Physiology Intro to Stats

So, we should reject the null hypothesis H0. At a 0.05 level of significance level, we conclude that there is a significant difference between the average height for females and the average height for the males.

Using a measuring tape on the wall each member of the lab group separately stood with either side against the tape and extended the arm beside the wall as high as possible. This height was recorded in centimeters. Next each lab member separately applied chalk to their fingertips, crouched down beside the tape on the wall, and jumped as high as they could while hitting the measuring tape at their highest point. This measurement was again recorded in centimeters. Then the height the lab member’s arm extended while standing was subtracted from the height when they jumped. This number denoted the vertical height jumped. For the jump height measurements a scatter plot was constructed. In order to do this, the data was again entered into excel, highlighted, insert chart and the first scatter plot choice was chosen. It was then that it was necessary to edit our axes and other parts of the graph. The x-axis was right-clicked and format axis was selected and fixed was selected and 20.0 was entered into the blank. Following this, a trend line was added to the graph by right clicking on a data point. A drop down popped up, and from there add trend line was selected from the choices.

The null hypothesis was that the female and male shoe sizes have an equal mean while the alternative hypothesis was that female and male shoe sizes do not have an equal mean. With the degrees of freedom being 33, the t-statistic is -8.27. The probability that -8.27 is ≤-1.69 is 7.5×10-10 for the one-tailed test. Also, the probability that -8.27 is ≤ ±2.03. is 1.5×10-9 for the two-tailed test. Due to both probabilities being under the alpha value of 0.05, the null hypothesis is rejected, and the alternative hypothesis is accepted at the 95% confidence level.

The H0 Should not be rejected because the runner pilse is slower than the one from other athletes.

The same must be said for the separation of boys and girls in activities such as sports, boy/girl scout groups, and schools. Although, it is true that men have more muscle mass than women, this does not greatly affect a woman's performance in sports and other activities. Furthermore, some boys have less muscle mass than other boys so this logic does not hold up. Men and boys are encouraged to work out while women and girls are encouraged to get smaller while not building muscle mass because of the body image issues presented by media and society. This means that the reason behind the smaller percentage of muscle mass in women is not entirely contributed to biological factors but societal factors too.

Running, it is the simplest of movements: right foot, left foot, right foot, and the simplest of actions: run, relax, and breathe. Many individuals participate in this form of exercise because they enjoy how good it makes them feel, they enjoy feeling the strength of their body while they run, and they enjoy using it as a way of relieving their stress. There are many other factors to take into consideration that effect a persons running such as, the type of footwear they are using, the distance they are running, and the speed of which they are running at. The objective of the main article was to research and compare the differences in stride length, hip, knee and ankle angles in runners when running two different conditions, shod and barefoot, also while running at two

As a result of these findings it was clear there was some kind of relationship between height and PEFR. To test this, data was taken from a class of biology students. PEFR values before and after exercise, their smoker status, age, gender, height, weight as well as other relevant information were recorded and narrowed down to a particular group of people to compare their PEFR values and height to determine whether there was in fact a positive linear relationship between height and PEFR. Using the information from previous studies a hypothesis was

because a males average weight is 10 ft and 800 pounds. A female can go way

Jumping is a movement which is fundamental to a vast majority of sports (Grimshaw et.al 2006). The goal of a jump may be to try and achieve the longest or highest jump possible, as in long jump and high jump; or they may be performed to intercept the opposition for example a header in football or defensive block in basketball (Matavulj et.al, 2001) (Juarez et.al, 2011). An example of a jump is a counter-movement jump as defined by (Marshall & Moran 2013) as being a form of vertical jump which involves a preparatory movement downwards before a forceful extension of the hip, knee and ankle moves the body upwards into the concentric phase. A jump which can be related to both vertical jump at toe-off (concentric) and counter-movement jump at the landing phase (eccentric) is a drop jump.

The t‑test for differences between two means has been a standard tool for more than a century, since William Gosset introduced the method in 1908 (Gosset (Student) 1908). We use the t‑test to infer from a sample a range of values for the true mean of the population from which the sample was drawn. When comparing two independent samples we use a t‑test to decide whether the averages of the two groups are probably different by testing whether the difference in means is sufficiently different from zero. Typically we conclude that if the ratio of the estimated pooled standard error to the estimated mean difference is greater than a value determined by the sample size (usually something around 2) then there is a “significant” difference – a t‑test. Alternatively, we look up the p‑value to learn what the odds are of finding such a mean and variance purely as a result of random error in sampling. If the p‑value is less than 0.05 then we conclude that the observed difference in means is sufficiently unlikely to be zero.

The most immediate difference is in the ratio of muscle mass to fat mass between males and females. On average, the fat percentage for males is lower than that of females, as the table below demonstrates.

The physical differences are rather obvious and most of these can be seen and easily measured such as Weight, shape, and size. There are many physical differences that are not noticeable. Men are 3o percent stronger especially in upper

Values are expressed as means ± standard Error. Means with similar superscript (a, b c, d) letters in columns indicate non-significant difference (P< 0.05).

To compare the various groups in this study, hypothesis was stated and tested at 0.05