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Statistical Thinking for Industrial Problem Solving

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  • Evaluating Model Assumptions

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the Cleaning data to fit a regression model for Removal and ID using Fit Y by X, and then conduct a residual analysis to evaluate model assumptions.   We select Fit Y by X from the Analyze menu. We'll use Removal as Y, Response, ID as X, Factor, and click OK.   Then...

    julian julian
    2340 views | 0 replies

  • Interpreting Regression Analysis Results

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we again use the Cleaning data to fit a regression model for Removal and ID using Fit Y by X. We'll discuss the statistical output provided, and will see how to make predictions using our regression equation.    We'll again use Removal as the Y, Response and ID as the X, F...

    julian julian
    5585 views | 0 replies

  • Fitting Polynomial Models

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we explore the FreeFall data using the Graph Builder, and see how to fit polynomial models using Fit Y by X.   We'll start by opening the Graph Builder from the Graph menu.   Recall that we are measuring Distance as a function of Time. We'll drag Distance to the Y zone and...

    julian julian
    7798 views | 0 replies

  • Fitting Multiple Linear Regression Models

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this example, we use the Cleaning data and fit a multiple linear regression model for Removal with three predictors, OD, ID, and Weight.   Recall that when we fit a linear model with one response and one predictor, we use either the Graph Builder or the Fit Y by X platform. Graph Buil...

    julian julian
    8371 views | 0 replies

  • Using the Prediction Profiler

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the Impurity example and fit a model for the response, Impurity, with three predictors, Temp, Catalyst Conc, and Reaction Time. Then we use the Prediction Profiler to better understand the model coefficients.   Let’s begin by selecting Fit Model from the Analyze men...

    julian julian
    10641 views | 1 replies

  • Analyzing Residuals and Outliers

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this example, we continue where we left off in the previous JMP demo. Recall that we fit a model for Impurity, with three predictors, Temp, Catalyst Conc, and Reaction Time, using Analyze, Fit Model.   Impurity is the Y variable, and Temp, Catalyst Conc, and Reaction Time are the mode...

    julian julian
    17200 views | 0 replies

  • Fitting a Model with Categorical Predictors

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this example, we again fit a model for Impurity using Fit Model. But this time, we also add the categorical predictors to the model.   First, we add Impurity as the Y variable.   Adding the categorical predictors to the model is no different than adding continuous predictors. We simpl...

    julian julian
    3281 views | 0 replies

  • Fitting a Model with Interactions

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this example, we again fit a model for Impurity using Fit Model. But this time we add interactions to the model.   Again, we start by adding Impurity as the Y variable.   Then we add the five main effects, our predictors, to the model.   To add a specific interaction term to the model...

    julian julian
    10854 views | 0 replies

  • Selecting Variables Using Effect Summary

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we see how to use the Effect Summary table for variable selection using the Impurity data.   We’ll start by fitting a full model, with interactions, using Fit Model.   First, we’ll select Impurity as the Y.   Then, we’ll select Temp through Shift, and select Macros, and th...

    julian julian
    3487 views | 0 replies

  • Assessing Multicollinearity

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the Bodyfat data to explore multicollinearity in JMP using all the potential predictors.   Recall that, in this scenario, we are interested in predicting %Fat as a function of several physical measurements.

    We’ll start by exploring the data.   We’ll use the Multivar...

    julian julian
    5677 views | 0 replies

  • Fitting a Multiple Logistic Regression Model

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the Impurity Logistic example and fit a model for the response, Outcome, with the five main effects, Temp through Shift. We reduce the model and then use the Prediction Profiler to better understand the significant model coefficients.   Let's begin by selecting Fit ...

    julian julian
    3701 views | 0 replies

  • Fitting a Logistic Regression Model with Interactions

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the MetalCoating example and fit a model for the response, Outcome. For this demonstration, we include only the continuous predictors and their two-way interactions as model effects.   We begin by selecting Fit Model on the Analyze menu.   We select Outcome as the Y...

