A common misconception in statistics is confusing correlation with causation. If two events are correlated, it merely means that they share the same behaviour over time, but it doesn't imply in any way that those event are related by a common cause, or even worse, that one implies the other.
You can find several (even humorous) counter examples online. For example, if you plot the number of reported pirates assault against the global temperature in the last years, you'll se that temperature is rising (unfortunately...) while pirates are almost disappearing.
One could observe this strong negative correlation and claim that hotter climate has solved the pirate issue. Of course this is a joke, but it explains why you shouldn't confuse correlation with causation.
Answer:
Step-by-step explanation:
(2x^2-11)(x^2+2)
2x^4-7x^2-22
Cost of lack increased by 30%
new cost of lack will be = 100% + 30% = 130%
We are given that new cost = £65
Thus 130% of original cost = 65
100% of original cost = 65 × 100/130 = £50
Thus original price was £50
Answer:
$1350
Step-by-step explanation:
5% of 1000 is 50
50x7 is 350
1000+350=1350
Answer:
Step-by-step explanation:
A suitable table or calculator is needed.
One standard deviation from the mean includes 68.27% of the total, so the number of bottles in the range 20 ± 0.16 ounces will be ...
0.6827·26,000 = 17,750 . . . . . within 20 ± 0.16
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The number below 1.5 standard deviations below the mean is about 6.68%, so for the given sample size is expected to be ...
0.66799·26,000 = 1737 . . . . . below 19.76
_____
<em>Comment on the first number</em>
The "empirical rule" tells you that 68% of the population is within 1 standard deviation (0.16 ounces) of the mean. When the number involved is expected to be expressed to 5 significant digits, your probability value needs better accuracy than that. To 6 digits, the value is 0.682689, which gives the same "rounded to the nearest integer" value as the one shown above.
