# Improve Phase

## Fractional Factorial Designs with JMP

What Are Fractional Factorial Experiments? In simple terms, a fractional factorial experiment is a subset of a full factorial experiment. Fractional factorials use fewer treatment combinations and runs Fractional factorials are less able to determine effects because of fewer degrees of freedom available to evaluate higher order interactions Fractional factorials can be used to screen…

Read More## Fractional Factorial Designs with SigmaXL

What Are Fractional Factorial Designs with SigmaXL? In simple terms, a fractional factorial design with SigmaXL is a subset of a full factorial experiment. Fractional factorials use fewer treatment combinations and runs. Fractional factorials are less able to determine effects because of fewer degrees of freedom available to evaluate higher order interactions. Fractional factorials can…

Read More## Fractional Factorial Designs with Minitab

What Are Fractional Factorial Experiments? In simple terms, a fractional factorial experiment is a subset of a full factorial experiment. Fractional factorials use fewer treatment combinations and runs. Fractional factorials are less able to determine effects because of fewer degrees of freedom available to evaluate higher order interactions. Fractional factorials can be used to screen…

Read More## Full Factorial DOE with JMP

Full Factorial DOE In a full factorial experiment, all of the possible combinations of factors and levels are created and tested. For example, for two-level design (i.e.each factor has two levels) with k factors, there are 2k possible scenarios or treatments. Two factors, each with two levels, we have 22 = 4 treatments Three factors, each…

Read More## Full Factorial DOE with Minitab

What is a Full Factorial DOE? In a full factorial experiment, all of the possible combinations of factors and levels are created and tested. For example, for two-level design (i.e.each factor has two levels) with k factors, there are 2k possible scenarios or treatments. Two factors, each with two levels, we have 22 = 4 treatments…

Read More## Full Factorial DOE with SigmaXL

What is a Full Factorial DOE with SigmaXL? In a Full Factorial DOE with SigmaXL, all of the possible combinations of factors and levels are created and tested. For example, for two-level design (i.e.each factor has two levels) with k factors, there are 2k possible scenarios or treatments. Two factors, each with two levels, we…

Read More## Logistic Regression with SigmaXL

What is Logistic Regression with SigmaXL? The Logistic Regression with SigmaXL is a statistical method to predict the probability of an event occurring by fitting the data to a logistic curve using logistic function. The regression analysis used for predicting the outcome of a categorical dependent variable, based on one or more predictor variables. The…

Read More## Chi Square Test with SigmaXL

Chi Square Test with SigmaXL (Contingency Tables) We have looked at hypothesis tests to analyze the proportion of one population vs. a specified value, and the proportions of two populations, but what do we do if we want to analyze more than two populations? A chi-square test with SigmaXL is a hypothesis test in which…

Read More## Logistic Regression with Minitab

What is Logistic Regression? Logistic regression is a statistical method to predict the probability of an event occurring by fitting the data to a logistic curve using logistic function. The regression analysis used for predicting the outcome of a categorical dependent variable, based on one or more predictor variables. The logistic function used to model…

Read More## Logistic Regression with JMP

What is Logistic Regression? Logistic regression is a statistical method to predict the probability of an event occurring by fitting the data to a logistic curve using logistic function. The regression analysis used for predicting the outcome of a categorical dependent variable, based on one or more predictor variables. The logistic function used to model…

Read More## Stepwise Regression with JMP

What is Stepwise Regression? Stepwise regression is a statistical method to automatically select regression models with the best sets of predictive variables from a large set of potential variables. There are different statistical methods used in stepwise regression to evaluate the potential variables in the model: F-test T-test R-square AIC Three Approaches to Stepwise Regression…

Read More## Stepwise Regression with Minitab

What is Stepwise Regression? Stepwise regression is a statistical method to automatically select regression models with the best sets of predictive variables from a large set of potential variables. There are different statistical methods used in stepwise regression to evaluate the potential variables in the model: F-test T-test R-square AIC Three Approaches to Stepwise Regression…

Read More## Box Cox Transformation with Minitab

What is a Box Cox Transformation? Data transforms are usually applied so that the data appear to more closely meet assumptions of a statistical inference model to be applied or to improve the interpret-ability or appearance of graphs. Power transformation is a class of transformation functions that raise the response to some power. For example,…

Read More## Box Cox Transformation with SigmaXL

Box Cox Transformation Data transforms are usually applied so that the data appear to more closely meet assumptions of a statistical inference model to be applied or to improve the interpret-ability or appearance of graphs. Power transformation is a class of transformation functions that raise the response to some power. For example, a square root…

Read More## Box Cox Transformation with JMP

What is a Box Cox Transformation? Data transforms are usually applied so that the data appear to more closely meet assumptions of a statistical inference model to be applied or to improve the interpret-ability or appearance of graphs. Power transformation is a class of transformation functions that raise the response to some power. For example, a…

Read More## Multiple Linear Regression with JMP

What is Multiple Linear Regression? Multiple linear regression is a statistical technique to model the relationship between one dependent variable and two or more independent variables by fitting the data set into a linear equation. The difference between simple linear regression and multiple linear regression: Simple linear regression only has one predictor Multiple linear regression…

Read More## Multiple Linear Regression with SigmaXL

What is a Multiple Linear Regression with SigmaXL? the Multiple Linear Regression with SigmaXL is a statistical technique to model the relationship between one dependent variable and two or more independent variables by fitting the data set into a linear equation. The difference between simple linear regression and multiple linear regression: Simple linear regression only…

Read More## Multiple Linear Regression with Minitab

What is Multiple Linear Regression with Minitab? The multiple linear regression with Minitab is a statistical technique to model the relationship between one dependent variable and two or more independent variables by fitting the data set into a linear equation. The difference between simple linear regression and multiple linear regression: Simple linear regression only has…

Read More## Simple Linear Regression with Minitab

What is Simple Linear Regression with Minitab? The Simple linear regression with Minitab is a statistical technique to fit a straight line through the data points. It models the quantitative relationship between two variables. It is simple because only one predictor variable is involved. It describes how one variable changes according to the change of…

Read More## Simple Linear Regression with JMP

What is Simple Linear Regression? Simple linear regression is a statistical technique to fit a straight line through the data points. It models the quantitative relationship between two variables. It is simple because only one predictor variable is involved. It describes how one variable changes according to the change of another variable. Both variables need…

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