You can have any two variables. In fact, you can also have just one variable over two points in time - for example, a scatter plot of the price of something plotted against the price of the same thing a year earlier.
The answer depends on how many variables you wish to compare and whether of not the variables are related. For example, if you have measurements of height and weight for a group of people, AND the two variables are tied, then a scatter graph is probably best. But if you cannot tie a height measurement to a weight measurement, then a clustered (or grouped) bar graph using two vertical axes is appropriate. With more than 2 variables, you may wish to consider three-dimensional scatter plots or bar charts. If you want to compare the amounts, as parts of a whole, concentric pie charts many be best. For example, two pies in which the inner pie represents a company's expenditure - with each slice representing inputs - and the outer pie representing revenues - with each slice representing outputs. The difference between the areas of the two pies could represent the profits.
1.3 in a number line is between 1 and 2 .
variables are used to substitute for an unknown number. john is 10 years old. harry is 2 years older than him. 10+2=x x=12
Data that is too narrow or too wide (small or large) as it cannot fit into a steam and leaf plot considering the purpose of a steam and leaf plot (how it breaks down the numbers that have 2 or more digits). A steam and leaf plot has 2 separate columns that separate the value of numbers into those 2 sections (although if there is less than 2digts or if there are too many, the process will fail).
You can have any two variables. In fact, you can also have just one variable over two points in time - for example, a scatter plot of the price of something plotted against the price of the same thing a year earlier.
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It depends on the kind of data you have, but a scatter plot or bar graph would be best.
If you have 2 sets of data, one that is independent and one that is dependent (I will assume this because relating two sets of unrelated data is useless), then you plot the independent on the x and the dependent on the y and assess how y changes in relation to x
Algebraic expressions may contain variables but they are not normally called variables. In fact, if they are related to identities, they need not be variable. For example, (4x2 + 8xy + 4y2)/(x + y)2 is an algebraic expression, but it is not a variable: it equals 4.
A direct correlation, it appears as a straight line on a graph and occurs when variables are related as y=xk.
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The line of best fit is a concept in statistics rather than algebra. Given two variables, X and Y, they can be plotted (a scatter plot). Before going any further, it is good practise to look at the scatter plot and see if the points look like they are approximately in a straight line. If not, a line of best fit is not appropriate: you may need to transform one or both variable.For each observed value, xi, there will be a value yi where i ranges over all the observations: that is, your data comprises the set of ordered pairs (xi, yi). You can then draw a line over the scatter plot. Many points will not lie on the line but some distance above or below it. If the value on the line. corresponding to xi is yi-fitted, then ei = (yi-fitted - yi) is the error between the fitted and observed values. Then the line of best fit is the one which minimises the sum of (ei)2.It is not easy to explain this in plain text, and more so when you are handicapped by a rubbish browser. Although the calculations may look daunting, they are not that bad, and there packages which will do away with the drudgery.
Qualitative and quanitative are two types of variables.
There are 2 variables and they are independent and dependant.
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It depends on the number of variables and their nature: 2 variables, both independent: either axis 2 variables, one independent: x-axis 3 variables, all independent: any axis 3 variables, 2 independent: x or y-axis. 3 variables, 1 independent: x-axis. and so on.