Factors that influence demand for cars

Demand can be defined as a force that drives a consumer to desire for a certain product and the willingness to pay for the price of the good or service.

The following factors affect the demand of a certain good.

Product price. Since demand is the desire for a consumer to pay a certain price for a given good or service this will offer the satisfaction for his or her desire. Price determines the quantity of a product that customers will be willing to buy given the price of the product. Any increase in price of a product will adversely affected the quantity demanded in that the quantity will fall while a reduction in price will lead to an increase in quantity demanded. The same law applies to purchase of cars.

Price of related goods affect the quantity demanded of a product in that any increase in price of related products will increase demand for the product. it mostly apply to related goods that can be used as substitutes. Of the price of the substitute rises, the quantity demanded lowers and people to substitute the good with another and thus the quantity of this product will rise.

Income of the consumer.

Tastes and preferences.

Advertising.

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, 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.

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.

Comment on the outliers found (if any)

There are two outliers found in the scatter plot, that is 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.

Reference List

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