1. Statistical Data Analysis
Prof. Terry A. Ring, Ph. D.
Dept. Chemical & Fuels Engineering
University of Utah

2. Making Measurements Choice of Measurement Equipment
Accuracy – systematic error associated with measurement.
Precision – random error associated with measurement.

3. Definitions
Error – the difference between the measured quantity and the ”true value.”
The “true value” is not known!!!
So how do you calculate the error??
Random errors - the disagreement between the measurements when the experiment is repeated
Is repeating the measurement on the same sample a new experiment?
Systematic errors - constant errors which are the same for all measurements.
Bogus Data – mistake reading the instrument

4. Random Error Sources Judgement errors, estimate errors, parallax
Fluctuating Conditions
Digitization
Disturbances such as mechanical vibrations or static electricty caused by solar activity
Systematic Error Sources
Calibration of instrument
Environmental conditions different from calibration
Technique – not at equilibrium or at steady state.

5. Statistics Mean xM Deviation xi-xM Standard Deviation 
Confidence level or uncertainty,
95% confidence = 1.96 
99% confidence = 2.58 
Please note that Gaussian distributions do not rigorously apply to particles- log-normal is better.
Mean and standard deviation have different definitions for non-Gaussian Distributions

6. Comparison of means – Student’s t-test
v= n1+n2-2
use the t-value to calculate the probability, P, that the two means are the same.

7. Estimating Uncertainties or Estimating Errors in Calculated Quantities –with Partial Derivatives
G=f(y1,y2,y3,…)

10. Way around the Partial Derivatives
This approach applies no matter how large the uncertainties (Lyons, 1991).
(i) Set all xi equal to their measured values and calculate f. Call this fo.
(ii) Find the n values of f defined by
fi = f(x1,x2,...,xi+si,...,xn) (11)
(iii) Obtain sf from
(12)
If the uncertainties are small this should give the same result as (10). If the uncertainties are large, this numerical approach will provide a more realistic estimate of the uncertainty in f. The numerical approach may also be used to estimate the upper and lower values for the uncertainty in f because the fi in (11) can also be calculated with xi+ replaced by xi-.

11. Table 4. Uncertainties in Gas Velocity Calculated from (15) and (17)
Now try the same calculation using the spread sheet method. The dimensionless form of (12) is (after taking the square root)
(17)
The propagated fractional uncertainties using (15) and (17) are compared in Table 4.
A further advantage of the numerical approach is that it can be used with simulations. In other words, the function f in (12) could be a complex mathematical model of a distillation column and f might be the mole fraction or flow rate of the light component in the distillate.
Table 4. Uncertainties in Gas Velocity Calculated from (15) and (17)
Equation Used
f/f0
(9)
(12) with +
(12) with -
See web page with sample calculation done with Excel

12. Fitting Data Linear Equation – linear regression Non-linear Equation
Linearize the equation- linear regression
Non-linear least squares

13. Rejection of Data Points
Maximum Acceptable Deviations (Chauvenet’s Criterion)

14. Example
xi-xM/= /0.3=2.33
xi-xM/= /0.3=1.0

15. Regression Linear Regression Linearize non-linear equation first
good for linear equations only
Linearize non-linear equation first
linearization leads to errors
Non-linear Regression
most accurate for non-linear equations
See Mathcad example

16. Residence Time Measurements
Time(min)
Time(min)
T=(To-Tin)exp(-t/tau)+Tin

17. Non-Linear Fit
Linearized Eq. Fit
Flow Calc.s
Temperature(C)
Time (min)

18. Results