Retail price data for n= 60 hard disk drives were recently reported in a computer magazine. Three variables were recorded for each hard disk drive: y = Retail PRICE (measured in dollars) x1 = Microprocessor SPEED (measured in megahertz) (Values in sample range from 10 to 40) x2 = CHIP size (measured in computer processing units) (Values in sample range from 286 to 486) A first-order regression model was fit to the data. Part of the printout follows: Analysis of Variance SOURCE DF MS F VALUE PROB>F MODEL 2 34593103.008 17296051.504 19.018 0.0001 ERROR 57 51840202.926 909477.24431 C TOTAL 59 86432305.933 ROOT MSE DEP MEAN C.V. 953.66516 3197.96667 0.4002 0.3792 R-SQUARE ADJ R-SQ 29.82099 Test to determine if the model is adequate for predicting the price of a computer. Use a = .01.
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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