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Topic Establish Statistical Correlation Between Weather and Quality Defects?

Establish Statistical Correlation Between Weather and Quality Defects?

Home Forums General Forums Tools & Templates Establish Statistical Correlation Between Weather and Quality Defects?

This topic contains 2 replies, has 3 voices, and was last updated by  Chris Seider 3 months, 1 week ago.

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  • #705777 Reply

    I want to see if there is a statistical correlation between weather and certain defects at my manufacturing plant using Minitab. These defects relate to the painting process of the product which is sensitive to sudden temp and humidity conditions I’ve got two datasets:

    First one containing the sample of units produced that day and quantity of defective items.
    Second one containing weather conditions (min, max and avg temp, min-max and avg humidity)

    What would be the beset way to find out a statistical correlation? Im confused if I should use Pearson or if a regression. Here’s a screenshot of what my raw data looks like raw data

    I’m a total noob to minitab so this is probably very basic but still couldn’t get around to it. I appreciate any help I can get. Thanks!

    #705783 Reply

    For your first attempt I would recommend plotting your defect count data against all of the temperature variables of interest and see what you get. If all you have for plots are a series of shotgun blasts then you will know without wasting a lot of time that there isn’t any clear relationship between the variables of interest and the defect count. On the other hand if you do get some clear trending you can go about running a regression to model what you see. Based on my experience there is a good chance that if you find a graphical relationship it will not be a simple straight line – hence the recommendation of plotting before trying anything else.

    #705784 Reply

    Check out the Matrix Plot–then do plots on those with interest to you.

    Matrix plot is a great screening tool in the graphical analysis approach that’s very underutilized nowadays.

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