It is desired to test h0: μ = 55 against h1: μ < 55 using α = 0.10. the population in question is normally distributed with a standard deviation of 20. a random sample of 64 will be drawn from this population. if μ is really equal to 50, what is the probability that the hypothesis test would lead the investigator to commit a type ii error?

Suppose you are going to graph the data in the table. minutes temperature (°c) 0 -2 1 1 2 3 3 4 4 5 5 -4 6 2 7 -3 what data should be represented on each axis, and what should the axis increments be? x-axis: minutes in increments of 1; y-axis: temperature in increments of 5 x-axis: temperature in increments of 5; y-axis: minutes in increments of 1 x-axis: minutes in increments of 1; y-axis: temperature in increments of 1 x-axis: temperature in increments of 1; y-axis: minutes in increments of 5