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# Minitab 16 - Science topic

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Questions related to Minitab 16
Question
I want to use Box-Behnken design (BBd) For reducing my tests.(4PBT/SCB/ITS/ITSM)
Greetings there,
To be laconic, I think if u can go through it, it might help.
Question
help with calculating average specificity and sensitivity
below is:
Summary of Classification with Cross-validation
True Group
Put into Group 0.0        0.5      1.0
0.0                     12           3        3
0.5                       7          6          5
1.0                       3          2        15
Total N               22        11       23
N correct           12         6         15
Proportion    0.545    0.545    0.652
N = 56 N Correct = 33 Proportion Correct = 0.589
also do you think it is possible to present the result as the total average of sensitivity and specificity for all  groups after calculating individually?  or just represent each class individually in a table?
thank you
is that a confusion matrix? very untidy. Specificity and sensitivity you can calculate the following way:
However you can do both ways i.e. individually and over all the TP's and TN's, FP's and FN's. However, looking at it class wise would make much more sense.
Sensitivity: TP/(TP+FN)
Specifity: TN/(TN+FP)
Question
Design of experiments using Response Surface Methodology.
You can used RSM in three factor, but I will advice you to tanguchi or full factorial b/c RSM you mighty not get accurate result, is more better when you applied it in Four factors five levels or more
Question
I used Minitab to optimise media parameters. i was able to study the interaction between a set of two parameters with the extra parameters at hold value. is it possible to study the interaction between three parameters?
In order to study the interaction between three parameters,  you need to fit your data with cubic regression model. However, standard response surface methodology such as central composite design can not fit cubic regression model. You can study the interaction between three parameters using cubic regression model by using custom design of experiments such as D-optimal design or I-optimality criteria.
Question
Hello every one. Good morning.
I would like to create a regression equation from the available experimental data in the following format.
U=D. A^x. B^y. C^z
But, i am getting the equations in the below format.
U = D + xA + yB + zc.
How to get the equation in “U=D.A^x.B^y.C^z” format using Minitab, Excel or any other software?
You could transform this equation by taking the logarithm of both sides of the equation to convert it to a linear equation and then perform linear regression. After that you can the anti-log.
Here is how your equation will look like after the logarithmic transformation:
Log(M) = x.log(A) + y.log(B) + z.log(C)
Question
The Pareto chart signifies the important factors where a dotted line crosses over. I need to know how does the minitab calculate the number.
Hello. 1- If you are refering to the Pareto chart for the standarized effects in a design of experiment, the reference line is the quantile in the Student's t-distribution or the Lenth’s pseudo-standard error (PSE) . See e.g. http://support.minitab.com/en-us/minitab/17/topic-library/modeling-statistics/doe/factorial-design-plots/what-is-a-pareto-chart-of-effects/
2- If you mean the traditional Pareto chart the dotted line is the cumulative frequency. See the description here (http://support.minitab.com/en-us/minitab/17/topic-library/quality-tools/quality-tools/pareto-chart-basics/).
Regards
Question
I am performing a two-way ANOVA test in order to determine the combined effect of temperature and salinity on the growth of a diatom. In results it shows * is shown for F and p- value.
Does this * mean that there is no significant effect ?
Sorry...I didn't read the whole question. In the case of F statistic or p-value, it means the value could not be calculated. Sometimes there are not enough degrees of freedom, Missing p-values and F-statistics will occur in an ANOVA table whenever you have a 2-level design with one replicate, and you include all the terms in your model.
i hope that helps.
Question
I've got a factorial design including two factors with unbalanced observations. I was wondering how I can do analysis of variance to test null hypothesis for them in Minitab 16?
Hi
Its very simple to do ANOVA analysis in Minitab
Open Minitab, go to "Stat" then to "ANOVA"and to "General regression"
Chose the response and the model type then you will have ANOVAanalysis and
Question
H0 = the number of production outputs during 3360
minutes, Y, is equal to 3247 (Y=3247)
H1 = the number of production outputs during 3360
minutes, Y, is not equal to 3247 (Y≠3247):
Hi KS,
Thanks for the article, let's look at some facts here. The initial production line is unbalanced, the longest cycle time is 47 seconds and the average output calculated is 62 seconds. This is due to the asynchronous process creating idle time and waste. Would be better to have station close to each other with no WIP in between and running at about 50 seconds automated takt, then grouping station 1 and 2 then 7 and 8.
