Answer:
Check Explanation
Step-by-step explanation:
A) The null hypothesis would be that the proportion of newly hired candidates that are not white is not significantly different from the proportion of the applicants that are not white & there is no significant evidence that the company's hiring practices are discriminatory.
Mathematically,
H₀: μ₀ = 0.53
And the alternative hypothesis would be that there is a significant difference between the proportion of newly hired candidates that are not white is not significantly different from the proportion of the applicants that are not white. More specifically, that the proportion of newly hired candidates that are not white is significantly less than the proportion of applicants that are not white & there is significant evidence that the company's hiring practices are indeed discriminatory.
Mathematically,
Hₐ: μ₀ < 0.53
B) The two errors that can come up in this hypothesis testing include -
Type I error: We reject the null hypothesis because we obtain that the proportion of newly hired candidates that are not white is significantly less than the proportion of applicants that are not white and conclude that there is indeed significant evidence that the company's hiring practices are discriminatory when in reality, there is no significant difference and hence, no discrimination.
Type II error: We accept the null hypothesis (fail to reject the null hypothesis) because we obtained that there is no significant difference between the proportion of newly hired candidates that are not white & th proportion of applicants that are not white and conclude that there is no discrimination in the company's hiring practices when in reality, there is significant difference in the stated proportions above and significant evidence that there is indeed significant evidence that the company's hiring practices are discriminatory.
C) The power of the test increases as the significance level reduces. This is because t-statistic increases as significance level reduces.
D) The standard error of the mean used in computing the t-score is given as
σₓ = (σ/√n)
It is evident that as the value of n increases, the standard error reduces and this widens the effect of the test, hence, the power of the test increases.
Hope this Helps!!!
Answer:
i think you add both fabric prices then divide with 5.50
General Idea:
The relationship between rate(R), distance(D) and time(T) given below:
Applying the concept:
We need to make use of the formula to find Kelly's walking rate before and after her snack
Option A isn't correct because before snack Kelly walking rate is not 4/14 miles per hour.
Option B is <u>Correct,</u> Kelly walking rate after snack is 2 2/3 miles per hour.
Option C isn't correct because it doesn't took Kelly 2 hours longer to walk 1/6 mile than it did for her to walk 1/4 mile. It took 1/112 hour longer.
Option D isn't correct because 2 2/3 miles per hour is slower than 3 1/2 miles per hour.
Conclusion:
Option B is <u>Correct,</u> Kelly walking rate after snack is 2 2/3 miles per hour.
Answer:
(a) P-value = 0.074, α = 0.05 do not reject H0
(b) P-value = 0.006, α = 0.001 do not reject H0
(c) P-value = 0.494, α = 0.05 do not reject H0
(d) P-value = 0.074, α = 0.10 reject H0
(e) P-value = 0.028, α = 0.01 do not reject H0
(f) P-value = 0.296, α = 0.10 do not reject H0
Step-by-step explanation:
The p-value is used to determine the statistical significance of the results of a statistical test. The p-value is the probability that the null hypothesis is correct. Smaller the p-value, higher is the probability that the alternate hypothesis is correct. On the other hand, the significance level (α) is the probability of rejecting the null hypothesis when it is true. It is the risk that you are willing to take in saying that there are differences between groups when there are not.
In order to reject the null hypothesis, the p-value should be lower than the significance level (α).
