Please help, i am struggling to understand. Its due tonight. Thank you.
19%13%32%24%12%
Under 20 20 – 35 36 – 50 51 – 65 Over 65
29 26 67 66 22
(i) Give the value of the level of significance.
State the null and alternate hypotheses.
H0: The distributions for the population 10 years ago and the population today are the same.
H1: The distributions for the population 10 years ago and the population today are different.
H0: Time ten years ago and today are independent.
H1: Time ten years ago and today are not independent.
H0: Ages under 20 years old, 20- to 35-year-old, between 36 and 50, between 51 and 65, and over 65 are independent.
H1: Ages under 20 years old, 20- to 35-year-old, between 36 and 50, between 51 and 65, and over 65 are not independent.
H0: The population 10 years ago and the population today are independent.
H1: The population 10 years ago and the population today are not independent.
(ii) Find the sample test statistic. (Round your answer to two decimal places.)
(iii) Find or estimate the P-value of the sample test statistic.
P-value > 0.100
0.050 < P-value < 0.100
0.025 < P-value < 0.050
0.010 < P-value < 0.025
0.005 < P-value < 0.010
P-value < 0.005
(iv) Conclude the test.
Since the P-value ≥ α, we do not reject the null hypothesis.
Since the P-value < α, we do not reject the null hypothesis.
Since the P-value < α, we reject the null hypothesis.
Since the P-value ≥ α, we reject the null hypothesis.
(v) Interpret the conclusion in the context of the application.
At the 1% level of significance, there is insufficient evidence to claim that the age distribution of the population of Blue Valley has changed.
At the 1% level of significance, there is sufficient evidence to claim that the age distribution of the population of Blue Valley has changed.
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21s2
27s2
Classify the problem as being a Chi-square test of independence or homogeneity, Chi-square goodness-of-fit, Chi-square for testing or estimating σ 2 or σ, F test for two variances, One-way ANOVA, or Two-way ANOVA, then perform the following.
One-way ANOVA
Two-way ANOVA
Chi-square test of independence
F test for two variances
Chi-square test of homogeneity
Chi-square goodness-of-fit
Chi-square for testing or estimating σ2 or σ
(i) Give the value of the level of significance.
State the null and alternate hypotheses.
H0: σ12 = σ22; H1: σ12 > σ22
H0: σ12 = σ22; H1: σ12 ≠ σ22
H0: σ12 < σ22; H1: σ12 = σ22
H0: σ12 = σ22; H1: σ12 < σ22
(ii) Find the sample test statistic. (Round your answer to two decimal places.)
(iii) Find the P-value of the sample test statistic.
P-value > 0.200
0.100 < P-value < 0.200
0.050 < P-value < 0.100
0.020 < P-value < 0.050
0.002 < P-value < 0.020
P-value < 0.002
(iv) Conclude the test.
Since the P-value is greater than or equal to the level of significance α = 0.05, we fail to reject the null hypothesis.
Since the P-value is less than the level of significance α = 0.05, we reject the null hypothesis.
Since the P-value is less than the level of significance α = 0.05, we fail to reject the null hypothesis.
Since the P-value is greater than or equal to the level of significance α = 0.05, we reject the null hypothesis.
(v) Interpret the conclusion in the context of the application.
At the 5% level of significance, there is insufficient evidence to show that the variance for the new manufacturing process is different.
At the 5% level of significance, there is sufficient evidence to show that the variance for the new manufacturing process is not different.
At the 5% level of significance, there is insufficient evidence to show that the variance for the new manufacturing process is not different.
At the 5% level of significance, there is sufficient evidence to show that the variance for the new manufacturing process is different.
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