A random number generator is supposed to produce random numbers that are uniformly distributed on the interval from 0 to 1 . If this is true, the numbers generated come from a population with μ=0.5 and σ=0.2887 . A command to generate 100 random numbers gives outcomes with mean x¯=0.4365 . Assume that the population ?σ remains fixed. We want to test H0:μ=0.5 Ha:μ≠0.5 (a) Calculate the value of the ?z test statistic. (Enter your answer rounded to two decimal places.) z= Between what two numbers does the P‑value lie? A) between 0.02 and 0.04 B) between 0.01 and 0.02 C) between 0.04 and 0.05 D) above 0.25 Between which two Normal critical values z in the bottom row of Table C does z lie? A) below 0.674 B) between 1.036 and 1.282 C) between 1.960 and 2.054 D) between 2.054 and 2.326 (b) Use Table C: Determine whether z is significant at the 5% level (α=0.05 ). A) significant B) not significant (c) Use Table C: Determine whether z is significant at the 1% level (α=0.01 ). A) significant B) not significant (d) Which is an appropriate conclusion for this test? A) We have good evidence at the 5% level that this random number generator is performing properly. B) We have insufficient evidence to say the random number generator is not performing properly. C) We have strong evidence at the 1% level that this random number generator is not performing properly. D) We have good evidence at the 5% level that this random number generator is not performing properly.
Contingency Table
A contingency table can be defined as the visual representation of the relationship between two or more categorical variables that can be evaluated and registered. It is a categorical version of the scatterplot, which is used to investigate the linear relationship between two variables. A contingency table is indeed a type of frequency distribution table that displays two variables at the same time.
Binomial Distribution
Binomial is an algebraic expression of the sum or the difference of two terms. Before knowing about binomial distribution, we must know about the binomial theorem.
A random number generator is supposed to produce random numbers that are uniformly distributed on the interval from 0 to 1 . If this is true, the numbers generated come from a population with μ=0.5 and σ=0.2887 . A command to generate 100 random numbers gives outcomes with mean x¯=0.4365 . Assume that the population ?σ remains fixed. We want to test
H0:μ=0.5
Ha:μ≠0.5
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