A researcher is interested in finding out the factors which determined the yearly spending on family outings last year (Y, measured in dollars) She compiles data on the number of members in a family (X,), the annual income of the family (X,), and the number of times the family went out on an outing in the last year (X2). She collects C from 152 families and estimates the following regression: Y= 120.45 + 1.12X, +2.12X, + 1.98X3- Suppose B4. ß2, B3, denote the population slope coefficients of X,, X2, and X3, respectively The researcher wants to check if neither X, nor X, have a significant effect on Y or at least one of them has a significant effect, keeping X, constant. She calculates the value of the F-statistic for the test with the two restrictions (Ho: B = 0, B, =0 vs. H,: B, #0 and/or B, #0) to be 3.00. The p-value for the test will be (Round your answer to two decimal places.) v the null hypothesis. At the 10% significance level, we will reject fail to reject Enter your answer in the answer box.
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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