Signal related spurious relationship

Specifications Explained: Spurious-Free Dynamic Range (SFDR) - National Instruments

signal related spurious relationship

An example is provided that demonstrates the IP2 and 2x2 relationship for Maxim's For this example, the undesired signal at MHz causes a half-IF spurious spurious response equal to 65dBc under similar conditions which results in. In statistics, a spurious relationship or spurious correlation is a mathematical relationship in which two or more events or variables are not causally related to. We investigated relations in a similar way for other couples of instruments: between audio signals are spurious relationships caused by the score effect.

But then they go on to say, "The researchers write that teens who ate breakfast regularly had a lower percentage of total calories from saturated fat and ate more fiber and carbohydrates.

Breakfast tends to be things like cereals, grains. You eat syrup, you eat waffles-- that all tends to fall in the category of carbohydrates and sugars.

And frankly, that's not even necessarily a good thing. Not obvious to me whether bacon is more or less healthy than downing a bunch of syrup or Fruit Loops or whatever else. But we'll let that be right here. Regular breakfast eaters seemed more physically active than the breakfast skippers. So the implication here is that breakfast makes you more active. And then this last sentence right over here, they say "Over time, researchers found teens who regularly ate breakfast tended to gain less weight and had a lower body mass index than breakfast skippers.

So the entire narrative here, from the title all the way through every paragraph, is look, breakfast prevents obesity. Breakfast makes you active. Breakfast skipping will make you obese. So you just say then, boy, I have to eat breakfast. And you should always think about the motivations and the industries around things like breakfast. But the more interesting question is does this research really tell us that eating breakfast can prevent obesity?

Does it really tell us that eating breakfast will cause some to become more active? Does it really tell us that breakfast skipping can make you overweight or make it obese? Or, it is more likely, are they showing that these two things tend to go together? And this is a really important difference. And let me kind of state slightly technical words here.

Correlation and causality (video) | Khan Academy

And they sound fancy, but they really aren't that fancy. Are they pointing out causality, which is what it seems like they're implying. Eating breakfast causes you to not be obese. Breakfast causes you to be active. Breakfast skipping causes you to be obese. So it looks like they are kind of implying causality. They're implying cause and effect, but really what the study looked at is correlation. The whole point of this is to understand the difference between causality and correlation because they're saying very different things.

And, as I said, causality says A causes B. Well, correlation just says A and B tend to be observed at the same time. Whenever I see B happening, it looks like A is happening at the same time. Whenever A is happening, it looks like it also tends to happen with B.

Specifications Explained: Spurious-Free Dynamic Range (SFDR)

And the reason why it's super important to notice the distinction between these is you can come to very, very, very, very, very different conclusions. So the one thing that this research does do, assuming that it was performed well, is it does show a correlation. So the study does show a correlation. It does show, if we believe all of their data, that breakfast skipping correlates with obesity and obesity correlates with breakfast skipping. We're seeing it at the same time.

Activity correlates with breakfast and breakfast correlates with activity-- that all of these correlate.

Average RMS PEP And PEAK Power.. AC volts to watts. Pay Attention CB Radio World!.

What they don't say-- and there's no data here that lets me know one way or the other-- what is causing what or maybe you have some underlying cause that is causing both. So for example, they're saying breakfast causes activity, or they're implying breakfast causes activity. They're not saying it explicitly. But maybe activity causes breakfast.

They didn't write the study that people who are active, maybe they're more likely to be hungry in the morning. And then you start having a different takeaway.

Correlation and causality

Then you don't say, wait, maybe if you're active and you skip breakfast-- and I'm not telling you that you should. I have no data one way or the other-- maybe you'll lose even more weight. Maybe it's even a healthier thing to do.

signal related spurious relationship

So they're trying to say, look, if you have breakfast it's going to make you active, which is a very positive outcome. But maybe you can have the positive outcome without breakfast. Likewise they say breakfast skipping, or they're implying breakfast skipping, can cause obesity.

But maybe it's the other way around. Maybe people who have high body fat-- maybe, for whatever reason, they're less likely to get hungry in the morning. So maybe it goes this way. Maybe there's a causality there. Or even more likely, maybe there's some underlying cause that causes both of these things to happen. And you could think of a bunch of different examples of that.

One could be the physical activity. And these are all just theories.

signal related spurious relationship

I have no proof for it. In fact, the non-stationarity may be due to the presence of a unit root in both variables. See also Spurious correlation of ratios. An example of a spurious relationship can be seen by examining a city's ice cream sales. These sales are highest when the rate of drownings in city swimming pools is highest. To allege that ice cream sales cause drowning, or vice versa, would be to imply a spurious relationship between the two. In reality, a heat wave may have caused both. The heat wave is an example of a hidden or unseen variable, also known as a confounding variable.

Another commonly noted example is a series of Dutch statistics showing a positive correlation between the number of storks nesting in a series of springs and the number of human babies born at that time.

Of course there was no causal connection; they were correlated with each other only because they were correlated with the weather nine months before the observations.

Here the spurious correlation in the sample resulted from random selection of a sample that did not reflect the true properties of the underlying population.