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• Calculating the Sample Size for a Confidence Interval

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  Statistical Thinking for Industrial Problem Solving
  
  (view in My Videos)   In this video, you learn how to use the Sample Size for Confidence Intervals calculator to compute the sample size required to create a confidence interval with a specified margin of error.   This calculator is available from the Help menu in JMP, under Sample Data and then Calculators. ...

  jules

  Learning Library |

  Dec 3, 2021 1:03 PM

  14683 views | 0 replies

• Exploring the Power Animation

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  Statistical Thinking for Industrial Problem Solving
  
  (view in My Videos)   In this video, we use the Power animation to explore the relationship between sample size and power for a one-sample t test using the file Diameter Test.jmp.   First, we conduct a one-sample t test for Diameter. To do this, we use the Distribution platform from the Analyze menu. We selec...

  jules

  Learning Library |

  Dec 3, 2021 1:03 PM

  1612 views | 0 replies

• Calculating the Sample Size for a One-Sample t Test

  Learn more in our free online course:
  Statistical Thinking for Industrial Problem Solving
  
  (view in My Videos)   In this video, you learn how to calculate the sample size for a one-sample t test. To do this, we select Sample Size and Power from the DOE menu under Design Diagnostics.   Sample size and power calculations are available for many situations. We’ll select One Sample Mean.   In this scena...

  jules

  Learning Library |

  Dec 3, 2021 1:03 PM

  3699 views | 0 replies

• Calculating the Sample Size for Two or More Sample Means

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  Statistical Thinking for Industrial Problem Solving
  
  (view in My Videos)   In this video, you learn how to calculate the sample size for a two-sample t test and ANOVA. To do this, we select Sample Size and Power from the DOE menu under Design Diagnostics.   To calculate the sample size for a two-sample t test, we select Two Sample Means.   In this scenario, the...

  jules

  Learning Library |

  Dec 3, 2021 1:03 PM

  13084 views | 0 replies

• Demonstration: Influence of Outliers

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  Statistical Thinking for Industrial Problem Solving
  
  (view in My Videos)   To explore the impact of unusual observations and outliers on the correlation coefficient, we use the demoCorr script. This script is in the JMP Sample Scripts Directory.   The demo correlation script starts with several observations and allows us to drag points or add new points and exp...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  781 views | 0 replies

• Assessing Correlations

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  Statistical Thinking for Industrial Problem Solving
  
    (view in My Videos)   Correlation is a measure of the linear association between two variables. In this video, we show how to assess correlations for the Cleaning data using three platforms: Graph Builder, Fit Y by X, and Multivariate.   We start with the Graph Builder, which is the first option under the G...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  7523 views | 0 replies

• Fitting a Regression Model

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  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 OD using Graph Builder and Fit Y by X.   We start with the Graph Builder, which is the first option under the Graph menu.   We'll drag Removal to the Y zone, and then drag OD to the X zone. The default grap...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  7283 views | 0 replies

• Evaluating Model Assumptions

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  2957 views | 0 replies

• Interpreting Regression Analysis Results

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  6618 views | 0 replies

• Fitting Polynomial Models

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  8705 views | 0 replies

• Fitting Multiple Linear Regression Models

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  9498 views | 0 replies

• Using the Prediction Profiler

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  13962 views | 1 replies

• Analyzing Residuals and Outliers

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  19958 views | 0 replies

• Fitting a Model with Categorical Predictors

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  3937 views | 0 replies

• Fitting a Model with Interactions

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  12322 views | 0 replies

• Selecting Variables Using Effect Summary

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  3963 views | 0 replies

• Assessing Multicollinearity

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  6852 views | 0 replies

• Fitting a Multiple Logistic Regression Model

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  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 ...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  4200 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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  3230 views | 0 replies

• Designing Full Factorial Experiments

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  10195 views | 0 replies

• Analyzing Full Factorial Experiments

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  5847 views | 0 replies

• Creating 2k-r Fractional Factorial Designs

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  Statistical Thinking for Industrial Problem Solving
  
  (view in My Videos)   In this video, you learn how to create 2^k-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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  5398 views | 0 replies

• Creating Screening Designs in the Custom Designer

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  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 ...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  1903 views | 0 replies

• Designing a Central Composite Design

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  9651 views | 0 replies

• Optimizing Multiple Responses

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  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...

  jules

  Learning Library |

  Dec 3, 2021 1:02 PM

  6346 views | 1 replies