Recent content by awkhan

Understand , it does all these that you mentioned. Along with that we do test statistic(Hypothesis Testing) to check if 2 variables are statistically significant or not. Why do we do this, if we can do it by using correlation, which can check if 2 variables are related or not

1) Why do we do statistical tests to check if the independent variables used in the regression are statistically significant or not, when it can be checked using correlation? 2) My understanding is when we have only one continuos variable - we check if the data given is normally distributed or...

4) My understanding on t - test is when we have one regressor (i.e. one independent variable) , we carry out t- test to check if the variable is significant or not (i.e X (Indep. vatiable) has a direct relationship with Y variable or not) by carrying out the hypotehsis test. As we are working on...

Thanks for all response. can you help answer the below queries as well 1)Do we need to carry out hypothesis tests before carrying out prediction like regression - to ensure that samples taken are coming from the same population. If not, when these tests are carried out. 2)Regression requires...

I am bit confused..can you help answer the below queries. So z-test or t-test is carried out only to make inferences about the population? t-test is preferred over z-test when the sample size is >=30? For prediction we dont require any of these tests. if yes, is there any pre-requisite that we...

so are we saying here that t-test dont need the data to be normalised if the sample size is >= 30....if that is the case how will it handle accuracy of the prediction....because here we are dealing with samples and we are making inferences about something which we dont know

I understand that we go for t test in regression , after the data has been normally disributed using z distribution. Need to know why cant we use t - test in all the phases , i mean why is it required that t- test requires that the data is normally distributed.....why t-test can't handle this...

I dont have any data set/dump. The basis of my question is when the data has outliers, we can visualize to look for any inconsistencies in the data. I didn't understand the other part - what do you mean by "just because a observation is >/< 2 SD doesn't mean it needs to be removed or...

I am not able to distinguish the outliers - When to go with std. dev and When do we need to go with Median. My understanding on std. dev. is - if the data is away from mean by more than 2 std dev. we consider that as outlier. Similarly for Median, we say that any data that is not in-between q1...