The Minister of Finance has hired you as an economic consultant to estimate the impact of a new tax on carbon use on household spending. To do this, you have obtain data for a large sample of 1,500 individuals over the last 5 years (7,500 observations in total) in the United Kingdom where a carbon tax was put in place 3 years ago. You start with a very simple regression model to get a feel for the data: Y = A+ BX + e where Y = annual spending on all goods and services and X = tax price on carbon. Unfortunately the tax price on carbon varies by type of individual, so it is measured with error. a) If the true value of B is known to be -0.79, based on reliable estimates for other countries, the variance of the measurement error is estimated to be 2.4, and the varian of X is estimated to be 1.6, the estimated slope coefficient for B could be biased by an amount equal to: b) This type of measurement error will result in the slope B being:
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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