Amsco Integrated Algebra I: Bivariate Statistics
 This is a free lesson from our course in Amsco's Integrated Algebra

 This lesson explains concepts of bivariate statistics or two valued statistics. In bivariate statistics, a relation between two variables or different sets of data is studied. E.g.  A study conducted to see if there were a relationship between the weight and height of school students. Since we are working with two variables (weight and height), we would be working with bivariate data. The data can be graphed on a scatter plot. The data may have positive, negative, or no correlation. Data that has positive or negative linear correlation can be represented by a line of best fit. Further you'll learn how to investigate the relationship or correlation between the variables based on their scatter plots, considering five cases of two-valued statistics: CASE 1:The data has positive linear correlation. The points in the scatter plot approximate a straight line that has a positive slope. (More text below video...)
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 (Continued from above) CASE 2:The data has moderate positive correlation. The points in a scatter plot do not lie in a straight line but there is a general tendency for the values of y to increase asthe values of x increase. CASE 3:The data has no correlation. CASE 4:The data has moderate negative correlation. The points in a scatter plot do not lie in a straight line but there is a general tendency for the values of y to decrease as the values of x increase. CASE 5:The data has negative linear correlation. The points in the scatter plot approximate a straight line that has a negative slope. Then you can walk through the basic cocepts of line of best fit and line of regression i.e. when it makes sense to consider one variable as the independent variable and the other as the dependent variable, and the data has a linear correlation(even if it is only moderate correlation), the data can be represented by a line of best fit. The line of regression is a special case of best fit which minimizes the square of the vertical distances to each data point. The video below will explain in detail with the help of several examples.

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