Use the following linear regression equation to answer the questions. X1 = 1.3 + 3.0x2 - 8.3x3 + 1.6x4 (a) Which variable is the response variable? O X1 X2 X3 X4 Which variables are the explanatory variables? (Select all that apply.) V XA V X3 O x2 X1 (b) Which number is the constant term? List the coefficients with their corresponding explanatory variables. constant X2 coefficient X3 coefficient X4 coefficient (c) If x2 = 8, x3 = 2, and x4 = 6, what is the predicted value for x1? (Use 1 decimal place.) (d) Explain how each coefficient can be thought of as a "slope" under certain conditions. O If we hold all other explanatory variables as fixed constants, then we can look at one coefficient as a "slope." If we hold all explanatory variables as fixed constants, the intercept can be thought of as a "slope." If we look at all coefficients together, the sum of them can be thought of as the overall "slope" of the regression line. If we look at all coefficients together, each one can be thought of as a "slope."
Correlation
Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.
Linear Correlation
A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.
Regression Analysis
Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.
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