Answer:
52.5°
Step-by-step explanation:
Given : The ray XZ is the angle bisector of ∠WXY and m∠WXY = 105°.
To Find : The measure of ∠WXZ is ?
Solution:
∠WXY = 105°
The ray XZ is the angle bisector of ∠WXY
This means XZ divides the ∠WXY in two equal angles i.e. ∠WXZ and ∠ZXY
So, ∠WXY = ∠WXZ + ∠ZXY
∠WXY = 2∠WXZ
Hence The measure of ∠WXZ is 52.5°
∠WXZ = 52.5°
∠WXY = 105°
and ∠WXY = ∠WXZ + ∠ZXY
∠WXZ = = 52.5°
Answer:
If a regular year, about 158,904. If a leap year, about 158,469
Step-by-step explanation:
for a regular year, 58,000,000 divided by 365 = 158,904
for a leap year, 58,000,000 divided by 366 = 158,469
Answer: 158,904
Step-by-step explanation:
58,000,000/365=158,904.10958904 which rounded is 158,904
(If the slope is undefined, enter "DNE")
Answer:
15 / 17
Step-by-step explanation:
slope = (y2 - y1) / (x2 - x1)
= (-9 + 24) / (8 + 9)
= 15 / 17
Answer:
hello your question has some missing parts below is the missing part
Given below are the analysis of variance results from a Minitab display. Assume that you want to use a 0.05 significance level in testing the null hypothesis that the different samples come from populations with the same mean.
Identify the p-value.
Source DF SS MS F p
Factor 3 13.500 4.500 5.17 0.011
Error 16 13.925 0.870
Total 19 27.425
A) 0.011 B) 4.500 C) 5.17 D) 0.870
answer : p-value = 0.011 ( A )
Step-by-step explanation:
using this information
Source DF SS MS F P
Factor 3 13.500 4.500 5.17 0.011
Error 16 13.925 0.870
Total 19 27.425
significance level = 0.05
given that the significance level = 0.05
and
F statistics are given as : F = 5.17 , F critical = 3.25
hence the p-value = 0.011
from the analysis the p-value is less than the significance level is lower than the significance level
The p-value in a Minitab analysis of variance (ANOVA) test helps determine whether to reject or accept the null hypothesis that the samples all come from populations with the same mean. You would reject the null hypothesis if your p-value is less than the significance level (α = 0.05). Please refer back to your Minitab results to find this p-value.
In the context of your Minitab analysis of variance (ANOVA) results, the p-value that you should be looking at to determine the null hypothesis is not explicitly mentioned in your question. However, based on your description, you want to test the hypothesis that the different samples come from populations with the same mean (null hypothesis).
The p-value represents the probability that you would obtain your observed data (or data more extreme) if the null hypothesis were true. Therefore, if the p-value is less than the significance level (α = 0.05), you would reject the null hypothesis, suggesting that the samples do not all come from populations with the same mean. Conversely, if the p-value is larger than 0.05, you would fail to reject the null hypothesis, suggesting that the samples could come from populations with the same mean.
Please refer back to your Minitab results to find this p-value. Usually, it's labeled in the ANOVA table output as 'P' or 'Prob > F'.
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Answer:
Step-by-step explanation:
Given line:
Convert the equation into slope-intercept form:
It has a slope of 1/4.
Parallel lines have equal slopes.
Find the parallel line lines that passes through the point (2, - 5):
Substitute x and y values to work out the value of b:
The line is:
Covert this into standard form:
Isolate y
Equation of line in point slope form
(b) How many bacteria are there after t hours?
(c) How many bacteria are there after 40 minutes? (Round your answer to the nearest whole number.)
(d) Graph the population function and estimate the time for the population to reach 40,000. (Give your answer to the nearest tenth.)
This problem involves calculating a bacteria culture's exponential growth over time. After 4 hours, there are 230,400 bacteria; after t hours, the formula 900*2^(2t) can be used to find the population. After 40 minutes, there are approximately 2,165 bacteria. The population reaches 40,000 after about 4.4 hours.
This problem is an example of exponential growth, which can be described by the equation N = N0 * 2^(t/T), where N is the final population, N0 is the initial population, t is the time elapsed, and T is the doubling time.
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