Consider the following data for a dependent variable y and two independent variables, x, and x,. X1 x2 y 30 12 93 47 10 108 25 17 112 51 16 178 40 5 94 51 19 175 74 7 170 36 12 117 59 13 142 76 16 213 The estimated regression equation for these data is ŷ = -20.24 + 2.04x, + 4.78x2. Here, SST = 15,563.6, SSR = 14,417.6, s,, = 0.2488, and = 0.9548. (a) Test for a significant relationship among x,, X2, and y. Use a = 0.05. State the null and alternative hypotheses. O Ho: B, + 0 and ß, = 0 H: B, = 0 and ß, # 0 O Ho: B1< B2 Hgi Pq Z Bz Hoi Bz = B2 = 0 H: One or more of the parameters is not equal to zero. O Ho: B1 > B2 Hai B1 s Bz O Hoi B. + 0 and Ba + 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.
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