Suppose we have collected a random sample from our population, denoted by (xi , yi), i = 1, . . . , n. We now fit a least squares line: yˆi = βˆ 0 + βˆ 1xi (i = 1, . . . , n). What additional assumption do we need in order to carry out statistical inference on our least square estimators βˆ 0 and βˆ 1? c. Using the results we’ve derived in class, prove that the sum of residuals is zero (Pn i=1 ei = 0)
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.
Suppose we have collected a random sample from our population, denoted by (xi , yi), i = 1, . . . , n. We now fit a least squares line: yˆi = βˆ 0 + βˆ 1xi (i = 1, . . . , n). What additional assumption do we need in order to carry out statistical inference on our least square estimators βˆ 0 and βˆ 1? c. Using the results we’ve derived in class, prove that the sum of residuals is zero (Pn i=1 ei = 0)
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