Suppose you were given a 95% confidence interval for the difference in two population means. What could you conclude about the population means if a) The confidence interval did NOT cover zero? b) The confidence interval did cover zero?

(a) The population means are not same.

(b) The population means are same.

Step-by-step explanation:

The hypothesis to test whether there is a significant difference between two population means is:

H₀: There is no difference between the two population means, i.e.               μ₁ - μ₂ = 0.

Hₐ: There is no difference between the two population means, i.e.              μ₁ - μ₂ ≠ 0.

The decision rule based on confidence interval for the difference between two population means is:

If the confidence interval consists the null value of the difference, i.e. 0 then the null hypothesis will be accepted or else it will be rejected.

(a)

It is provided that the confidence interval for the difference between two population means does not contains 0.

Since the null value of the difference is not contained in the interval, it can be concluded that the null hypothesis will be rejected.

Thus, the population means are not same.

(b)

It is provided that the confidence interval for the difference between two population means does contains 0.

Since the null value of the difference is contained in the interval, it can be concluded that the null hypothesis will not be rejected.

Thus, the population means are same.

- "runner b is moving at a slower rate."

- "the graphs shown are graphs of distance(km) against time(min).

the slopes of this graphs are are given by:

slope = (change in y)/(change in x)

= (change in distance)/(change in time)

this is the formula for finding the speed. so, the slope of these graphs represent the speed of the runner.

taking each division in the graph to represent 1 on both axis:

runner a ⇒ speed = (2-0)/(1-0)

= 2/1

= 2 km/min

runner b ⇒ speed = (3-0)/(3-0)

= 3/3

= 1 km/min

this shows that   runner b is moving at a slower rate."

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