    julian julian
    2672 views | 0 replies

  • Designing Full Factorial Experiments

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we show how to design full factorial experiments using the Full Factorial platform in JMP.   To do this, we select DOE, then Classical, and then Full Factorial Design.   In the Responses panel, we can change the response name and the response goal, and we can add responses...

    julian julian
    8812 views | 0 replies

  • Analyzing Full Factorial Experiments

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we show you how to analyze full factorial experiments in JMP using the file 2x2x2 Unreplicated.jmp.   As the name implies, this is an unreplicated 23 full factorial experiment. The factors are Temperature, Time, and Catalyst, and the response is Yield.   Because this exper...

    julian julian
    5059 views | 0 replies

  • Creating 2k-r Fractional Factorial Designs

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, you learn how to create 2k-r fractional factorial designs in JMP. We’ll create a fractional design to study five 2-level continuous factors.   To create a fractional factorial design, we select DOE, then Classical, and then Screening Design.   We’ll use Y as the response n...

    julian julian
    4365 views | 0 replies

  • Creating Screening Designs in the Custom Designer

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we show how to generate a screening design using the Custom Designer in JMP. We’ll create an optimal screening design for the Heck reaction scenario, with five factors.   First, we select Custom Design from the DOE menu.   We change the response to Yield.   There are five ...

    julian julian
    1523 views | 0 replies

  • Designing a Central Composite Design

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we show you two ways to create central composite designs in JMP. First, we use the classical Response Surface Design platform, and then we create the same design using the Custom Designer.   To start, we select DOE, then Classical, and then Response Surface Design.   In th...

    julian julian
    8013 views | 0 replies

  • Optimizing Multiple Responses

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we show how to analyze an experiment with multiple responses, and how to optimize multiple responses, using the file Anodize.jmp.   In this example, a 12-run custom design with five factors was conducted. The experimental objective is to find settings of the factors to opt...

    julian julian
    5452 views | 1 replies

  • Simulating Data Using the Prediction Profiler

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we show how to do a Monte Carlo simulation in JMP using the Prediction Profiler.   We use the file Anodize.jmp. This file contains the results of a 12-run custom design with five factors and four continuous responses. The experimental objective was to find settings of the ...

    julian julian
    5023 views | 0 replies

  • Creating a Validation Column (JMP Pro)

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we show how to create a validation column in JMP Pro using the Make Validation Column utility.   We'll use the Impurity example and create a new column, Validation. We'll randomly assign 60% of the observations to the training set and the remaining 40% to the validation se...

    julian julian
    8868 views | 0 replies

  • Fitting a Multiple Linear Regression Model with Validation

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this example, we fit a predictive model for Impurity using Fit Model with all main effects and two-way interaction terms.   The data have been partitioned into training and validation data. 60% of the observations have been randomly assigned to the training set, and 40% of the observa...

    julian julian
    6202 views | 0 replies

  • Fitting a Logistic Model with Validation

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the Impurity example and fit a model for the categorical response, Outcome, with the three continuous main effects, Temp, Catalyst Conc, and Reaction Time.   The data have been partitioned into training and validation data. 60% of the observations have been randomly...

    julian julian
    3646 views | 0 replies

  • Changing the Cutoff for Classification

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos) When you fit a classification model in JMP, the probability cutoff for classification is 0.50. In this video, we see how to change the cutoff for classification using a formula column in the data table.   To apply a different cutoff, you can save the probability formula to the data table a...

    julian julian
    4387 views | 0 replies

  • Creating a Classification Tree

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the Chemical Manufacturing example and fit a classification tree for the categorical response, Performance.   To do this, we select Predictive Modeling from the Analyze menu, and then select Partition.   We select Performance as the Y, Response variable.   Then we s...

    julian julian
    4746 views | 0 replies

  • Fitting a Regression Tree

    Learn more in our free online course:
    Statistical Thinking for Industrial Problem Solving

    (view in My Videos)   In this video, we use the Chemical Manufacturing example and fit a regression tree for the continuous response, Yield.   To do this, we select Predictive Modeling from the Analyze menu, and then Partition.   We select Yield as the Y, Response variable. Then we select the two groups of pr...

    julian julian
    2321 views | 0 replies