Linear regression doubling time

In summary, the conversation discusses the equation y=mx+c and its application to doubling time calculations. The equation is used to determine the relationship between t (time) and log y (bits per second). The doubling time is found when 13/70 * t is equal to log2. The conversation also mentions the use of semilog graphs and the difference between semilog and log-log graphs.
  • #1
yecko
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Homework Statement
see image
Relevant Equations
y=mx+c
1619617808719.png

nielsen-law-bandwidth-growth-1983-2019.png

y=mx+c
by z=log y axis
m=9-2.5 / 35 = 13/70
z=13/70 * (t-1983) + 2.5
log y = 13/70 * (t-1983) + 2.5 #

doubling time: t1=y1, t2=y2=2y1
log y1 = 13/70 * (t1-1983) + 2.5 ---{1}
log (2*y1) = 13/70 * (t2-1983) + 2.5 ---{2}
{2}-{1}: log2=13/70 * [(t2)-(t1)]
[(t2)-(t1)] = log2/(13/70) #

for log scale, can I log only y axis? for the constant c, is it 2.5 or 10^2.5?
is the doubling time calculated by simultaneous equation like my attempt?

Thank you.
 
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  • #2
yecko said:
y=mx+c
by z=log y axis
m=9-2.5 / 35 = 13/70
z=13/70 * (t-1983) + 2.5
log y = 13/70 * (t-1983) + 2.5 #
You are leaving us guessing what you did. A straight line would be ##\ \log_{10} y = m x + c \ ## or ## y = 10^{c} \; \left (10^{x} \right )^m ##

##m=9-2.5 / 35 \ne 13/70 \ \ ## but ## \ \ m= (9-2.5) / 35 = 13/70 ##

And yes, in 70 years ##\log_{10} ## increases by 13 (actually: a little less !), so y becomes a factor 1013 bigger.

Doubling time is when ##\ \ 13/70 * t = \log_{10} 2 \ \ ##. I wouldn't call that solving simultaneous equations...

yecko said:
for log scale, can I log only y axis?
What do you mean ? The verb logging means something else 13
 
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  • #3
yecko said:
for log scale, can I log only y axis?
A graph can be semilog, such as log y vs x, or log-log, such as log y vs log x. The graph you showed is semilog, with t on the horizontal axis, and log(bits per sec) on the vertical axis.

Is that what you're asking?
 
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FAQ: Linear regression doubling time

What is linear regression doubling time?

Linear regression doubling time is a mathematical concept used to estimate the amount of time it takes for a variable to double in value. It is commonly used in scientific research and data analysis to understand the growth or decline of a particular phenomenon.

How is linear regression doubling time calculated?

The calculation for linear regression doubling time involves using a regression line to fit a straight line to a set of data points. The slope of the regression line is then used to estimate the doubling time by dividing the natural logarithm of 2 by the slope.

What does the doubling time value indicate?

The doubling time value indicates the rate at which a variable is increasing or decreasing over time. A shorter doubling time indicates a faster rate of growth, while a longer doubling time indicates a slower rate of growth. It can also be used to predict future values of the variable.

Can linear regression doubling time be negative?

No, linear regression doubling time cannot be negative. It is a measure of time and therefore cannot have a negative value. If the regression line has a negative slope, it means that the variable is decreasing over time, but the doubling time will still be a positive value.

What are the limitations of using linear regression doubling time?

Linear regression doubling time is based on assumptions about the data, such as a constant rate of change, which may not always hold true. It also assumes that the data follows a linear trend, which may not be the case in all situations. Additionally, it is important to consider the context and potential biases in the data before making conclusions based on the doubling time value.

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