Answer:
92 years old
Step-by-step explanation:
Multiply 23 by 4
92
So Morgan's grandfather is 92 years old
Hope this helps :)
Please consider Brainliest :)
Answer:
92
Step-by-step explanation:
23x4=92
0.67 = ?
7 is repeating
Answer:
61/90
Step-by-step explanation:
−5y=−5 THANKS
7x+6y=7
Is (5,1) a solution of the system?
Answer:
{x = 1/7,y = 1
Step-by-step explanation:
Solve the following system:
{-5 y = -5 | (equation 1)
{7 x + 6 y = 7 | (equation 2)
Swap equation 1 with equation 2:
{7 x + 6 y = 7 | (equation 1)
{0 x - 5 y = -5 | (equation 2)
Divide equation 2 by -5:
{7 x + 6 y = 7 | (equation 1)
{0 x+y = 1 | (equation 2)
Subtract 6 × (equation 2) from equation 1:
{7 x+0 y = 1 | (equation 1)
{0 x+y = 1 | (equation 2)
Divide equation 1 by 7:
{x+0 y = 1/7 | (equation 1)
{0 x+y = 1 | (equation 2)
Collect results: Answer: {x = 1/7,y = 1
Answer:
No
Step-by-step explanation:
width of the rectangle = b = x
length of the rectangle = l = 8 + 2x
Perimeter of the rectangle = 70cm
Also, perimeter of the rectangle = 2(l + b)
70 = 2[x + (8 + 2x)]
70 = 2(x + 8 + 2x)
70 = 2(3x + 8)
70 = 6x + 16
70 - 16 = 6x
54 = 6x
54/6 = x
9 = x
Therefore, b = x
b = 9cm
l = 8 + 2x
I = 8 + 2×9
I = 8 + 18
I = 26cm
a. If the sample variance is s^2=32 , are the data sufficient to conclude that the treatment has a significant effect using a two-tailed test with alpha=.05
b. If the sample variance is s^2=72 , are the data sufficient to conclude that the treatment has a significant effect using a two-tailed test with alpha=.05 ?
c. Comparing your answer for parts a and b, how does the variability of the scores in the sample influence the outcome of a hypothesis test?
A hypothesis test was conducted to evaluate the treatment's effect. For both variances, we failed to reject the null hypothesis, so we can't conclude that the treatment had a significant effect. The variability of scores plays a crucial role, as more variability makes it harder to identify a significant effect.
To determine if the treatment has a significant effect, we perform a hypothesis test using the sample mean (M), sample variance (s^2), and population mean (μ). The null hypothesis is that there's no effect from the treatment (μ=M), while the alternative hypothesis is that there is an effect (μ≠M).
a. For sample variance s^2=32, we can use the formula for the t score: t = (M - μ)/(s/√n) = (35 - 40)/(√32/√8) = -2.24. Based on a two-tailed t-distribution table, the critical t values for α=.05 and 7 degrees of freedom (n-1) are approximately -2.365 and 2.365. Our t value (-2.24) lies within this range, so we fail to reject the null hypothesis. We cannot conclude that the treatment has a significant effect.
b. Repeat the same process with sample variance s^2=72. The t value is now (35 - 40)/(√72/√8) = -1.48, again falling within the range of the critical t values. We can't conclude that the treatment has a significant effect.
c. As the variability (s^2) of the sample scores increases, it becomes more difficult to find a significant effect. Higher variability introduces more uncertainty, which can mask actual changes caused by the treatment.
#SPJ12
To evaluate the effect of a treatment using a two-tailed test with alpha = 0.05, we compare the calculated t-value to the critical t-value. The sample variance influences the outcome of the hypothesis test, with a larger variance leading to a wider critical region.
a. To test if the treatment has a significant effect, we will conduct a two-tailed hypothesis test using the t-distribution. The null hypothesis states that the treatment has no effect (μ = 40), while the alternative hypothesis states that the treatment has an effect (μ ≠ 40). With a sample size of 8, degrees of freedom (df) will be n-1 = 7. We will use the t-test formula to calculate the t-value, and compare it to the critical t-value from the t-table with α = 0.05/2 = 0.025. If the calculated t-value falls outside the critical region, we reject the null hypothesis and conclude that the treatment has a significant effect.
b. Similar to part a, we will conduct a two-tailed t-test using the same null and alternative hypotheses. With a sample size of 8, df = n-1 = 7. We will calculate the t-value using the sample mean, population mean, and sample variance. Comparing the calculated t-value to the critical t-value with α = 0.05/2 = 0.025, if the calculated t-value falls outside the critical region, we reject the null hypothesis and conclude that the treatment has a significant effect.
c. The variability of the scores in the sample, as indicated by the sample variance, influences the outcome of the hypothesis test. In both parts a and b, the sample variance is given. A larger sample variance (s^2 = 72 in part b) indicates more variability in the data, meaning the scores in the sample are more spread out. This leads to a larger t-value and a wider critical region. Therefore, it becomes easier to reject the null hypothesis and conclude that the treatment has a significant effect.
#SPJ11
Answer:
No correlation
Step-by-step explanation:
Hey there! :)
This has no correlation because all the points are spread out throughout the graph making no correlation.
Answer:
D no correlation
Step-by-step explanation:
too many scattered dot all over the place if its some going up down its NO CORRELATION!!!