Lydia Delgado Uriarte

The variables vehicle_weight, spoiler_angle and AWD were the ones providing a non-random amount of variance.

The slope in this model is not considered zero according to the p-value that equals 5.35e-11 and it must be more than 0.05% to be consideed zero.

It can predict almost all effectively since the value of R is near 1 being this value 0.7149, if it was 1 it would predict all efectively so this model predict almost all correctly except in some cases .

For the manufacturing loots in total the suspension coils does not exceed the 100 pounds per square inch considering all the lots in total, this is since is the calculation of the lots all together.

The Lots individually the first Lot and second Lot does not exceed the 100 pounds since the value of the Lot1 is 0.9795918 and Lot2 of 7.4693878. Lot 3 does exceed the 100 pounds as well 70 pounds more because the value of the variance is 170.2861124.

The mean highly resembles with all the lots, as well that it is a probability that the null hypothesis is true.

All the lots look similar, the least alike is the third Lot having a mean less than expected and far away from the mean 1500, the first lot was the most close to 1500 and with a p-value of 1.

A car price can be different according to different factors, this is why it is important to put in retroprespective different type of situations as well.

• City where it was manufactured
• Vehicle velocity
• Price
• Year manufactured
• Fuel efficiency

H0= There is NO statistical difference between each sample of fuel.
H1= There is a statistical difference between each sample of fuel.

A Two-Sample T-Test test would be the best in this scenario taking samples to compare, if this value never changes it means H0, otherwise H1.

• Check if significance level is below of 0.05 percent to reject the null hypothesis of the paired t-test.

• Condition of the car
• Model of the car
• Kilometers driven
• Type of fuel used
• Price of the fuel
• Mileage
• Year of purchase
• Location manufactured