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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 MoreFractional 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 MoreFractional 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 MoreFull 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 MoreFull 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 MoreFull 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 MoreLogistic 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 MoreChi 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 MoreLogistic 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 MoreLogistic 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 MoreStepwise 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 MoreStepwise 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 MoreBox 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 MoreBox 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 MoreBox 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 MoreMultiple 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 MoreMultiple 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 MoreMultiple 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 MoreSimple 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 MoreSimple 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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