A marketing analyst wants to examine the relationship between sales (in $1,000s) and advertising (in $100s) for firms in the food and beverage industry and so collects monthly data for 25 firms. He estimates the model: Sales = Bo + B1 Advertising + ɛ. The following table shows a portion of the regression results. Coefficients Standard Error t-stat p-value Intercept 41.1 13.88 2.961 0.0046 Advertising 2.72 1.7 -1.6 0.0586 When testing whether the slope coefficient differs from 3, the value of the test statistic is Multiple Choice t23 = 1.809 t23 = 0.165 t23 = -0.165 t23 = -1.809
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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