Statistics is a form of mathematical analysis that uses quantified models, representations and synopses for a given set of experimental data or real life studies. Statistical analysis involves the process of gathering and evaluating data and then summarizing the data into a mathematical form. Statistics is a term used to summarize a process that a statistician, mathematician or analyst uses to characterize a data set.
Statistics studies approaches to gather, review, analyze and draw conclusions from data. If the data set depends on a sample of a larger population, then the analyst can develop interpretations onto the population primarily based on the statistical outcomes from the sample. Some statistical measures include mean, [regression analysis], [skewness], [kurtosis], variance and [analysis of variance].
Statistical methods are established to analyze large volumes of data and their properties. Governments and companies use statistical methods to compute a joint property about employees or people. These properties influence the decisions utilized by the governments and companies. Statistics is used in various disciplines such as psychology, business, physical and social sciences, humanities, government and industry.
Statistical data is gathered using a sample procedure or other method. Two types of statistical methods are used in analyzing data, descriptive statistics and inferential statistics. Inferential statistics is used when data is viewed as a subclass of a specific population; inferences are used to reach conclusions about a larger undetected population. Descriptive statistics is used to synopsize data from a sample exercising the mean or standard deviation.
<>Regression Analysis<>
Regression analysis determines the extent specific factors such as interest rates, the price of a product or service, or particular industries or sectors influence the price fluctuations of an asset. This link is typically depicted in the form of a straight line called linear regression that provides the best estimates of all the individual data points.
<>Mean<>
A mean is the mathematical average of a group of two or more numerals. The mean for a given set of numbers can be computed in multiple ways, including the arithmetic mean method, which uses the total of the numbers in a sequence, and the geometric mean method, which decides the performance results of an investment portfolio.
<>Kurtosis<>
Kurtosis is a measure of whether the data are heavy-tailed or light-tailed in relation to a standard distribution. Data sets with positive or high kurtosis have a propensity to have heavy tails. Data sets with negative or low kurtosis are predisposed to have light tails.
<>Skewness<>
Skewness describes the degree a set of data varies from the standard distribution in a set of statistical data. Most data sets, including stock prices and commodity returns, have either positive skew, the curve is skewed toward the left or negative skew or the curve is skewed toward the right of the data average.
<>Analysis of Variance<>
Ronald Fisher developed the analysis of variance method. It is an analysis tool that separates the total volatility located in a data set into two parts: random and systematic factors. It is also used to decide the effect solitary variables have on the dependent variable in a regression analysis.
<>Variance<>
Variance is a measurement of the span among numbers in a data set. The variance measures the distance each number in the set is from the mean. Variance can help determine the risk an investor might accept when buying a specific holding.
<>History<>
The word statistics was derived from the Latin word status, which means government or political state. In the 18th century, statistics implied the collection of economic and demographic data by states. In the early 19th century, Francis Galton, Karl Pearson and William Gosset drove work in the field. The field expanded to include data gathering, summary, analysis and probability theory. Statistics is presently used in all sciences, business and government.