6. Statistical Variables

Transcript

Welcome to the next lecture on statistical variables. There are many variables, but we are trying to cover a little bit such that at the by the end of this course you can be able to appreciate and use these variables for your real time application. The first big classification of variable is going to be Discrete variable. Discrete variable means it is going to have full numbers, for example, number of breads loafs which have been manufactured, number of people who are attending a party, number of trees grown in one square area, number of bananas yield in one square kilometer. So, all these things are discrete variable a variable that can only take on a certain number of values is called as discrete variable. For example, you cannot say number of bananas grown in one square kilometer is twelve and a half, right number of fish in one square kilometer you cannot say 254 and a half, so you say 254 number of bananas 12. So that is discrete. When we start talking about Continuous. Continuous means the variables with infinite number of values are called as Continuous Variables. In continuous variables you can have 11.1, 11.111, 11.1111, so you can have, these are all continuous data which keeps changing between eleven to twelve. So, this is called as continuous variable.

The third thing is called as Control variable. When we are trying to do some experiments for example, trying to grow fish in a aqua culture, and then trying to maintain the temperature at 25 degree celsius and see what is the yield or try to maintain the ph level in a certain value, and then try to see what is the output, trying to add certain percentage of nitrogen or phosphorous or ammonia in the soil such that the yield increases. So, here what you do is you try to dictate or control one variable. So, in statistical term if we wanted to do experiments, we call it as one factor at a time, we try to control one variable, and then one level to see what is its influence on the output, those things are called as control variables, or a factor in an experiment which must be held constant. For example, in an experiment to determine whether light makes plant grow faster that is called as control variable.

The next one is called as Binary variable. A variable that can only take one of the two values either 0 or 1, yes or no, tall or short, you can have only two variables. So, those things are called as binary variables handling binary variables needs a special skill but as far as beginners are concerned we always try to take discrete variable, continuous variable, and control variable. When we move further down categorical variable it is also overlapping with that of binary the variable that can be put into categories. For example, fertilizer brand might contain the variable IFFCO or HFCL. For example, soap which falls under uni lever or Godrej something like that, two companies. So you try to do bending only on the say for example, bananas good bad, you do not have a gray area in between. Fish which is of 25 less than 25 centimeter in this more than 25 centimeters those things are called as Categorical variables.

A Measured variable has a number associated with it. For example, Ramkumar is 75 kgs so, it is number which is associated with the weights right. So, it is an amount or something or number of something. You can also say Rs. 5, USD 10 right, so this are something there. Then you can try to have variables. So, measurement variables are numbers which are associated with it or it is an amount of something or a number of something. So, those things are called as measurement variables. The last one which is very important is Independent variable and Dependent variable. Independent variable and Dependent variable when we talk about x, y graph this is your y axis this is your x axis x axis can be time or here y axis can be yield. So, these two axis they do not have any dependency so it is called as independent variable in the same graph if you put two dependent variable then you will never be able to get your plot properly. Dependent variable is for example, if you are trying to boil water right, so when you try to boil water the temperature of the water and if you try to plot against boiling of the water then these two are dependent. If you try to plot with fuel fire that means to the heat applied with respect to boiling these two are independent parameters. Dependent variables are the outcome of an experiment, as you change the independent variable you watch what happens to the dependent variable, okay. So, generally we have to bring in convert all the dependent variable into independent variable and then start doing your experiments to have a better control or understanding of the parameters with respect to your output.

So, in this lecture we were seeing only different types of – variable, discrete, continuous, binary, category, dependent, independent. So, all these things are very important though the list of variables when you take a statistic book it runs for long but these six if you understand it is good enough for doing experiments at the field and trying to interpret the data.

Thank you.