Generating Standard Curve to analyse the reaction optimization  Real Time qPCR
A standard curve can be generated using a 10fold dilution of a template amplified on a realtime system (Example: ABI 7500). Each dilution can assayed in triplicate(its always better to do in duplicates or in triplicates).
Below are the data generated after a Real time PCR run.
The template used for this purpose can be a target with known concentration (e.g., nanograms of genomic DNA or copies of plasmid DNA) or a sample of unknown quantity (e.g., cDNA). Here plasmid is used as the template for the reaction.
Below is the standard curve graph generated using the data obtained after the PCR run, here Ct value is plotted against log DNA dilution.
If perfect doubling occurs with each amplification cycle, the spacing of the fluorescence curves will be determined by the equation 2^n =dilution factor, where n is the number of cycles between curves at the fluorescence threshold (in other words, the difference between the Ct values of the curves).
for example, with a 10fold serial dilution of DNA, 2^n = 10. Therefore, n = 3.32, and the Ct values should be separated by 3.32 cycles.
A standard curve can be generated using a 10fold dilution of a template amplified on a realtime system (Example: ABI 7500). Each dilution can assayed in triplicate(its always better to do in duplicates or in triplicates).
Below are the data generated after a Real time PCR run.
Ct

Log
DNA dilution

36.47

10^6

33.83

10^5

29.49

10^4

26.85

10^3

23.11

10^2

20.14

10^1

17.16

10^0

The template used for this purpose can be a target with known concentration (e.g., nanograms of genomic DNA or copies of plasmid DNA) or a sample of unknown quantity (e.g., cDNA). Here plasmid is used as the template for the reaction.
The standard curve is constructed by plotting the log of the starting quantity of template (or the dilution factor, for unknown quantities) against the Ct (Cycle Threshold) value obtained during amplification of each dilution.
Below is the standard curve graph generated using the data obtained after the PCR run, here Ct value is plotted against log DNA dilution.
Standard Curve qPCR 
The equation of the linear regression line, along with Pearson’s correlation coefficient (r) or the coefficient of determination (R2), can then be used to evaluate whether your qPCR assay is optimized.
Realtime quantification (qPCR) is based on the relationship between initial template amount and the Ct value obtained during amplification, an optimal qPCR assay is absolutely essential for accurate and reproducible quantification of the sample.
The hallmarks of an optimized qPCR assay are:
• Linear standard curve (R^2 > 0.980 or r > –0.990)
• High amplification efficiency (90–105%)
• Consistency across replicate reactions.
One should strive to achieve a PCR efficiency above 90%.
qPCR Calculations
As everyone knows in PCR one copy of the template becomes two after the first cycle. the template copy number keeps accumulating as the no of cycles proceeds. it is given by a general formula:
2^n; after 25 cycles one copy of the template becomes 2^25 = 33554432 copies or 3.35*10^7 copies.
If perfect doubling occurs with each amplification cycle, the spacing of the fluorescence curves will be determined by the equation 2^n =dilution factor, where n is the number of cycles between curves at the fluorescence threshold (in other words, the difference between the Ct values of the curves).
for example, with a 10fold serial dilution of DNA, 2^n = 10. Therefore, n = 3.32, and the Ct values should be separated by 3.32 cycles.
if the PCR assay has 100% efficiency one copy becomes two after one cycle, so how to calculate PCR Efficiency.
Amplification efficiency denoted by E can be calculated from the below equation:
E = 10^(1/slope);
From the standard Curve chart (y = mx + b)
where m is the slope;
b is the intercept;
slope (m) =  3.2746
Amplification efficiency E = 10^(1/3.2746) = 2.02
Amplification efficiency is also frequently presented as a percentage, that is, the percent of template that was amplified in each cycle.
To convert E into a percentage:
% Efficiency = (E – 1) x 100%
% Efficiency = (2.0 – 1) x 100% = 100%.
An efficiency close to 100% is the best indicator of a robust, reproducible assay. Low reaction efficiencies < 90% may be caused by poor primer design or by suboptimal reaction conditions. Reaction efficiencies >100% may indicate pipetting error in your serial dilutions or coamplification of nonspecific products, such as primerdimers.
When using the method described above to determine amplification efficiency, the presence of inhibitor can also result in an apparent increase in efficiency. This is because samples with the highest concentration of template also have the highest level of inhibitors, which cause a delayed Ct, whereas samples with lower template concentrations have lower levels of inhibitors, so the Ct is minimally delayed. As a result, the absolute value of the slope decreases and the calculated efficiency appears to increase. If the reaction efficiency is <90 nbsp="" or="">105%, one should modify the assay by redesigning your primers and probes.
Reference
PCR Application guide BioRad
Mathematics in molecular biology and Biotechnology
Internet Sources
Amplification efficiency denoted by E can be calculated from the below equation:
E = 10^(1/slope);
From the standard Curve chart (y = mx + b)
where m is the slope;
b is the intercept;
slope (m) =  3.2746
Amplification efficiency E = 10^(1/3.2746) = 2.02
Amplification efficiency is also frequently presented as a percentage, that is, the percent of template that was amplified in each cycle.
To convert E into a percentage:
% Efficiency = (E – 1) x 100%
% Efficiency = (2.0 – 1) x 100% = 100%.
An efficiency close to 100% is the best indicator of a robust, reproducible assay. Low reaction efficiencies < 90% may be caused by poor primer design or by suboptimal reaction conditions. Reaction efficiencies >100% may indicate pipetting error in your serial dilutions or coamplification of nonspecific products, such as primerdimers.
When using the method described above to determine amplification efficiency, the presence of inhibitor can also result in an apparent increase in efficiency. This is because samples with the highest concentration of template also have the highest level of inhibitors, which cause a delayed Ct, whereas samples with lower template concentrations have lower levels of inhibitors, so the Ct is minimally delayed. As a result, the absolute value of the slope decreases and the calculated efficiency appears to increase. If the reaction efficiency is <90 nbsp="" or="">105%, one should modify the assay by redesigning your primers and probes.
Reference
PCR Application guide BioRad
Mathematics in molecular biology and Biotechnology
Internet Sources