![]() To learn more about Scatter Plots please watch this short educational video. The statistical test to use to test the strength of the relationship is Pearson's Correlation Coefficient, also known as Pearson's r. The scatter plot is interpreted by assessing the data: a) Strength (strong, moderate, weak), b) Trend (positive or negative) and c) Shape (Linear, non-linear or none) (see figure 2 below).Ī scatter plot could be used to determine if there is a relationship between outside temperature and cases of the common cold? As temperatures drop, do colds increase?Īnother example (see image below), is there a relationship between the length of time of a consultation with a doctor in outpatients and the patients level of satisfaction? The closer the points hug together the more closely there is a one to one relationship. The scatter plot is used to test a theory that the two variables are related. The purpose of the scatter plot is to display what happens to one variable when another variable is changed. A scatter plot is composed of a horizontal axis containing the measured values of one variable (independent variable) and a vertical axis representing the measurements of the other variable (dependent variable). Although these scatter plots cannot prove that one variable causes a change in the other, they do indicate, where relevant, the existence of a relationship, as well as the strength of that relationship. Remember a correlation does not imply causation.Scatter plots (also known as Scatter Diagrams or scattergrams) are used to study possible relationships between two variables (see example in figure 1 below). There are many other factors that could influence both, such as medical care and education. The fertility rate does not necessarily cause the life expectancy to change. Caution: just because there is a correlation between higher fertility rate and lower life expectancy, do not assume that having fewer children will mean that a person lives longer. It appears that there is a trend that the higher the fertility rate, the lower the life expectancy. ![]() This correlation would probably be considered moderate negative correlation. It looks a little stronger than the previous scatter plot and the trend looks more obvious. Graph 2.5.4: Scatter Plot of Life Expectancy versus Fertility Rate for All Countries in 2013Īgain, there is a downward trend. Let’s see what the scatter plot looks like with data from all countries in 2013 ("World health rankings," 2013). To adjust the position of the label on the scatter plot, we use the. The statcor () function takes method as an argument to decide which correlation coefficient we need to add on scatter plot, for example, Pearson, Spearman, or Kendall coefficient. The trend is not strong which could be due to not having enough data or this could represent the actual relationship between these two variables. The statcor () function is used to add correlation coefficients with p-values to a scatter plot. What this says is that as fertility rate increases, life expectancy decreases. ![]() Graph 2.5.3: Scatter Plot of Life Expectancy versus Fertility Rateįrom the graph, you can see that there is somewhat of a downward trend, but it is not prominent. Note: Always start the vertical axis at zero to avoid exaggeration of the data. The vertical axis needs to encompass the numbers 70.8 to 81.9, so have it range from zero to 90, and have tick marks every 10 units. The horizontal axis needs to encompass 1.1 to 3.4, so have it range from zero to four, with tick marks every one unit. ![]() In this case, it seems to make more sense to predict what the life expectancy is doing based on fertility rate, so choose life expectancy to be the dependent variable and fertility rate to be the independent variable. Sometimes it is obvious which variable is which, and in some case it does not seem to be obvious. To make the scatter plot, you have to decide which variable is the independent variable and which one is the dependent variable. \): Life Expectancy and Fertility Rate in 2013 Countryįertility Rate (number of children per mother)
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