On linear regression: Part a. Which of the following are standard assumptions of a linear regression model (check all that apply) : The predictor and response variable are both normally distributed. The residuals are all equal to zero. The relationship between the predictor and response variable is linear. The residuals have an approximately normal distribution. A line describes all of the predictable relationship between the predictor and response variables. part B: Which of the following are assumptions made about the errors in a simple linear regression model? (check all that apply): Observations from a uniform distribution Distribution has standard deviation = 1 Dependent observations Independent observations Observations from a normal distribution Distribution has constant standard deviation Distribution has mean = zero
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.
On linear regression:
Part a.
Observations from a uniform distribution
Distribution has standard deviation = 1
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