied in other scientific disciplines. These methods are employed not only in the social sciences, but also in the field of natural and physical sciences and in business and government. In other words, statistical techniques are used in all walks of life, either explicitly or implicitly.

When people are asked to name sciences, they usually reply by naming such familiar entities as physics, chemistry, biology, and geology. They usually do not mention the name of statistics. And yet statistics is a recognized discipline in the field of science. Not only that, it is one of the most widely used scientific disciplines. There are many careers in academic and departmental fields of statistics. In addition to academic institutions and government departments, there are many business and industrial areas that employ statisticians today. There are other persons who do not have the word statistician in their job titles, but who do practically nothing but statistics. The future for careers in statistics is extremely bright, for with the aid of computers, the decisions that can be made can be greatly improved, and statistical study to allow for even more sophisticated application of the subject to the problems of worthwhile life.

2. Descriptive Statistics

Descriptive statistics organize and describe the characteristics of a collection of data. Descriptive statistics help identify the most significant features of a large set of measurements, such as the major outcomes of a statistical analysis. Once these important features have been identified, the research can focus on them in more detail. Descriptive statistics can be reported graphically (e.g., by bar charts, line plots, or scatter plots) or as reports using averages or percentages.

There are many ways in which descriptive statistics can be used. The most common statistics are the average (mean), the middle value (median), and variation (range), but we can also group the data and count how many times each value occurs (frequency), and find many other interesting aspects of the data. Descriptive statistics are brief descriptive coefficients that summarize a given data set, which can be either a representation of the entire or a sample of a population. Descriptive statistics are used to describe the basic features of the data in a study. They provide simple summaries about the sample and the measures. With simple summaries about the sample and the measures, they form the basis of nearly every quantitative analysis of data. With statistics, we can better plan the future and make important decisions.

3. Inferential Statistics

Statistics refers to the body of scientific methods and procedures used to provide information that can be quantitatively applied to any field of inquiry or investigation. There are two principal branches of statistics: descriptive and inferential.

Descriptive statistics involve handling and analyzing data to obtain quantitatively processed information in the form of tables and statistics. These methods are used to describe different sets of data, provide a summary of the major features of a set of data, or obtain a quantitative measure of the relative position or tendency.

On the other hand, inferential statistics are methods used to make estimates of future data based on quantitatively processed information from observed data in relation to a set of underlying theory. The term “inferential” is used because it is possible to infer the statistics of an observation from the observed statistics of a sample of such observations.

In summary, descriptive statistics is useful when the population is finite (often very small) and corresponding information is easily obtainable. Inferential statistics, on the other hand, is applicable when the population is too large to test and sample information can appropriately serve this purpose, or when the population sample information costs involved will be prohibitive.

Some applications of inferential statistics include the estimation of population characteristics (such as average, variance or standard deviation, proportion, and mean) from sample information, hypothesis testing or testing of relationships between variables, prediction, and process control, among others.

4. Applications of Statistics in Real Life

Statistics has various applications in diverse fields. In a country, statistics data is very important in taking any decision concerning the population, agriculture, medical, health, education, mining, pharmaceutical industry, petroleum industry, biotechnology, and pollution. A population census cannot be complete without the use of statistics in organizing the data collected to provide a meaningful analysis of the information. The formulation of policies, as well as the generation of information to validate plans and programs of government in all the sectors mentioned earlier, is not obtainable without the use of statistics.

In the field of agriculture, statistics is very important in the area of farm survey, which is useful in identifying problems facing farmers and making suggestions on how to overcome the problems. It is also useful in field experimental design to identify the best fertilizer to be used and how the feature affects the result. Statistical techniques are used in the creation and maintenance of a system that can efficiently install plant species and improve the plant and animal kingdom.

In medical research, statistical methods are used in research designs, how it helps to plan experiments and clinical trials, logistic regression analysis in identifying the risk factors, the screening tests which can be used to perform useful distinguishing between the real required test and the created test, and statistical modeling techniques are used to make an inference about the general population from which the sample data is collected.

5. Challenges and Future Directions in Statistics

The increasing need for statistical thinking across scientific disciplines, the rapid accumulation of data, and the development of problem-driven interdisciplinary research has greatly increased sophisticated statistical application to a wide range of scientific topics. This has led to increased interest in near-term statistical modeling and methodological contributions. However, statisticians are facing new and great challenges in these areas.

The sequence of this article is as follows. We use the first one to summarize important and useful aspects in statistical work based on the contributions papers and our experience, including consulting experiences. Next, we comment on challenges in implementing these aspects and in establishing interdisciplinary research collaborations. Model-based data analysis has become the centerpiece of extrapolating available data to answer scientific questions. Models are so embraced in twenty-first-century scientific work that scientists use them both to prove theorems and to disprove them. Throughout, the unifying theme is that statistical work is inevitably interconnected. The acronyms PBR and EDA explain why. They stand for the problem-based approach in statistical research, or in statistical model-based analysis, and for exploratory data analysis. For each, we hope to persuade the reader that avoiding them can lead to error.