Answer:
Step-by-step explanation:
Using either the critical value rule or the p-value rule, a conclusion can be drawn at a level of significance (alpha)
The null hypothesis: u = hypothesized mean
Alternative hypothesis: u > u0 or u < u0 for a one tailed test
Alternative hypothesis for a two tailed test: u =/ u0
To draw a conclusion by failing to reject the null hypothesis as stated then: using critical value
Observed z score > critical z score for both the one and two tailed test.
Or using p value:
P-value > alpha for a one tailed test
P-value > alpha/2 for a two tailed test
Thus, if a one-sided null hypothesis for a single mean cannot be rejected at a given significance level, then the corresponding two-sided null hypothesis will also not be rejected at the same significance level.
30 is the smallest for both
Answer:
The percentage of people that could be expected to score the same as Matthew or higher on this scale is:
= 93.3%.
Step-by-step explanation:
a) Data and Calculations:
Mean score on the scale, μ = 50
Distribution's standard deviation, σ = 10
Matthew scores, x = 65
Calculating the Z-score:
Z-score = (x – μ) / σ
= (65-50)/10
= 1.5
The probability based on a Z-score of 1.5 is 0.93319
Therefore, the percentage of people that could be expected to score the same as Matthew or higher on this scale is 93.3%.
Answer:
2x² + 3x - 1 - 5/(5x +2)
Step-by-step explanation:
2x² + 3x - 1
__________________
5x + 2 | 10x³ + 19x² + x - 7
| -<u>10x³ - 4x² </u>
| 15x² + x
| <u>- 15x² - 6x </u>
| - 5x - 7
| <u> 5x +2</u>
| - 5
2x² + 3x - 1 - 5/(5x +2)
Answer:
I think its A. vertical stretch 2 and up 3
Step-by-step explanation:
Sorry if not correct! :)
