5. Which group's test scores had the least amount of variability or dispersion? Provide a rationale for your answer. Ans: The control group had the least amount of variability (SD 10.34) because the control group was not provided the same resources that the experimental group was provided. 6.
Alternatively, we reject the null hypothesis, if the p value is less than the significance level Substituting the value we get t = 43.74-5014.6396/50 = -3.02 The p value corresponding to t = -3.02 and 49 d.f. is 0.002 which is smaller than the significance level. The value of the test statistic is in the critical region and hence it is significant. Therefore, we reject the null hypothesis at 5% level of significance. The p-value is 0.002 which is smaller than the significance level.
The Shuttle run test has the weakest relationship with Quadriceps strength index 120°/s. That is because the absolute value of the r value is less than all the others, and the p value is greater than all others. 6. Which of the following sets of variables has the strongest relationship? a. Hamstring strength index 120°/s and the Hop index b. Quadriceps strength index 60°/s and
Describe the clinical importance of this relationship. variance = r^2 = (0.15)^2 = 0.0225 = 2.25% This is not considered clinically important since the variance is less than 9%. 6. Which two variables in Table 2, have the weakest correlation, or r value? Which relationship is the closest to this r value?
Q: Retail industry 26. What does the evidence in Panel A of Table 2 suggest about revenues, earnings and abnormal stock returns in 14-week quarters compared to 13-week quarters? Q: In Panel A of Table 2, the researchers contrast mean and median values of revenues, revenue forecast errors, earnings, earnings forecast errors and abnormal stock returns in 14-week quarters relative to all 13-week quarters. By comparison, all revenue metrics in 14-week quarters are higher, on average, than corresponding metrics in 13-week
In hypothesis testing, the smaller the p value the more important it is. It is usually compared with the level of significance value and if it is lower, the null hypothesis is rejected. If it is higher than the null hypothesis is not rejected. It is important to note that a very small p value is an indication that there is a greater chance of the null hypothesis being rejected.
Central tendency and spread were calculated for all variables. Chi- square tests and rank sum tests were specific to baseline and post intervention call light use between the two units. The fall rate before the intervention was 3.37 per 1,000 patient days. The fall rate post intervention was 2.6 per 1,000 patient days. The author noted that although the decrease was not statistically significant (p= 0.672), it was clinically significant at a 23% reduction in patient falls.
There is a very low probability (5% at most) that the annual income for the data from AJ DAVIS is less than $50,000. The way that we concluded this was to test the probability that the annual income of our customers is $50,000 versus the probability that the average annual income was less than $50,000. What we found was that there is a 95% chance that the average annual income of our customers is between $69,997.9 and $70,001.8. This was also backed with a p-value (which determines the strength of the evidence) that showed weak evidence against the average income equaling $50,000. Since we cannot deny that the annual income average is $50,000, we have no choice but to keep it as a consideration moving forward.
Even though the p-value for our Intercept and DAYS increased, it didn’t affect the significance of our model. Getting rid of PAYOR variable didn’t have much of a significant change on our PHYS p-value as it did for our Intercept and DAYS. The overall p-value changed from 2.73E-63 to 1.51E-64, however, the number is still low and there is still a low chance of inaccuracies in this model right now. In order to make this model more accurate, I am going to take PHYS out of the