Rajwinder EXCERCISE 23 AND 24 Essay

After performing the test, a chi square value of 126.58 was determined. With a degrees of freedom value of 4, this constituted a p value of 2.101644 x 10-26. With an alpha level of .01, the null hypothesis was rejected.

Testing allows the p-value that represents the probability showing that results are unlikely to occur by chance. A p-value of 5% or lower is statistically significant. The p value helps in minimizing Type I or Type II errors in the dataset that can often occur when the p value is more than the significance level. The p value can help in stopping the positive and negative correlation between the dataset to reject the null hypothesis and to determine if there is statistical significance in the hypothesis. Understanding the p value is very important in helping researchers to determine the significance of the effect of their experiment and variables for other researchers

Information about statistical significance and confidence interval is presented and reviewed. There was good use of tables and figures that included titles and headings that were clearly and appropriately labeled. The results were also clearly displayed in tables with identifiable titles and labeled headings. The study included descriptive statistics. The study described the main characteristics in the dataset. The mean and standard deviation for each blood pressure measurement was calculated before and after crossing of the legs was performed by the study subjects. Inferential statistics were also present in this study. In order to test mean differences with three or more groups, an analysis of variance (ANOVA) statistical test is used. This research study conducted a repeated-measure ANOVA, which is when there are three or more measures of the same dependent variable

Since the P-value (0.386) is greater than the significance level (0.05), we fail to reject the null hypothesis. The p-value implies the probability of rejecting a true null hypothesis.

P-value represents a decimal between 1.0 to below .01. Unfortunately, the level of commonly accepted p-value is 0.05. The level of frequency of P>0.05 means that there is one in twenty chance that the whole study is just accidental. In other words, that there is one in twenty chance that a result may be positive in spite of having no actual relationship. This value is an estimate of the probability that the result has occurred by statistical accident. Thus, a small value of P represents a high level of statistical significance and vice

a.) The correlation coefficient for this data set is 0.4669 or 46.69% which shows that this weak but positive correlation indicates that as the years from 1960 increase, the average number of home runs increase at a constant rate.

Manual muscle testing will be tested to assess the strength in muscles used for running such as the prime/strong muscles which are the hamstrings, gastrocnemius, soleus, quadriceps, and tibialis anterior. This will also determine the weakness in muscles such as the plantarflexors, dorsiflexors, knee flexion, and knee extension. Also, range of motion will be tested on the tight muscles such as the hamstrings, gastrocnemius, quadriceps, and tibialis anterior. When the muscles are tight, the runner will increase the risk of injury (Schipper, 2009). Gait analysis will be used to examine deficits to body function, stability, and describe how the patient will be running. This will help determine the patient’s biomechanics and achieve adequate mobility. Lastly, we will perform a pain assessment to see what pain level the patient is at when running. This will help determine whether the patient is minimizing stress on the body to achieve no pain at all while running. To monitor progress of these assessments, we must know that if these “muscles are weak or become fatigued easily, there is less control of the leg and the risk of injury increases" (Schipper, 2009, paragraph

The differing results are likely because of differences in skill level among test subjects, methods of evaluation, and erratic sprint distances. The main strength qualities being explored in this data analysis are: absolute strength, relative strength, high-load speed strength, and low-load speed strength. Every sport requires several combinations of these strength qualities to be successful (13). Absolute strength is the measurement of how much force a subject can exert with no regard to body size (3). Relative strength is the measurement of strength to body size. High-load speed strength is the ability for the muscle to exert a high amount of force while contracting at a high speed. The tests to gauge high-load speed strength are typically short in nature, executed at maximal speeds, and produce high power outputs, and include exercises such as the power clean, snatch, and push jerk (1). Triple extension of the hips, knees, and ankles during these movements allows the athletes to express force against the ground rapidly. Low-load speed strength involves low velocity movements, and is a quality that “reflects the dynamic abilities necessary for sport. They are the maximal strength tests of choice for strength and conditioning professionals”

With a P-value of 0.00, we have a strong level of significance. No additional information is needed to ensure that the data given is accurate.

They defined one independent variable as group, referring to either healthy control or GH injury. The other independent variable was defined as functional exercise, which consisted of bow and arrow (BA), external rotation with scapular squeeze (ERSS), lawnmower, and robbery. The dependent variables were identified as normalized muscle-activation from the serratus anterior (SA), upper trapezius (UT), middle trapezius (MT), and lower trapezius (LT). These values were used to calculate ratios of muscle-activation (Moeller, Huxel Bliven, & Snyder Valier,

Results: Obtaining a R2 value of 0.042592 means there is no relationship between calf circumference and jump height. One cannot determine the jump height by having the calf circumference.

The higher R2 of 0.9652 indicates that the model is a good fit. It indicates that the model explains 96.52% of the variability of data around the mean. It implies that these data is close to the mean.

They did a test with people with broken arms. Half of the people worked out their good arm and the other half did not. After the cast was removing the ones who didn’t work out their bad arms lost more mass and had a longer recovery time then the people who did work their opposite arm. Another, study to prove if cross education training. The test was given to 39 right hand dominant females. They were scattered into a group that consisted of left-hand, right-handed training, and a control group. These strength training was a six-week camp. The women in the left hand training group strengthened there left hand and slightly their right hand. But the women in the right hand training group had a greater increased training in there left hand. There was also an interview on Project Acceleration it was a collegiate runner named Jayme Stanek. During the interview Jayme Stanek explained that she has torn her ACL throughout her career a number of three times. She explained that during her two ACL tears she was out for four and waited one whole year to come back to track. But on her third tear she rehabbed using cross education training and she cut down the time of recovery and she also retained her muscle mass and strength on her injured leg. She also explained that by using this therapy has strengthened her leg so she could go back to track. She finished her track year without any more ACL tears and explains that she believes that by doing this technique benefited her for not retearing her ACL

Table 2 shows the mean and standard deviation of coefficient of friction (COF), walking speed (WS), stride length (SL), stride duration (SD) and cadence in 6 different conditions of the test.

The results shown above support the hypothesis. The temperature behind the knee was measured, before the actual experiment commenced. The thermometer read 33.7 °C. A session of exercise was undertaken, in this case high knees. Each session was 30 seconds long. As each block of exercise was completed, the results displayed a gradual decrease in the amount of high knees completed. Therefore, this meant that the body was overcoming muscle fatigue and was