What does continuous mean in statistics

Without numbers, we have no analyses nor graphs. Even categorical or attribute data needs to be converted into numeric form by counting before we can analyze it. At this point, you may be thinking, "Wait a minute—we can't really measure anything infinitely,so isn't measurement data actually discrete, too? If you're a strict literalist, the answer is "yes"—when we measure a property that's continuous, like height or distance, we are de facto making a discrete assessment.

When we collect a lot of those discrete measurements, it's the amount of detail they contain that will dictate whether we can treat the collection as discrete or continuous. I like to think of it as a question of scale. With a scale calibrated to whole pounds, all I can do is put every box into one of three categories: less than a pound, 1 pound, or more than a pound. With a scale that can distinguish ounces, I will be able to measure with a bit more accuracy just how close to a pound the individual boxes are.

I'm getting nearer to continuous data, but there are still only 16 degrees between each pound. The individual boxes could have any value between 0. The scale of these measurements is fine enough to be analyzed with powerful statistical tools made for continuous data. Not all data points are equally valuable, and you can glean a lot more insight from points of continuous data than you can from points of attribute or count data.

How does this finer degree of detail affect what we can learn from a set of data? It's easy to see. Let's start with the simplest kind of data, attribute data that rates a the weight of a cereal box as good or bad. For boxes of cereal, any that are under 1 pound are classified as bad, so each box can have one of only two values.

If we bump up the precision of our scale to differentiate between boxes that are over and under 1 pound, we can put each box of cereal into one of three categories. Here's what that looks like in a pie chart:. This gives us a little bit more insight—we now see that we are overfilling more boxes than we are underfilling—but there is still a very limited amount of information we can extract from the data.

If we measure each box to the nearest ounce, we open the door to using methods for continuous data, and get a still better picture of what's going on. Here is a link to a normal probability table. It is important to note that in these tables, the probabilities are the area to the LEFT of the z-score.

If you need to find the area to the right of a z-score Z greater than some value , you need to subtract the value in the table from one. Because the normal distribution is symmetric, we therefore know that the probability that z is greater than one also equals 0.

To calculate the probability that z falls between 1 and -1, we take 1 — 2 0. This solutions jives with the three sigma rule stated earlier!!! We can convert any and all normal distributions to the standard normal distribution using the equation below. Example Normal Problem We want to determine the probability that a randomly selected blue crab has a weight greater than 1 kg.

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Everyday maths 2 Start this free course now. Free course Everyday maths 2. Activity 1: Presenting discrete and continuous data Match the best choice of graph for the data below. Chart to show favourite drink chosen by customers in a shopping centre. Chart to show the temperature on each day of the week. Chart to show percentage of each sale of ticket type at a concert. Figure 1 Different types of charts and graphs. Long description. The best choice here is d the bar chart as it can show the profit clearly year by year.