The strength of the correlation is determined by the correlation coefficient, r. It is sometimes referred to as the Pearson product moment correlation coefficient in honor of its discoverer, Karl Pearson, who first introduced the term in 1900. There are three different formulas used to calculate this number: the raw score formula, the deviation formula or the covariance formula.
Know MoreThe correlation coefficient measures the degree of a linear relationship between two variables. These variables are usually labeled X and Y. Correlation is similar to regression, but different in how it relates two variables to one another. Regression is concerned with using one variable to predict another. Correlation looks at the relationship between the two variables. The correlation coefficient has either a positive or a negative sign. The sign describes the direction of the relationship between the two variables. A positive sign and a positive correlation coefficient indicates that when the value of one variable increases, the value of the other increases as well. Similarly, if the value of one variable decreases, the value of the other variable decreases if the correlation coefficient is positive. If the correlation coefficient is negative, when the value of one variable increases, the value of the other variable decreases and vice versa.
Learn more about StatisticsThe coefficient of variation is used in statistics to measure distribution. It can be found from the ratio of the standard deviation over the mean of a set of numbers to calculate both probability and frequency. When it is used in finance, the mean is considered the expected return.
Full Answer >The average weight for a 10-year-old boy at a height of 4'11'' is 100 pounds. A 10-year-old boy weighing more than 123 pounds is considered to be obese according to the body mass index scale.
Full Answer >Figuring percentages is determining how many of something is per 100. For example, 50 percent is the same as 50 per 100.
Full Answer >The purpose of statistics is to allow sets of data to be compared so that analysts can look for meaningful trends and changes. Analysts review the data so that they can reach conclusions regarding its meaning. Statistics allow people to see how things are or are not correlated and how a change in one variable might affect another.
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