Use the sample data collected in Exercise 6 to construct all pairwise comparison confidence intervals to estimate the difference in the mean time required to accelerate from 0 to 60 mph for compact, mid-size, and luxury cars with a simultaneous confidence level of 95%. Interpret the results.

Exercise 6: A random sample of 10 compact cars, 10 mid-size cars, and 10 luxury cars were selected. The time (in seconds) each of the randomly selected cars required to accelerate from 0 to 60 mph was looked up on the Autos.com website. The results are presented below. Car Type Time (in seconds) Required to Accelerate from 0 to 60 mph

Compact 9.3 8.1 10.2 8.8 9.0 9.3 7.7 9.2 9.4 8.6

Mid-Size 6.9 5.7 8.3 7.7 8.6 5.9 6.1 8.8 6.1 7.2

Luxury 5.7 6.3 5.4 4.7 6.2 7.0 5.9 5.3 6.3 5.0

Conduct a hypothesis test using ! = 0.05 to determine whether the mean time required to accelerate from 0 to 60 mph is the same for compact, mid-size, and luxury cars.

Teacher uses these... please no excel because I am trying to figure out the math so that I understand. Thank you

One-Way Analysis of Variance is used to determine if the means for more than two populations are all equal State hypothesis H0: All of the population means are equal H1: Not all of the population means are equal Use ! = 0.05 (unless stated otherwise) Enter the sample from population 1 into L1 Enter the sample from population 2 into L2 Enter the sample from population m into Lm ANOVA(L1,L2, ... Lm) Decision: Reject H0 when p-value " ! Otherwise do not reject H0 State conclusion Linear Regression Analysis is used to predict one variable based on the linear relationship with another Enter the predictor data (xi) into L1 Enter the predicted data (yi) into L2 LinReg(ax+b) L1,L2 The linear regression model that will best predict y based on x is y = ax + b where a is the slope and b is the y-intercept It is appropriate to use the linear regression model to make predictions when the coefficient of determination r2 is close to 1. When r2 is close to 1, the linear regression model fits the sample data very well.