ECON 6001 Economic Principles-Demand and Supply Theories
1. In Graph 1 which is the dependent variable and which is the independent variable
2. In your own words what is Graph 1 postula1ng about the rela1onship between the number of accidents and the number of drink drivers How does the theory underlining Graph 2 differ from the one in Graph 1
3. What does the distance B in Graph 1 represent
4. Using the leCers incorporated in Graph 1, how do you measure the slope of the line
5. Does the slope in Graph 1 measure the amount of accidents caused
by drink drivers Are your conclusions different for Graph 2 Jus1fy your answers.
6. Is the slope of the line posi1ve or nega1ve in Graph 1
7. Is the slope of the line in Graph 1 increasing Briefly jus1fy your answer.
8. What would an increase in traffic conges1on do to the graph
9. Looking at Graph 2 above, which of the following statements is most correct (Write down the op1on you think is the most correct):
(A) The number of road accidents falls as the number of drink drivers increases.
(B) The number of accidents per drink driver goes down as the number of drink drivers decreases.
(C) The rate at which accidents increase falls as the number of drink drivers goes up. Torrens_assessment template.docx
(D) Factors other than alcohol become less important as the number of drink drivers goes up.
10.In your own words outline the hypothesis about the rela1onship between road accidents and the number of drink drivers on the road that is suggested by Graph 2 Does the hypothesis sound feasible to you Jus1fy your answer.
Answer:
- The dependent variable in graph 1 is number of accidents and the independent variable is the number of drink drivers.
- Graph 1 shows positive linear relation between dependent and independent variable. Number of accident increases with the increase in the number of drink driver and vice versa.
On the other hand, Graph 2 represents non-linear relationship between two variables. The curve is positively sloped concave curve. The non-linear relationship tells that at the first stage number of accident increases steadily with the increase in number of drink drivers. The rate of increase in accident decreases after a certain point (Tong, 2012).
- Distance B in graph 1 is the intercept of the curve. It indicates that accident occurs for other reasons even if there are no drink drivers on the road.
- The triangle shown in the graph is the slope of the line (Varian, 2014). The slope of the curve is obtained by the ratio of the change in number of drink driver and change in number of accident. In graph 1, dA/dD is the slope of the line.
- The number of accidents caused by drink drivers cannot be measured by the slope. Only the change in number of accidents can be measured with respect to the changes in the number of drink drivers (Wetzstein, 2013). The conclusion is same for graph 2 irrespective of the shape of the curve.
- Positive slope is reflected in the straight line of graph 1.
- The slope of the line in graph 1 is constant as the curve is linear. Change in number of accident remains constant for change in the same number of drink driver.
- An increase in traffic congestion would rotate the curve upward (Varian, 2014). Number of accident is likely to rise for a given number of drink drivers in both the curves with constant intercept.
(Source: created by author)
- For graph 2, answer (c) is correct. As the slope of the curve decreases after a certain point, the rate of accident falls.
- Hypothesis:
H1: There is a positive non-linear relationship between the number of drink driver and the occurance of road accident.
H0: There is no such relationship between the number of drink driver and the occurance of road accident.
Any research initially focuses on the null hypothesis and tries to prove the alternative hypothesis using collected data. H0 is here null and H1 is research hypothesis.
No, this hypothesis does not sound feasible as the there is no such reason that acts to lessen the figure of road accident after a definite point if the figure of drink driver gets higher. If there is any reason for reducing the rate of accident, that would be true for every point of the curve and not applicable after a certain point (Newbold, Carlson & Thorne, 2012). The reasons behind non-linear relationship cannot be understood from the graph also. Hence, the hypothesis is not seemed to be feasible.
References
Newbold, P., Carlson, W., & Thorne, B. (2012). Statistics for business and economics. Pearson.
Tong, H. (2012). Threshold models in non-linear time series analysis (Vol. 21). Springer Science & Business Media.
Varian, H. R. (2014). Intermediate Microeconomics: A Modern Approach: Ninth International Student Edition. WW Norton & Company.
Wetzstein, M. E. (2013). Microeconomic theory: concepts and connections. Routledge.
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