#quantitative variables

CONTINUOUS VARIABLE

Continuous variables are numerical variables that can assume any value within a specific range. If the values of a variable can dbe devided into practoons we call it Continuous variable.

This, in particular, is a kind of quantitative variable often used in machine learning and statistical modeling to describe data that is measurable in some way. Continuous variables are generally measured on scales such as height, weight, temperature, etc.

Continuous variables are the ones which in between any two numeric values have an infinite number of values. Example of Continuous variable: Counting the amount of money in everyone’s bank account.

The picture below will help you understnad more of the differences between Discrete nd Continuous Variables.

DISCRETE VARIABLE

A discrete variable is a kind of statistics variable that can only take on discrete specific values. It a variable that takes on distinct, countable values. It is also a numerical variable that can only take on a finite numberof distinct values within a given range.

For example, you can count the change in your pocket. You can count the money in your bank account. You could also count the amount of money in everyone’s bank accounts. It might take you a long time to count that last item, but the point is—it’s still countable.

CONFOUNDING VARIABLE

Confounding variable is a type of Extraneous that also related to dependen and independent variables. A Confounding variable  is an unmeasured third variable that influences both the supposed cause and the supposed effect. Even if you correctly identify a cause-and-effect relationship, confounding variables can result in over- or underestimating the impact of your independent variable on your dependent variable.

Make sure you identify all of the possible confounding variables in your study. Make a list of everything you can think of and one by one, consider whether those listed items might influence the outcome of your study. Confounding variables can ruin an experiment and produce useless results. They suggest that there are correlations when there really are not. 

DEPENDENT VARIABLE

Dependent variable is a variable that is being measured or tested in an experiment.

A dependent variable is the variable that changes as a result of the independent variable manipulation. It’s the outcome you’re interested in measuring, and it “depends” on your independent variable.

The dependent variable is what you record after you’ve manipulated the independent variable. A dependent variable from one study can be the independent variable in another study, so it’s important to pay attention to research design.

One way to help identify the dependent variables is to remember that it is depends on the Independent variable.

A example of a Dependent variable, a test score could be a dependent variable because it could change depending on several factors such as how much you studied, how much sleep you got the night before you took the test, or even how hungry you were when you took it. Usually when you are looking for a relationship between two things you are trying to find out what makes the dependent variable change the way it does.

Another clear example:

WEEK 5: ASSIGNMENT 8 - PEARSON CORRELATION COEFFICIENT

  Q->Q: Quantitative vs Quantitative

I am going to analyze the relationship between the following two quantitative variables, with the Pearson Correlation Coefficient, r.

S2AQ8C – Largest number of drinks of any alcohol usually consumed on days when drank alcohol in last 12 months  (1-80 drinks)

AGE – Age of adults (30 – 45 years old)

  Figure 1 shows the scatter plot of the two variables (AGE: Explanatory and S2AQ8C: Response). And there you can see a positive negative trend that is a value of r less than zero. Nevertheless, the relationship is not so strong, as you can see.

From the analysis of the Correlation Coefficient, shown in Figure 2, we determine that there is a significant relationship (p<,0001 that is p<0,05) but it is weak, because the r value is very close to zero (r = -0,048).

And Figure 3 shows the procedure used in SAS.