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Data in Everyday Life: Gas Prices

Up, down, or just an excuse for sensational headlines?

Beth Savage
Wed, 03/16/2016 - 15:39
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Out-of-control gas prices reported in the news have our attention. Nearly every media outlet, from the small-town daily news to The Wall Street Journal, has a gas price story on a weekly basis: “Gas Prices Are Plunging,” and “How Low Will Gas Prices Go?” It’s news when they rise and news when they fall as we try to time fill-ups when prices are lowest.

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Although the gas prices are bouncing like a Super Ball, I wondered if the prices are statistically different, or if the variation we’re experiencing is actually normal? To answer this question, I look to the control chart—the best tool for analyzing data to determine if a process is experiencing the normal variation that is inherent in every process, or if special-cause variation is present.

Looking at the average weekly gas prices per quarter since 2005 reveals a process that is highly unstable. In fact, at least four types of out-of-control conditions exist:
1. Points above the upper control limit
2. Point below the lower control limit
3. Runs below the mean
4. A run above the mean

 …

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Comments

Submitted by MathGrl on Wed, 03/16/2016 - 09:30

Range chart

Although we don't see out-of-control points on the yearly average chart, the range chart for the yearly averages will contain out-of-control points. Also, the control limits on the Xbar chart are created using the mean range from the R chart. The UCL and LCL on the Xbar chart are inflated because of this. It's not surprising that the yearly average gas prices are in-control with respect to Rule 1 given an LCL of ~$1.70 and UCL of ~$4.20.

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Submitted by BA Savage on Thu, 03/17/2016 - 12:16

In reply to Range chart by MathGrl

Range charts

Great point about the range charts. I agree that pairing variability charts with charts for averages is useful. In my zeal to get the reader to focus on investigating data over time in ways that are meaningful to him or her, I ignored the variability chart.

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Submitted by dvanputten on Fri, 03/18/2016 - 06:32

Macro Open System

This data comes from a macro open system with a tremedous list of direct and indirect sources of variation. Stakeholders are trying to cause variation. What gives me a grin is that in the industrialized world we pay attention to gasoline price fluctations. We know what the price per unit volume is today. We have apps that tell us where the cheapest gas is. However, there are other bulk mateirals that cost way more per unit volume and we dont even try to know the variation. Why arent coffee prices advertised on signs? Why dont we know the daily price change in coffee (or milk or ....)? Is it the volume of consumption per unit of time and therfore the overall personal ecomomic impact? 

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Submitted by kkbari on Fri, 03/18/2016 - 09:12

Heavily Autocorrelated - not unstable

Just because we can crank numbers into a formula or software doesn't mean we should.  These observations are not independent by any stretch of the imagination.  Applying statistical limits to a process that by its very nature is heavily autocorrelated will lead to bad conclusions.  Wrong tools for this data.  We could change our sample frequency such that the autocorrelation disappears, but by that time there are very few data points to estimate limits properly.  Although I get that the article was to both teach SPC and promote PQ products, better data should have been chosen for that effort.

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Submitted by BA Savage on Fri, 03/18/2016 - 10:21

In reply to Heavily Autocorrelated - not unstable by kkbari

Autocorrelated

Thanks for your comments. You make some good observations. I agree that just because we can easily display a control chart for data, it doesn’t mean we should. And yes, this data is heavily autocorrelated. But the data used in the article is data that is familiar to most everyone, which I thought would be useful to illustrate 1) how data can be viewed differently and 2) the importance of investigating data over time in ways that are meaningful.

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