Mwaimoffat

Hypotheses Introduction A hypothesis is an explanation which is not fully accepted so as to describe certain phenomena. These statements or theories are mostly tentative answers to scientific questions. Although these statements are tentative, they also have to be testable since they are followed by experiments so as to prove whether or not the hypothesis are true. In this study, several hypotheses will be developed with the aim of proving the factors that are influential to the prices of used vehicles from the data provided below. Hypothesis 1 The older the car, the cheaper it becomes. Graph Results It is noted that the R squared of this graph is 0.2785. On further investigations it was noted that from the analysis of price against Years of usage the R squared yielded was 1. Conclusion From the analysis it was clear that 27.85% of the data did not agree with the fact that older vehicles were cheaper. Nonetheless, from the chart it was clear that the newer and younger a vehicle the more expensive it is. Therefore, it is true that the older the vehicle, the cheaper it becomes. Hypothesis 2 The greater the size of the vehicle’s engine, the higher the price of the used vehicle. Graph From the data collected from the table, a price against engine scatter graph was plotted and the following was depicted: Furthermore, from the performance of the Anova analysis, the following data was realized: Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .607a .368 - a. Predictors: (Constant), Engine ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression- .000b Residual- Total- a. Dependent Variable: Price b. Predictors: (Constant), Engine Coefficientsa Model Unstandardized Coefficients Standardized Coefficients t Sig. B Std. Error Beta 1 (Constant) - -2.485 .014 Engine- - .000 a. Dependent Variable: Price Result As depicted from the graph, the r squared of the relationship between car engines and their prices is 0.368. This was also the data that was depicted from the ANOVA analysis that was carried out. Furthermore, from the data it is clear that the model had one degrees of freedom and the adjustable r square is 0.365. Sub Hypothesis 1 The number of gears in a vehicle do not affect the price of used vehicles. Graph Results From the graphs above it is shown that the sum of the prices of the cars with five gears is higher than the others, this was followed closely by the cars with automatic transmissions which was about 500000 with those from 4 and 6 gears recording the lowest sum. Furthermore, the r squared of the scatter plot is 0.317. Sub hypothesis The distance covered by the vehicle since it was bought increased the price of the vehicle. Graph Result From the R squared of the graph it is 5.155E-4. Additionally, it can be seen that slope of the graph is 3.5 3E-0.11. Conclusions Although the engine and the prices of used cars regress linearly, it is clear that the prices of these vehicles are not determined by the size of engines. This is from the adjusted R-square it can be clearly seen that only 36.5% of the prices of used vehicles were determined by the number of engines. The remaining 73.5% were not affected by the sizes of the engines. Additionally, from the area graph it is seen that vehicles that have covered a great distance are low in price. This means that the lower the distance covered the higher the price. This is also supported by the way the regression of the line of best fit in the scatter plot. This is because it is shown that vehicles that have covered a higher ground and distance are cheaper compared to those that haven’t covered quite much. Therefore, the distance covered by the vehicles do not increase the price of the cars but rather decrease them. Moreover, it can be noted that vehicles with six and four gears are cheaper than those with automatic and five gears. This is because the sum of their prices is higher than those of cars with six and four gears. Nonetheless, the type of transmission is a reason as to why prices of vehicles change. It is therefore, clear that the car prices are affected by the gears it has. Hypothesis 3 The make of vehicles increases the prices of used car. Graph Result The R squared of the graph is 0.00218 with its slope being 14.782. Sub hypothesis The colors of the vehicle decrease the prices of used vehicle. Graph Results It was witnessed that the R squared of color against price of vehicles is 0.00218 while its slope is 14.782. Furthermore, from the chart of color against the price of used cars ed vehicles had a total sum of 1180 one of the highest sums of car prices. Conclusion From the R square of the graph it can be seen that the make of the vehicle does not affect the price of used cars. Nonetheless, it affects the price of 2.18% of the vehicles and the 97.2% of these cars were not affect by the make of the vehicle. Therefore, prices of these cars are not increased by their make. Similarly, it was witnessed that color did not affect the prices of the cars. However, in total red vehicles were bought at the highest price in total followed by blue and brown vehicles were the least valued in total. Hypothesis 4 Cars with additional features are more expensive that those that are not fitted with these features. Sub Hypothesis 1 In order to prove this hypothesis these vehicle features were subdivided into the number of doors, airbags and air conditioning. First there is need to prove that vehicle with air conditioning devices are more expensive that those that do not have them. Graph Results From the graph it has been seen that the R squared of the scatter graph is -0.156. Additionally, from the chart it can be noted the most priced vehicle had no air conditioner. Conclusion Although the most expensive vehicle had no air conditioner, from closely analyzing the chart of the price of Used Vehicles against Air Conditioning it can be noted that most vehicles without air conditioners were cheaper than those that had one fitted in them. The usage of this chart was important since the r squared that was portrayed was negative, this is because the chosen model of graphing was not fit in the analysis of the data, therefore leading to the disregard of the data collected from the scatter graph. Sub hypothesis 2 Cars that have air bags are more expensive than those without air bags Graph Results From the chart it can be seen the most expensive vehicle was fitted with an airbag whereas the cheapest did not have any air bag. Furthermore, from the scatter graph it can be seen that the R squared of the graph is 0.0218. Conclusion It is clear that vehicles with fitted airbags are expensive in comparison to those that have no air bags. Moreover, from the scatter graph only about 2.18% of the vehicle’s prices were not determined by the airbag presence meaning that 97.82% of the prices were determined by the air bag feature in the vehicle. Sub Hypothesis 3 The prices of used cars are not determined by the number of doors the car has. Graph Results From the chart of price against the number of doors it can be seen that the r squared of the chart is -0.156. However, from the scatter graph the r squared is 0.0025. Additionally, it can be noted that the vehicle with the highest price was fitted with only two doors and so was the cheapest one. Conclusions Although other features affect the prices of used cars, the number of doors a vehicle had did not influence in any way the prices of the vehicles. This is because only 0.25% of the vehicles’ prices were influenced by the number of doors they had. The remaining 99% were not. Therefore, the number of doors does not affect the price of cars. Hypothesis 5 The prices of used car decrease if the car does not have any history of being serviced. Graph Results The scatter plot shows the r squared of the prices of Used Vehicles Against Their service history is 0.218 with a positive slope. Furthermore, it is also seen that the total price of vehicles that have undergone some services is more that those that have not undergone any services. Conclusion Vehicles that had gone through servicing had a total price of 816472 whereas those that did not undergo any service were sold at a total price of 501650. It is clear that serviced vehicles are a bit expensive than those that have not yet had any services done to them. Furthermore, from the scatter plot the hypothesis was 21.8% false and so making it 88.2% true. Sub hypothesis The higher the number of owners, the higher the price of used vehicles Graph Results This is graph shows that with the increasing number of owners a car’s value increases. This is because the slope of the graph is 170.86 and this line intercepts the Y- axis at 6223.7. Conclusions From the graph it can be noted that a high number of owners increases the prices of a vehicle. This is a fact that is only 0.1% correct. Therefore, 99.9% of the vehicle’s prices decrease with an increase of owners. This means that vehicles that have had only one owner are priced higher than those that have had more than 14 owners.