 तो और किुग काकलि लिए will क्योगा, उगर से थो बगर थी ख़ा था क्योगा. प्यम टैकिझी की ज्योगा एंएं। तो में जालन समप्रतार थी कोर्लेशन लेईस कुब ढोग, if we have one dichotomous independent variable and one continuous dependent variable we can still use a PSN correlation method the second one is that each subject must provide a score on both variables as I told you, one subject's X and Y is important for both of them i.e. one data point's X and Y score on both of them both pieces of information must be from the one same subject the third assumption is that the observations that make up the data must be independent of one another i.e. each observation of my amendment must not be influenced by any other observation or my amendment so on both of our X and Y, both observations are independent i.e. they are not affected by any other variable because on each variable should be normally distributed the fourth assumption is that our underlying distribution will be normal which is the assumption of every parametric test the relationship between the two variables should be linear the fifth assumption is our linearity whenever we use the PSN product moment our underlying assumption is that both the variables are linear i.e. both are positive and negative there are different methods for non-linear and there are other correlation techniques but for PSN we assume that the data we are assessing is linear so this means that when we look at a discrete plot of scores there should be a straight line roughly or that tend to form a line not a curve so here is an example now we will put it in the SPSS data and we will run it in the SPSS so this is an example our researcher reports the annual number of series crimes and the amount spent on crime prevention the data are given in the table so X variable is the number of crimes and Y variable is the amount spent for prevention and this amount is given in millions compute the PSN correlation to measure the degree of relationship between the two variables and we have given the alpha to the level of 0.01 we have to do a two-tailed test i.e. we are not assuming that this is positive or negative but we will just be using a two-tailed test and then running on alpha 0.01 so let's go to the SPSS and enter the data there so here is the SPSS data sheet you are pretty much familiar now we will enter the data from this example or quickly we will enter it 3, 4 so these two variables are our example of crime or prevention so crime we have put data in the X variable or in the Y variable I have given the name in the variable view X variable is equal to crimes and then prevention and the other things are left by default but where we have to tell it that the level of measurement is our scale the scale means that the interval scale is the continuous data now what we will do now we will go to the analysis and we will go to the correlate and we will take out the two variables and this by default the PSN product moment is already checked this is our already checked PSN correlation is to be taken out so you will select both the variables you will send it here and in this two tails are already there and our flag significant correlation is also there in options you can take our mean and standard variations that what are the variables let the cross product be because it already calculates the value of the correlation so we don't have to check anything else this is the kind of flag significant correlation this means that the correlation which is significant will be attached to it we will ok it and we will get the output we will go back to the power point and then we will discuss the output there