(a) i. î = -1.5+ 8.5æ ii. î = 8.5 – 1.5z iii. j = -4.045 + 7.019r iv. î = 7.019 – 4.045 Use information in Figure 2 and choose the correct regression equation Coefficients" Standardized Coefficients Unstandardized Coefficients Model B Std. Error Beta Sig. (Constant) 8.500 1.211 7.019 .002 -1.500 .371 -.896 -4.045 .016 a. Dopendent Variable: y Figure 2: Estimated regression coefficients (b) i. 5.50 Use Figure 3 to find value of s² ii. 1.375 iii. 22.50 iv. 16.364 ANOVA" Sum of Model Squares df Mean Square Sig. Regression .016 22.500 22.500 16.364 Residual 5.500 4 1.375 Total 28.000 a. Dependent Variable: y b. Predictors: (Constant), x Figure 3: Analysis of Variance Use Figure 3 to find value of r² (c) i. 0.196 ii, 0,804 iii. 5.091 iv. 1.244
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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