For instance, one study found that whole-body fat content in men could be predicted using information on thigh circumference, triceps and thigh skinfold thickness, biceps muscle thickness, weight, and height. The independent and dependent variables are, by convention, referred to as “x” and “y” and are plotted on horizontal and vertical axes, respectively.Īt times, one is interested in predicting the value of a numerical response variable based on the values of more than one numeric predictors. This can be done using “simple linear regression” analysis, also sometimes referred to as “linear regression.” The variable whose value is known (MUAC here) is referred to as the independent (or predictor or explanatory) variable, and the variable whose value is being predicted (BMI here) is referred to as the dependent (or outcome or response) variable. As a next step, they may be tempted to ask whether, knowing the value of one variable (MUAC), it is possible to predict the value of the other variable (BMI) in the study group. If the dots fall roughly along a straight line, sloping either upwards or downwards, they would conclude that a relationship exists. In such a situation, as we discussed in a recent piece on “Correlation” in this series, the researchers would plot the data on a scatter diagram. For instance, in a recent study, researchers had data on body mass index (BMI) and mid-upper arm circumference (MUAC) on 1373 hospitalized patients, and they decided to determine whether there was a relationship between BMI and MUAC. values of one characteristic vary depending on the values of the other. We often have information on two numeric characteristics for each member of a group and believe that these are related to each other – i.e.
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