Q: What statistical test should be used to compare whether one of two variables A or B has the greatest influence on variable set C?

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A dummy variable is a variable that takes on the values 1 and 0; 1 means something is true. These are used in statistical analyses.

If there are only two variables, then the dependent variable has only one variable it can depend on so there is absolutely no point in calculating multiple regression. There are no other variables!

Yes and it is called "the line of best fit"

In a statistical model, you have two kinds of variable. Response variables are the "outputs" of your model. Explanatory variables, on the other hand, are the "inputs" of your model. Response variables are dependent on the explanatory variables. Explanatory variable are independent of the response variables.Imagine you were trying to formulate a statistical model of your car's fuel economy. The "output" of your model is miles per gallon (or kilometres per litre). That's your response variable. "Inputs" into your model might be (for example) engine capacity, number of cylinders, tyre pressure, etc. These are your explanatory variables. That is, fuel economy may be, or is, (to be determined by the modeling) dependent on engine capacity and/or number of cylinders and/or tyre pressure, etc.after the treatment

In a statistical model you have two kinds of variable. Response variables are the "outputs" of your model. Explanatory variables, on the other hand, are the "inputs" of your model. Response variables are dependent on the explanatory variables. Explanatory variable are independent of the response variables.Imagine you were trying to formulate a statistical model of your car's fuel economy. The "output" of your model is miles per gallon (or kilometres per litre). That's a dependent variable. "Inputs" into your model might be (for example) engine capacity, number of cylinders, tyre pressure, etc. These are your independent variables. That is, fuel economy may be, or is, (to be determined by the modelling) dependent on engine capacity and/or number of cylinders and/or tyre pressure, etc.

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Yes, the dependent variable is influenced by changes in the independent variable. The relationship between the two variables is typically investigated through statistical analysis to determine the extent of this influence.

A dummy variable is a variable that takes on the values 1 and 0; 1 means something is true. These are used in statistical analyses.

The outcome variable is the dependent variable in a statistical analysis that is being measured or predicted based on changes in other variables, known as independent variables. It is the variable of interest that is being studied to understand its relationship with other variables.

Statistical Process ControlA) TrueB) False

If there are only two variables, then the dependent variable has only one variable it can depend on so there is absolutely no point in calculating multiple regression. There are no other variables!

Manipulating variable weight involves adjusting the numerical value assigned to a specific variable within a statistical model or algorithm. This can be done to give more or less importance to certain variables based on their impact on the model's output. By changing the weight assigned to a variable, you can control its influence on the overall analysis or prediction.

No, it would not. It is possible that the statistical model is under-specified and that the variables being studied are all "caused" by another variable.

Predicting variables are variables used in statistical and machine learning models to predict an outcome or target variable. These variables are used to forecast or estimate the value of the target variable based on their relationships and patterns in the data. Selecting relevant predicting variables is important for building accurate and effective predictive models.

A major variable is a key factor in a research study or statistical analysis that has a significant impact on the outcome or results of the study. It is a variable that researchers are particularly interested in studying due to its potential influence on the research question being investigated. Identifying major variables helps researchers focus their study and interpret the findings accurately.

Yes and it is called "the line of best fit"

A blocking variable is a variable that is included in a statistical analysis to account for the effects of that variable on the outcome of interest. By including a blocking variable, researchers can control for potential confounding factors and ensure that the relationship being studied is accurately captured. Blocking variables are commonly used in experimental design to improve the precision and validity of study results.

In a statistical model, variations in the dependent variable can be attributed to independent variables. However, there is a random element that is not accounted for and this is the stochastic error.