Answer:
D)Yes, because the difference in the means in the actual experiment was more than two standard deviations from 0.
Step-by-step explanation:
We will test the hypothesis on the difference between means.
We have a sample 1 with mean M1=18.2 (drug group) and a sample 2 with mean M2=15.9 (no-drug group).
Then, the difference between means is:
If the standard deviation of the differences of the sample means of the two groups was 1.1 days, the t-statistic can be calculated as:
The critical value for a two tailed test with confidence of 95% (level of significance of 0.05) is t=z=1.96, assuming a large sample.
This is approximately 2 standards deviation (z=2).
The test statistict=2.09 is bigger than the critical value and lies in the rejection region, so the effect is significant. The null hypothesis would be rejected: the difference between means is significant.
Options :
A. The initial number of bacteria is 7.
B. The initial of bacteria decreases at a rate of 93% each day.
C. The number of bacteria increases at a rate of 7% each day.
D. The number of bacteria at the end of one day is 360.
Answer:
C. The number of bacteria increases at a rate of 7% each day.
Step-by-step explanation:
Given the function :
f(x)=360(1.07)^x ; Number of bacteria in sample at the end of x days :
The function above represents an exponential growth function :
With the general form ; Ab^x
Where A = initial amount ;
b = growth rate
x = time
For the function :
A = initial amount of bacteria = 360
b = growth rate = (1 + r) = 1.07
If ; (1 + r) = 1.07 ; we can solve for r to obtain the daily growth rate ;
1 + r = 1.07
r = 1.07 - 1
r = 0.07
r as a percentage ;
0.07 * 100% = 7%
120/4=30. Remember its widthxlength. if you forget ask your teacher or look online for more help. Good luck.
- For this study, we should use t-test and the null and alternative hypotheses would be given by H₀: μ = 7 and H₁: μ < 7.
- The test statistic is -1.941 and the p-value (0.0381) is <u>greater than</u> α = 0.01.
- Based on this, we should <u>fail to reject</u> the null hypothesis.
- Thus, the final conclusion is that the data suggest the population mean is not significantly lower than 7 at α = 0.01, so there is statistically insignificant evidence to conclude that the population mean waiting time to be admitted into the hospital from the emergency room for patients at rural hospitals is equal to 7 hours.
<h3>What is a null hypothesis?</h3>
A null hypothesis (H₀) can be defined the opposite of an alternate hypothesis (H₁) and it asserts that two (2) possibilities are the same.
<h3>How to calculate value of the test statistic?</h3>
The test statistics can be calculated by using this formula:
<u>Where:</u>
- is the standard deviation.
- n is the number of hours.
For this study, we should use t-test and the null and alternative hypotheses would be given by:
H₀: μ = 7
H₁: μ < 7
t = -0.7/0.3606
t = -1.941.
For the p-value, we have:
P-value = P(t < -1.9412)
P-value = 0.0381.
Therefore, the p-value (0.0381) is <u>greater than</u> α = 0.01. Based on this, we should <u>fail to reject</u> the null hypothesis.
Thus, the final conclusion is that the data suggest the population mean is not significantly lower than 7 at α = 0.01, so there is statistically insignificant evidence to conclude that the population mean waiting time to be admitted into the hospital from the emergency room for patients at rural hospitals is equal to 7 hours.
Read more on null hypothesis here: brainly.com/question/14913351
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