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(d) Compute a Critical Region (CR) for a level 0.01 test of Ho: 0:=1 = 1 vs. H₁: (e) Give the CR Report corresponding to part (d). ¹4+ S # #1.

(d) Compute a Critical Region (CR) for a level 0.01 test of Ho: 0:=1 = 1 vs. H₁: (e) Give the CR Report corresponding to part (d). ¹4+ S # #1.

Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
Glencoe Algebra 1, Student Edition, 9780079039897, 0079039898, 2018
18th Edition
ISBN:9780079039897
Author:Carter
Publisher:Carter
Chapter10: Statistics
Section10.3: Measures Of Spread
Problem 1GP
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Exercise 1. Revisit Example 1. (d) Compute a Critical Region (CR) for a level 0.01 test of Ho: = 1 vs. H1: # 1. (e) Give the CR Report corresponding to part (d).
Transcribed Image Text:Exercise 1. Revisit Example 1. (d) Compute a Critical Region (CR) for a level 0.01 test of Ho: = 1 vs. H1: # 1. (e) Give the CR Report corresponding to part (d).
A SRS of 50 male employees and, independently, a SRS of 40 female employees were taken from a certain technology sector. The salaries (in $10,000s) were recorded for each of the 90 sampled employees. The resulting data for males can be summarized as (x, = 8.5, s, = 3.1, n1 = 50) and for females (x2 = 7.7, s2 = 2.5, n2 = 40). between the mean salary for males in this technology sector and the mean salary for females in this technology sector. population of males and the population of females? Use these data to reduce uncertainty about the difference Is it reasonable to assume that the variability in salaries is the same for the Data Model: X:S1, X: S2, S1 = SRS(n, = 50, P,), S2 SRS(n2 40, P2), and SRS's are indep. Here, X = salary and {all male employees in sector} and P2 {all female employees in sector}. %3D We assume that the sample distributions support (using the FTS) the assumptions, X:P, ~ N(µ1,0f) and X: P, - N(u2, 02). Under these assumptions, we are in the Two Sample Inference Setting (with Normality). Targets of Inference: µ1 – µz = mean(X: P,) – mean(X: P2) = difference bw popn means. - теап of lož = var(X: P;)/var(X:P2) = ratio of popn variances.
Transcribed Image Text:A SRS of 50 male employees and, independently, a SRS of 40 female employees were taken from a certain technology sector. The salaries (in $10,000s) were recorded for each of the 90 sampled employees. The resulting data for males can be summarized as (x, = 8.5, s, = 3.1, n1 = 50) and for females (x2 = 7.7, s2 = 2.5, n2 = 40). between the mean salary for males in this technology sector and the mean salary for females in this technology sector. population of males and the population of females? Use these data to reduce uncertainty about the difference Is it reasonable to assume that the variability in salaries is the same for the Data Model: X:S1, X: S2, S1 = SRS(n, = 50, P,), S2 SRS(n2 40, P2), and SRS's are indep. Here, X = salary and {all male employees in sector} and P2 {all female employees in sector}. %3D We assume that the sample distributions support (using the FTS) the assumptions, X:P, ~ N(µ1,0f) and X: P, - N(u2, 02). Under these assumptions, we are in the Two Sample Inference Setting (with Normality). Targets of Inference: µ1 – µz = mean(X: P,) – mean(X: P2) = difference bw popn means. - теап of lož = var(X: P;)/var(X:P2) = ratio of popn variances.
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