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Business statistics

Just like the name suggests, business statistics draws concepts and tools from elementary statistics and applies them in business. For instance, probability can be used to project the chances of a defect coming off a factory line or future sales. Business statistics tools are founded on concepts such as mean, mode, median, basic probability, bell curve, bar graphs, regression analysis, etc. This subject provides managers with the information they need to make commercial decisions under uncertainties. Business statistics requires knowledge of data collection, description, and interpretation. You need this to understand and analyze economic theories as well as identify connections between variables and how they affect productivity, price, supply, etc. Do not get upset if your business statistics assignment is giving you a hard time. Consider procuring our low-cost service.

Biostatistics

In biostatistics, we apply statistical techniques in scientific research, and health-related fields. The knowledge of biostatistics is extensively used in public health, biology, medicine, etc. It helps researchers develop new tools that support various studies in the aforementioned fields. Biostatistics has grown in popularity since the twentieth century and has now become an indispensable tool in the elimination of illnesses and improvement of human health. Biostatisticians employ the methods and theories of statistics in designing studies and analyzing data used in research problems. Clinical researchers use biostatistics to draw references from their data, design trials, and development of protocols. Other important uses of biostatistics include data analysis and reporting, design of research framework, data monitoring, etc.

Inferential statistics

Inferential statistics are used by researchers to reach conclusions that extend beyond the immediate data. For example, we might use inferential statistics to know from our sample data what the entire population might think. Most of the widely used inferential statistics are drawn from the general linear models. Some of the examples of these models include Analysis of Variance (ANOVA), regression analysis, t-tests, Analysis of Covariance (ANCOVA), and other multivariate methods like multidimensional scaling and discriminant function analysis. It is essential that any student who wants to carve a niche in social research, then you have to be familiar with the workings of inferential statistics. The use of inferential statistics greatly depends on the research design used. There are two main types of research designs, quasi-experimental and experimental analysis.

Descriptive statistics

Descriptive statistics describe the features of data used in a study. It provides measures and a summary of the sample. Descriptive statistics is the basis of virtually all quantitative analysis of data. Unlike inferential statistics that go beyond the data, in descriptive statistics, we only describe or explain what the data shows. Descriptive statistics provides researchers with quantitative descriptions in a form that can be managed. It helps researchers simplify and make sense of a mammoth of data by reducing the data to a simpler summary. Descriptive statistics can be divided into measures of spread or variability (standard deviation, kurtosis and skewness, minimum and maximum, and variance) and measures of central tendency (mean, mode and median). Put an end to your descriptive statistics assignment misery by opting for our service.

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