Statistics Problem: The Analysis of Petrol Prices for Different Years - Assignment Example

PART I: When a scatter plot was generated from the data which the graph shown in diagram 1 was generated. The graph the following can be seen What is the Y-intercept of the linear regression line? The slope of the linear regression line = 0.056 The y-intercept is 0.7 What is the equation of the linear regression line in slope-intercept form? The slope intercept for of the equation is y = 0.056x-111.9 Based on the linear regression line, what would be an estimated cost of gas in the year 2020? The projected price will be 2.8 as indicated by the red line. Select a current price that you have seen or paid recently for gas. Is that price within the range of the linear regression line or is it an outlier? Is it within the confidence interval of 5% or either end? The current recent price in Michigan is 3.49 The predicted price is 2.4 Error of prediction = 3.40-2.4= 1 This error is greater than the standard error of 0.4. This is an indication that the price is not within 5% confidence interval and thus can be considered to be an outlier. Table 1 Regression Statistics Multiple R 0.798774385 R Square 0.638040518 Adjusted R Square 0.624634611 Standard Error 0.400541061 Observations 29 Table 2 ANOVA   df SS MS F Significance F Regression 1 7.635652463 7.635652463 47.59398455 2.05865E-07 Residual 27 4.331694823 0.160433142 Total 28 11.96734729       Residuals From table 3 the residuals have been given this gives the extent to which the predicted prices deviates from the actual prices. The observation column gives the years with observation 1 being the first year in the data least and observation 29 being the last year. Table 3 PART II: This project gives the analysis of petrol prices for different years. The prices of gas were obtained from gas stations in various parts of the country. The prices varied for various moths and the price for the year was obtained by taking the average of the 12 months for every year. The data was used in plotting a scatter graph which with a regression line and its equation as can seen in figure 1. The graph gives a rough estimate of the gas price over the years. From the graph it can be seen that there is a constant increase in gas prices over the years. It is also important to note that the current price is under estimated on the linear regression curve. This may be attributed to the fact that the rate of increase in price up to 2000 seems to be lower than the later years where there is a steeper increase in gas prices. The prediction may be improved if the years from 2000 and below are omitted so that the error in estimation can be improved. If this is done it will be a way of making the current predicted price to closer to the current actual price. It should be noted that the prediction will remain an approximation since prices are affected by many factors. The regression line has been projected to give the approximate prices up to 2032. There are several factors that may changes the prices of and thus affect the regression line. An increase or reduction use of gas in the general population could be have a considerable change in the demand. If the economy of the country improves there is likely to increase consumption of gas (Foss, 2007). This will mean that there will be increased driving and vacationing. On the other hand if government policy is established that will discourage use of personal cars the consumption of gas is likely to reduce considerably below the predicted price. The government may encourage the use of public mean by making it affordable and easily accessible. Cycling can be encourage through offering free parking spaces which increasing the parking fees for privately owned cars. Innovation is the other reason that is likely to dramatically change the demand of petroleum products. Currently there production of bio-fuel is increasing and this can change the future demand for gas. Electric cars are also being brought into the market. Suppose these innovations are adopted widely the demand for gas is likely to below the predicted or at least not to overshoot beyond the predicted. The ability of the producing countries to release to the market the required amount of gas will affect the prices considerably. If in the future there will be a big deficit between the demand and supply the prices are likely to be higher than predicted. The economic performance in different parts of the world. If there is increased industrialization in different parts of the world the demand of gas will be higher (Frankel, 1969).The current projection is based on the current industrialization trend and any rapid increase in industrialization will change the demand leading to prices beyond the predicted prices. This may highly be anticipated as many third world countries recognize that industrialization is the best option in development. On the other hand discovery of petrol in many parts of the world may also ensure that there is steady supply of gas throughout the world. This will cause the price either to be lower than the predicted price or at least will cushion the other factors that are making the prices to shoot, thus the price may be close to predicted price. Considering the supply perspective it is observed that the market price for gas not seen to be necessarily “fair value price” but it is the price at which the consumers are will to pay so as to acquire the gas that producer is not willing to put on the market. The sort time measures by consumers that impact on the price of gas include cutting down on travelling and strict budgeting of mileage a good example being the end of year busy summer comes with a drop in gas prices. The oil reserves within the country also plays a very big role in the price of the commodity as the pattern of oil release may ensure that gas is always near to the required demand (Foss et al,2010). Fluctuations in gas price are common in the short run as this is brought about by the reaction of the to news and events. In the long run markets always will go back to price that can be described as being fair. References Foss. M.M (2007). United States Natural gas prices to 2015. Oxford Institute of Energy Studies, NG. Foss et al (2010). Oil and gas prices and fundamentals, USAEE Dialogue. Frankel, P. (1969). Essential of petroleum: A key to oil economics. Routledge. Mariano E. G. et al. (2006). Historical data provide low-cost estimating tool. Oil and Gas Journal. Read More