When investing in a stock, there are only two numbers that ultimately matter: the price the stock was bought at, and the price its sold at. If I buy 10,000 shares of stock at $1, and next year I sell it at $2, I have $10000 more than I started with. What happens to the stock in the year I held it isn’t important. The stock price could soar to $1000 per share, and then sharply decline. On the other hand, it could be flat at $1 for 360 days, and then jump to $2, and you would earn the same amount of money.

When I talk about linear trends, I do not just look at the endpoints. Linear regression looks at all the data points, takes a line through those points, and minimized the squared error. From my meanderings on the Internets the last few weeks, it would seem that some people do not understand the difference between a linear trend, and connecting the end points. I think an example would be great here.
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The price of milk had been dropping. To make matters worse, Farmer John’s cows weren’t producing as much milk. He was becoming desperate to keep his farm afloat. Last year, he invested in a new as-seen-on-TV feed that was supposed to make the cows happier and thus produce more milk. It didn’t work. A year before that, he tried Beethoven for Bovines - no luck with that either. His attempts at solving the problem experimentally did not work, so he decided to see if a theoretician could help. His local university veterinary department did not have any bovine theoretician on staff. As a joke, they suggested the physics department. Farmer John, not in on the joke, asked Professor Honcho why his cows weren’t producing milk. Honcho replied that this should be an easy problem; not more than a day or two. Three weeks later, Honcho called the farmer and said, “I found your answer. It was more difficult that I first thought, but the result is interesting, and I’ll be presenting it at the department seminar this week.” As the talk begins, he goes to the blackboard and says, “First, assume a spherical cow…”
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I go out of town to visit relatives with no Internet access, and the proverbial fit hits the shan. That’s perhaps over-stating the magnitude of the situation, but Anthony Watts has a new post that is extremely misleading and it would have been nice if I could have thrown this post together sooner. He posts at Watts Up: 3 of 4 global metrics show nearly flat temperature anomaly in the last decade. The one that doesn’t show “nearly flat temperatures” is GISS, which seems to cause undue concern.

He concludes by saying

Given some of the recent issues Steve McIntyre has brought up with missing data at NASA GISS, it also makes me wonder if the GISS dataset is as globally representative as the other three.

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The issue of accuracy versus precision is interesting to me. In science, there is a distinct difference between the two words. Yet to everyone else, the two are synonymous. Therefore, I was slightly amused by this exchange on Anthony Watts’ blog. A commenter wrote the first part, and Anthony replied.

In an earlier entry, you said this MMTS unit is accurate to plus or minus 5 degrees. How does this compare to the MMTS units NOAA uses now? And how accurate should the unit be to replace current models?

REPLY: “you said this MMTS unit is accurate to plus or minus 5 degrees”

I never said any such thing. That level of accuracy would be useless.

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