Relationship between Linear Algebra and Statistics Linear algebra can be regarded as the arithmetic of linear substitution (Edwards, H. M., 1995). Matrices and linear substitutions are effectively the same. Statistics, on the other hand, in a broad sense is the science of collecting, organizing, analyzing and interpreting data. Statistics find applications in education, research, business, health, engineering, athletics, medicine and a lot more of the fields. Typical examples of statistics are those that deal with average rainfall and temperature, birth and death rates, average snowfall, crime rates, political popularity and much more. Even though statistics is usually studied as a course on its own, understanding basic statistical concepts is requisite for any student pursuing any field of study. This is because the student will be required to conduct research in his own field of study. Hence there will be need to know how to design experiments, gather data, organize, analyze and summarize data to draw conclusions or predictions based on the findings of the research. Statistics are encountered by just about anybody for instance in the magazines, news papers, television and so on. Therefore, basic understanding of statistical vocabulary, procedure and concepts is helpful in avoiding getting mislead by misleading data and information especially when you are a consumer of a product. Statistics as a field has strong relations and dependence on linear algebra. Descriptive statistics, for instance, uses algebraic summation so often (Frank, H., & Althoen, S. C., 1994). The data of various variables are summed up or the probabilities of events are summed. The key areas in statistics that have a stronger bias in linear algebra or applies linear algebra a lot are: problems in multivariate distributions, integrals and distributions, interdependence properties and characterization of distributions, probability inequalities, orderings, and simulations and much more (Johnson, C. R., & American Mathematical Society, 1990). From the look of these statistical topics it is very clear statistics converge with linear algebra in a lot of occasions. In this paper, I am going to study the linear correlation in statistics and show how it uses linear algebra to achieve its statistical objectives. Variance and Covariance of a Statistical Data Variance measures spread or variability in a data set. It is the average of the squared deviations from the mean. The formula is Where Covariance is the measure how corresponding elements from two ordered data sets seem to grow in a common direction. The formula for covariance is Variance-Covariance matrix This is a matrix which presents variances as diagonal elements and co-variances as off-diagonal elements. Variance-Covariance matrix appears as below. To create the variance-covariance matrix; · We transform the row scores from matrix X into deviation score for matrix x as · Computing x’x · Divide each term in th ...