But that was not your question. Normal distribution supposes that your variable can vary freely from minus infinite to plus infinite, and time data rarely meet this requirement, at least in real world. I have almost never seen real operator time data normally distributed as the probability to expand in plus direction is large and usually there is a hard limit in minus direction. Then why having time from operators with 3 digits? Of course it helps finding it normally distributed but time with operators recorded with 1 second resolution is enough, then by analyzing the distribution with 1 second classes we can see the modal value which is the most frequent one with the higher probability of occurrence, more than the average value.
I do not see in the article some foundation for the Mann-Whitney test. They talk about average and then do a test about median. With the 6 run's average a simple IMR chart shows that anywhere between 3243 and 3252 is the same thing.
Now if I create a simple calculation method, the simulation, that calculates 3247 with moderate variance, and I do a test to see if I find 3247 ???? What is the point? Do I demonstrate that the calculation repeat itself or that the simulation works?
Simulation is nothing more than denying reality of variance of some factors, when it is not ignoring them totally. Looks like the purpose here is to demonstrate that the software is good and we must buy it, I will not.
Best regards, Luc
Question
A pin on disc experiment has been carried out acc to taguchi does. the response(outputs) are coefficient of friction , frictional force. In taguchi analysis  , whether this experiment would be a static or dynamic experiment? If static then which optimization S/N ratio is to be considered:
a. smaller the better
b.larger the better
c.nominal the best
Definitions:
STATIC PROBLEMS :
Generally, a process to be optimized has several control factors which directly decide the target or desired value of the output. The optimization then involves determining the best control factor levels so that the output is at the the target value. Such a problem is called as a "STATIC PROBLEM".
DYNAMIC PROBLEMS :
If the product to be optimized has a signal input that directly decides the output, the optimization involves determining the best control factor levels so that the "input signal / output" ratio is closest to the desired relationship. Such a problem is called as a "DYNAMIC PROBLEM".
its a static problem, and you may use smaller is better as we desire a lower coefficient of friction and  frictional force
Question
how to Perform least significant difference or critical difference in minitab. Plz explain
So, I assume you are doing one way ANOVA:
Try this path:
Stat / ANOVA / One way ANOVA: put the codes of your treatments in 'factor" box and the response in "response"; click "comparison button", write the error rate: 5 for 95 and 1 for 99 percent confidence.
click ok twice and in results you have:
Treatment   N    Mean      StDev     95% CI
1                    3    5.000     1.000     (2.742, 7.258)
2                    3    7.33       2.08        ( 5.07, 9.59)
3                    3    7.667     1.528      (5.408, 9.925)
the data in 95% CI column are critical values.
Regards,
Question
I am doing structural optimization using a response surface method. My objective function is to reduce weight using the LS-OPT tool. I have done a total of 16 experiments. For each experiment, it displayed a computed value and a predicted value for weight. Which one should I choose as a optimum final answer? The compound or the predicted?
Vinod -
I am not clear on your context here, but in regression, the points on the regression line or curve are the predicted values, say y*, and the observed values are y. I factor the residuals into a random factor, and a nonrandom factor based on the regression weight, the product of which we will call e, such that we have yi = y*+ ei .
So the answer to your question depends upon your goal. If you are looking for an observed value, you take that which was observed. But perhaps you are more interested in the model?
I do not know if what you refer to by 'experiment' is a single observation, or a set of observations from which you estimate/'calculate' one aggregate observation, and whether the predictions are from one model or different ones, the details of which might be important, but it would seem that regardless, the answer to your question depends upon whether you want to know what was observed/calculated, or modeled. If you are looking to future use, the models may be more important. Regardless, you should also consider estimating the variance of the prediction error.
Cheers - Jim
Question
Could you please explain to me how I can do the MANCOVA using Minitab.
I have 3 categorical data in each one there is 4 numerical levels and these levels are fixed for all 3 categorical data and there is 4 responses should be evaluated in all experiments.
Why are you using MANCOVA? What is your covariate?
If you go to Stat>ANOVA>General Manova in Minitab, you can add your factors of interest in the "Model" box, your responses in the "Responses" box. There is also an option for adding covariates.
Question
I am doing a gage R&R to assess a new measurement system. I need to enter a historical standard deviation value to get % process in Minitab. Where do I get this value from?
Note: The parts are of different sizes and each has its own mean and standard deviation.
Dear Sri Abu
Great Question. Historical std. dev values are based on the info about a process in a particular environment... For example in an industry that mastered the art of drilling, their std dev is from the past data... however it may be different in a process of milling within the Industry... However, in the absence of historical info, it is Industry practise to assume 3 standard deviation on the tolerance of the spec.
Hope it helps... if you have specific question on R&R, pl do feel free to post it to me thro my home page.
Kindest Regards
Ramanan
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