Identify dependent and independent variables Develop a regression model to predict the Intention to Purchase Branded Shoes. 1. 11. Interpret the value of 2.902. iv. Explains the value of R and Adjusted R Squared. V. Determine which independent variables that are significant in predicting Intention to Purchase Branded Shoes? Test using a = 0.05. Based on your answer above (v), develop the best regression model to predict the Intention to Purchase Branded Shoes. vi.
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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Solved in 2 steps
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Based on the SPSS output given below is the Intention to Purchase Branded Shoes, Brand
Awareness, Brand Association, Brand Perception and Brand Loyalty on customers in Kangar,
Perlis for the year 2018.
Model Summaryb
Mo
del R R Square
Adjusted R
Square
Std. Error of the
Estimate
Durbin-
Watson
1 .218a
.047 .036 1.08730 1.158
ANOVAa
Model
Sum of
Squares df Mean Square F Sig.
1 Regression 18.972 1 4.743 4.012 .003b
Residual 380.672 328 1.182
Total 399.645 329
Coefficientsa
Model
Unstandardized Coefficients
Standardized
Coefficients
B Std. Error Beta t Sig.
1 (Constant) 2.7 .412 7.050 .000
Brand Awareness -.107 .060 -.101 -1.774 .077
Brand Association -.073 .054 -.077 -1.351 .178
Brand Perception -11.228 5.552 -7.996 -2.022 .044
Brand Loyalty 11.415 5.565 8.110 2.051 .041
i. Identify dependent and independent variables
ii. Develop a regression model to predict the Intention to Purchase Branded Shoes.
iii. Interpret the value of 2.902.
iv. Explains the value of R and Adjusted R Squared.
v. Determine which independent variables that are significant in predicting Intention to
Purchase Branded Shoes? Test using α = 0.05.
vi. Based on your answer above (v), develop the best regression model to predict the
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