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Economics

Part A

What are the main factors that influence the demand for cars and how does each of these factors influence demand?

The demand for cars is determined by the economic standards of the customers. This means that when customers have an increase in the income, the demand for cars increases. In addition, the fashion affects demand in that people demand cars that are in fashion. When cars are not in fashion, their demand declines (Garber & Hoel, 2009). Other factors that affect the demand for cars include the oil prices. An increase in the price of oil causes a decline in the demand for cars. In this case, customers spend a lot on fuel when oil prices increase. The government policies of a country also affect the demand of cars. When the government creates policies to reduce taxes on cars, many people are tempted to buy the products (Welch & Welch, 2010).

What product or service might have a highly positive cross elasticity of demand in the market for cars? Describe its impact on the market for cars.

The market for train, buses, taxis, sea and air transport might have a highly positive cross elasticity of demand in the market for cars. This means that when the price of cars increase, the demand for the other transport services increase because commuters will stop using cars. The two products are close substitutes of each other. Thus, an increase in prices of one product causes an increase in the demand for the other product (Tainsky, 2008).

Describe and show the effects on equilibrium market price and output in the weekly market for newspapers of the following:

A decrease in printing costs

The equilibrium shows the point where the consumers and suppliers agree to trade at a certain price and for a certain quantity of the product (Remler, 2012). A decrease in printing costs causes a shift of the supply curve below the equilibrium. This shows that the suppliers are willing to supply the product because the production costs decline. This causes a decline in the prices of the newspapers in the short run. However, the prices will remain constant in the long run because the suppliers will not be willing to reduce the prices below a given point.

An increase in consumer income

Increase in consumer income increases the demand for the weekly newspaper in the short run. This is because the consumers have a high purchasing power. Therefore, the demand curve shifts above equilibrium. This causes an increase in the prices because the quantity demanded also increases. However, in the long run, the increase in consumer income will have no effect on the prices of newspapers because the product is not a basic good.
(c)       A substantial reduction in the price of iPads
The price of iPads cannot affect the equilibrium of the weekly newspaper. The two products are not substitutes neither are they complements to each other. This means that the change in the price of iPads will not change the equilibrium of the weekly newspaper. It is possible that in the long run a substantial reduction on the price of iPad will lead people to read news from internet rather than reading from a newspaper. This will cause a reduction in the demand in the long-run.

Why are cigarettes taxed so heavily? Explain using demand curve analysis.

According to Vilcox and Mohan (2007), cigarettes are luxury products, and people are willing to buy the products even when the prices increase. Cigarettes are also addictive, and it is hard for the consumers to abandon the consumption of such products. On the other hand, the government has been fighting against the consumption of cigarettes. This has resulted to heavy taxation of cigarettes. The demand for cigarettes disobeys the demand rule. Therefore, an increase in the prices results in an increase in the demand. This is illustrated in the graph below. From the graph, it is evident that when the prices increase, the quantity demanded also increases from D1 to D2. This structure does not obey the normal demand curve, which shows that when prices increase, the demand increases (McEachern, 2012).
Part B

A brief description of the meaning of the data and the data sources.

The data were collected from 1980 to 2010, and it shows the air transport movements and the number of terminal passengers. The two variables are related because both variables are increasing with time. The source of the data is Traffic at UK Airports from 1950 onwards.

A description of the main movements over time of air transport movements and terminal passengers

There has been an increase in the number of air transport movements from 1980 to 2010. In addition, the number of terminal passengers has been increasing over the years. The values for the two variables seem to increase with time.

A description on the scatter graph linking air traffic movements and Terminal passengers

The scatter plot indicates that there has been an increase in the number of air transport movements compared to terminal passengers. There is a positive relationship between the air transport movements and the terminal passengers. The scatter plot shows that most of the points lie within the line of best fit. There are a few points that are slightly away from the line of best fit. This shows that the population data had few tails and that the errors were minimized during the process of collecting data (Mun, 2010).

An interpretation of the value of the correlation coefficient and the coefficient of determination between “air transport movements” and “terminal passengers”, in the context of the data.

The correlation coefficient is 0.994162723. This shows that a unit change in the air transport movement causes 0.994162723 changes in the number of terminal passengers. This shows that there is a positive and strong relationship between air transport movements and the number of terminal passengers.

The equation of the regression line produced in the context of the meaning of the data.

The regression equation line equation is y=132. 23x-75095. This means that the gradient of the equation is 132.23, while the y-intercept is -75095. Therefore, there is a positive relationship between the air transport movements and the number of terminal passengers. The gradient indicates that a unit change in the air transport movement results in an increase in the number of terminal passengers by 132.23. When the number of terminal passengers is zero, the air transport movement is -75095.

Report on the predictions made. Comment on the acceptability of using the regression equation to make these predictions.

The regression equation is used to predict the number of passengers that would be expected if the number of air traffic movements is 2 million and 3 million annually. It has been established that, when air transport movements is 2 million annually, the number of terminal passengers is 264.384.905. On the other hand, when the air transport movement is 3 million annually, the number of terminal passengers is 396.614.905. The regression equation is acceptable to make the predictions because the coefficient of determination is high (0,9884). The coefficient of determination shows that there is a good fit of the data in the regression line (Waters, 2003).

Comment on the outliers found

There are two outliers found in the scatter plot. This includes point 2.124 and 218.12. These points show that there were few errors during the measurement. In addition, the outliers show that the population has few tailed-distribution points.

 


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