Science topic

# Minitab 16 - Science topic

Explore the latest questions and answers in Minitab 16, and find Minitab 16 experts.

Questions related to Minitab 16

**I want to use Box-Behnken design (BBd) For reducing my tests.(4PBT/SCB/ITS/ITSM)**

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

Design of experiments using Response Surface Methodology.

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?

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?

The Pareto chart signifies the important factors where a dotted line crosses over. I need to know how does the minitab calculate the number.

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 ?

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?

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):

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".

how to Perform least significant difference or critical difference in minitab. Plz explain

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?

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.

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.

I have done a full factorial experiment (using 3 factors) and three center points. The experiment was repeated thrice at main points, overall producing 27 design points.

Now analyzing the data, I am not able to get the desired model. I analyzed the design using a first-order model with all interactions, but R square and r square adjusted was found to be very less.

After various iterations I finally ended up with a model having only main effects. However, the curvature effect was seen to be quite significant.

Subsequently when I tried to analyze the design using RSM, the model worsened and I had to revert back to the original model.

I am getting Rsquare and Re-sq. adjusted as 79 and 75% respectively. I feel it's too low to accept the model.

Can someone tell me whether curvature effects are taken into account while building a first-order model? I mean, is y=a+a*x1+b*x2+c*x3 sufficient even though centerpoint/curvature shows a significant effect?

If it's not included, can someone please explain how to improve the model or include the effect of curvature.