The best way to interpret this equation is for every additional used car sold at $32,162 there is a decrease of 112k in y. 14.8% of the variation of the price can be explained by the equation. This was found by checking the r-squared, which is a powerful tool in Anova; it measures how well future outcomes are likely to be predicted by the model. The reason why we do hypothesis testing is; it’s an assertion about the distribution of one or more random variables. The hypothesis test can be set up:
H0: β1 = 0 With Alpha .05
Ha: Β1 ≠ 0
Reject H0, if p value is <.05
Do not reject Ha, if p value is > or equal to .05
The p value is .006
My decision: Is to reject H0, there is a significant relationship. The predicted model for the dependent variable I got by multiplying the first miles (k) into the equation ŷ= -112.24(100,000) +32,162 which gave me -11,191, 832. This means miles are predicted to decrease by 11,191,832 when price goes up by one. There is a strong negative relationship. The independent variables will affect price of used cars differently. The miles (k) of the car are going to increase/decrease the price of the car, if the miles on the car are too high (ex: over 120,000 miles; price $9,000 also depending on brand of car). The mpg (how many miles car travels on single gallon) is going to increase/decrease sales price of the car if...