The amount of kerosene used by a typical household in a week (Q, in litres) is found to have a strong correlation with the price of kerosene (P, in $). For 6 observations, the following data was collected: Q P 11.4 4.00 14 3.50 16 3.00 18 2.50 20. 2.00 22 1.50 (a) Create a regression relationship for this data. (b) Perform a hypothesis test to determine whether the slope coefficient obtained in part (a) above issignificant. (c) Use the regression equation in part (a) above to predict the quantity of kerosene used by a household when the price is $3.75 per litre. (d) Calculate the price elasticity of demand for kerosene at a price of $3.75 (e) Using a further calculation, discuss how well the regression equation in part (a) above fits the data. (f) Discuss whether the relationship between the price and quantity could be due to the income effect, the substitution effect, or both
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.
The amount of kerosene used by a typical household in a week (Q, in litres) is found to have a strong
Q P
11.4 4.00
14 3.50
16 3.00
18 2.50
20. 2.00
22 1.50
(a) Create a regression relationship for this data.
(b) Perform a hypothesis test to determine whether the slope coefficient obtained in part (a) above issignificant.
(c) Use the regression equation in part (a) above to predict the quantity of kerosene used by a household
when the price is $3.75 per litre.
(d) Calculate the price elasticity of demand for kerosene at a price of $3.75
(e) Using a further calculation, discuss how well the regression equation in part (a) above fits the data.
(f) Discuss whether the relationship between the price and quantity could be due to the income effect, the substitution effect, or both
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