Consider the following model: y = ß0 + ß1x + ε . In this model, the following is true: a) there is a deterministic component and a random component in the model equation b) the error term in the model represents cumulative mistakes made by the researcher in computing his/her statistics on the sample c) the intercept value is necessarily equal to 0 for any sample on which this equation is applied (as evidenced by the fact that it is indicated by the subscript 0) d) when computed on a sample, the slope must always be positive for this equation to work e) if we were to use this equation for prediction, we’d place “hats” above certain values in the equation f) a and e g) a, b and e
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.
Consider the following model: y = ß0 + ß1x + ε . In this model, the following is true:
a) there is a deterministic component and a random component in the model equation
b) the error term in the model represents cumulative mistakes made by the researcher in computing his/her statistics on the sample
c) the intercept value is necessarily equal to 0 for any sample on which this equation is applied (as evidenced by the fact that it is indicated by the subscript 0)
d) when computed on a sample, the slope must always be positive for this equation to work
e) if we were to use this equation for prediction, we’d place “hats” above certain values in the equation
f) a and e
g) a, b and e
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