During its manufacture, a product is subjected to four different tests in sequential order. An efficiency expert claims that the fourth (and last) test is unnecessary since its results can be predicted based on the first three tests. To test this claim, multiple regression will be used to model Test4 score (y), as a function of Test1 score (x1), Test 2 score (x2), and Test3 score (x3). [Note: All test scores range from 200 to 800, with higher scores indicative of a higher quality product.] Consider the model: Ely) = B1 + B1x1 + B2x2 + B3x3 The first-order model was fit to the data for each of 12 units sampled from the production line. The results are summarized in the printout. SOURCE F VALUE DF MS PROB>F MODEL ERROR 3 151417 50472 18.16 .0075 22231 2779 TOTAL 12 173648 ROOT MSE 52.72 R-SQUARE 0.872 DEP MEAN 645.8 ADJ R-SQ 0.824 T FOR 0: ERROR PARAMETER = 0 PROB> ITI PARAMETER STANDARD VARIABLE ESTIMATE INTERCEPT 11.98 80.50 0.1111 0.0986 0.080S Compute a 95% confidence interval for B3 0.15 0.s85 0.2745 0.3762 0.3265 X1(TESTI) 2.47 0.039 X2(TEST2) 3.82 0.005 X3(TEST3) 404 0.004 O 33 + 105 O 33 t.19 33 + 4.04
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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