Hi, this tutorial covers a binomial question type. Let's just start by looking at some different variables and thinking about what kind of data would come out of each of them. So if we take a look at number one, gender. So if we think about what type of data would we be gathering there? So gender, we would basically be getting people to either answer male or female if they're asked what their gender was.
Question 2, status of the last shot a basketball player made. So really three, the basketball player either made the shot or missed the shot. So we would be just keeping track of how many makes and how many misses.
3, answer the question, did an alarm clock wake you up this morning. So if the alarm clock went off and that's all you woke up, you would say yes. If not, if you had the luxury of not setting an alarm clock, you would say no. So your data there would be yeses and noes.
And for number 4, outcome of a coin flip. So if we wanted to know what was the outcome of a coin flip. It would either be a head or a tail.
So all these all of these variables have something in common. And each of these are what are called binomial question types. And they would all produce binomial question types data. So binomial question type data is a type of categorical data, that's important, that only has two possible values, OK, 2. And we have that prefix bi, meaning two, so binomial, two categories. Yes/no questions are very common producers of binomial question type data.
So now let's take a look at a couple situations here and see if these would produce binomial question type data.
So number 1, a telephone surveying a group of 200 people to ask if they voted for President Obama in the last election. So again, we're looking for two categories as possible data values. So yes, this would produce binomial question type data because they would either answer yes or no.
Question 2, counting the average number of patients seen at a doctor's office daily. OK, so if we were to collect data here, we might get 25, 31, 17 would be our possible data values. In this case, this would not produce binomial question type data, because there are really all kinds of different answers that they could give here. So this would be a no. Number 1 was yes.
Number 3, you take a survey of 20 traffic lights in a certain city at 5:00 PM recording whether or not the light was green-- I'm sorry, red, green, or yellow at that time. So we do have categorical data. Our options are red, green, or yellow. But this is not binomial, because there are three options instead of just two. So, no, this does not produce binomial question type data.
And 4, you are at a fair playing pop the balloon with five darts. There are 30 balloons. 15 of the balloons have a ticket inside that say win. 15 have a ticket that say lose. So you'd want to be keeping track whether for every dart thrown is it a win or lose. So in this case, yes, this is binomial question type data, because your options are either win or lose.
So that is the tutorial on binomial question type. Thanks for watching.
Hi, this tutorial covers open versus closed questioning. So let's just start with a couple different questions here. So think about the differences in the following two types of questions
So type 1, "what are your goals for the future? How did you decide on your current profession? Why do you live where you live?" Now type 2, "how many children do you have? Do you like to eat tomatoes? What is your favorite restaurant?"
So both of those two questions are obviously pretty different. Type 1 are your open questions, sometimes called open ended questions. Type 2 are your closed questions.
So again, for type 1 they're open because you're going to give lengthier responses. The predictability is going to be a lot less when you have open questions. Type 2, these are all closed questions. How many children do you have?
Well, you just give them the number. You say 1 or 2 or 0. Do you like to eat tomatoes? That's really a yes or no question. What is your favorite restaurant? They're just going to name the restaurant there. So these are all closed questions, because they're going to be predictable.
So let's take a look at the two definitions now. So an open question is a question that has no definite length in the expected response. So again, how did you decide on your current profession? You're not really going to be able to have a definite length of how you would expect somebody to answer that question.
Whereas a close question is a question that has a definite short response in mind. A yes or no question is a common closed question. So what is your favorite restaurant? They're just going to give a definite short answer. They're going to say Applebee's or McDonald's, or they'll just give out that short answer.
When collecting data for analysis, closed questions are often best. You want to be predictable in your responses. Generally, you can get a lot of good information about open questions. But it's going to be harder to summarize that information, classify it. If you're going to do any graphing, it's impossible to graph answers to open questions. So again, answers to close questions can be easily classified and summarized whereas open questions cannot.
One thing, though, about closed questions is make sure that it applies to those in your sample. Don't ask the question when did you stop smoking to a group that includes those who have never smoked. So this is definitely a close question. When did you stop smoking? Six months ago, two years ago.
But that question doesn't make sense if somebody has never smoked before. So just make sure that when you're answering a closed question since they're only going to have a certain number of ways of responding, make sure that the question applies to them.
So this is has been your tutorial on open versus closed questioning. Thanks for watching.
