a) Consider the information given in the two ANOVA table below corresponding to the linear regression model: Y = Bo + B1x1 + Bzx2 + B3x3 + ɛ. Fill the missing information in the ANOVA table below: Source of DF SS MS F Variation Regression 77.76 Error Total 16 121.50 ANOVA table for the linear regression model b) Test the null hypothesis Ho: B1 = B2 = B3 = 0 against the appropriate alternative at a = 0.05 level of significance. c) Calculate R and adjusted-R2 from the data given in the ANOVA table in a) above.
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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to the linear regression model: Y = Bo + B1x1 + B2x2 + B3X3 + E. Fill the
missing information in the ANOVA table below:
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Source of
DF
SS
MS
F
Variation
Regression
77.76
Error
Total
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121.50
ANOVA table for the linear regression model
b) Test the mull hypothesis Ho: B1 = B2 = B3
alternative at a = 0.05 level of significance.
= 0 against the appropriate
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c) Calculate R- and adjusted-R- from the data given in the ANOVA table in a)
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