Statistical Analyst Essay

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.

Body composition provides an overall assessment of health and it is important in assessing an individual’s risk of developing diseases such as diabetes, high blood, obesity and some forms of cancer. Body composition indicates the proportion of essential fats and non-essential fats in the body (ACSM, 2012). An optimal health range has been established based on the difference in age, sex, height and weight. Individuals above the established range are at increased risk of developing high blood pressure, diabetes and obesity (ACSM, 2012). While, individuals below the established range are more likely to interrupt the delivery of key vitamins and nutrients to the cells of the body as well as adversely affect the proper functioning of their reproductive

Body weight expressed as a percentage of ideal in an SRS of n = 9 girls selected at random are as follows {114, 100, 104, 94, 114, 105, 103, 105, 96}. Calculate a 95% confidence interval for the population mean µ.

The critical value for significance level α=0.05 for an upper-tailed z-test is given as 1.645.

7. This practice problem uses the data contained in the file named Ch. 3 Data Set 3. There are two variables in this data set. The data sets can be found through the Sage Materials in the Student Textbook Resource Access link, listed under Academic Resources. Using IBM® SPSS® software, compute all of the measures of variability you can for height and weight. Copy and paste the output from IBM® SPSS® into this worksheet.

The critical value for significance level, α=0.05 for a lower-tailed z-test is given as 1.645.

The summary includes variance, mean, median, mode and standard deviation. As shown in the histogram majority of people in the data pool have a height of 62-68 inches. This is a symmetrical distribution seeing how close the mean and median are to each other.

Our experiment was performed in the Science and Technology build on the USCB Bluffton Campus on October 6th from 4:30 to 5:30 p.m. In order to construct the study for this experiment, we divided the actual experiment work in order to be able to complete the study for all sixteen (16) subjects within the given time period available to accommodate the test subjects. The experiments was conducted in the hallway, during the process we tested three participants at a time. We constructed an informational form regarding gender, age, weight amount of exercise per week. The supplies needed were approximately 25 test participants for the class, informational form, a blood pressure reader, timer, 15 jumping jacks, three chairs, paper and pencil to record

By using the mean, median, and mode of the results recorded, I believe women and men will be alike in ways people did not expect. By looking at the content of fat and muscle composition on the calf, weight of a female/male, and the ability to vertically jump, evidence will be provided that physical activity does not solely rely on the gender of a person. If the circumference of any persons’ calf is high due to fat then their ability to jump higher will decrease because of lack of muscle and/or lack of physical activity. Comparing men and women though, research leads to evidence that men can jump higher than women. If a man and a woman’s were compared, a man will be able to jump higher due to higher testosterone, activity of a male, and the muscle composition of a male.

Statistical conclusion validity signifies that two variables are related and the strength of that correlation (Polit & Beck, 2017). The fitness indicator utilized in this study are the “body mass index (BMI), grip strength, flexibility, abdominal muscle durability, back muscle durability and cardiopulmonary durability” (Yuan et al., 2009, p. 1405). In regards to the statistical significance, the participant sample size is small to make any difference, normally to make any significance in power analysis, there has to be a large sample size to make any significance difference between group scores and to achieve statistical significance. External validity infers that relationship amongst the variables investigated will hold over different individuals, times, settings, measurement variables, and treatment variables (Polit & Beck, 2017). In regards to the study, we can see that the in control group there was only forty-one participants that completed the study, arrays of elements can influence the need for physical exercises or otherwise; because there are other ways to live healthy without rigorous physical exercise activity such as walking, healthy diet etc. Construct validity is essentially one way of testing the validity of a test when hypothetical constructs of cause and effect

Evi’s mean amount of sugar daily was 124.97 grams, and Izzy’s was 28.71 grams. The recommended amount of sugar daily for us is approximately 75 grams, including natural and added sugars. Evi’s sugar intake was very high most days because she included a lot of fruit in her diet that had natural sugars, which greatly increased her sugar intake. Izzy, on the other hand, had little to no fruit in her diet, therefore had very low sugar amounts. Also, because Evi had a daily intake of fruit, heer sugar amounts were pretty even throughout the whole week. Izzy did not have foods with primarily sugary properties all throughout the week, so her data had extreme highs and lows, such as 1 gram of sugar one day, and 50 grams another day. Izzy had a standard deviation of 18.16, and Evi had a standard deviation of 26.51. This shows that even though Izzy had varied data in relation to the scale of her data collection, Evi’s larger amounts of sugar caused her to have a bigger range and more varied data, which is why she had a higher standard deviation. The visual representation of

The data can be put into a frequency distribution and it will be clear both from the numbers and the bar chart if the numbers show a symmetrical distribution or positive or negative skew. The mean for the data set is 18.9, the median is 19.0, and the mode 19.0. The range is 43.2 and the standard deviation is roughly 7.669. These numbers were calculated from the body fat column of the data since these are the numbers that will help us decide whether the claim about men having a body fat average of 20% is accurate or not. The bar chart of men's body fat at the end of the paper represents the distribution of body fat among the 252 men. The numbers were collected by going in increments. Body fat from 0 to 4.9 and 5.0 to 9.9 and so on, going in increments of 5, and all the way up to 40. The outliers in the distribution are 0 to 5 and 35 to 40 percent body fat. The overall look of the distribution looks symmetrical but due to the higher 10 to 15 column it might be able to be argued that the data is skewed to the left. For the purpose of this analysis we will continue to assume that it is symmetrical until further

Across the duration of Professor Van Oosbree’s Exercise Physiology class we had multiple labs which involved testing and the observations of our individual performance. Our first lab was a few weeks in September which was the test of our Muscular Strength. We had various techniques as warm ups to determine our one repetition maximum which was followed by multiple lifting exercises such as bench press, leg curls, and grip strength. On the bench press and leg curl I classified under “Fair” which was listed 4th out of 6 different classifications. Although I received a Fair ranking, out of my whole class I was right in the middle and that was due to the female’s results. Part of this is due to the statistics showing that men have significantly larger type 1 fiber areas and mean fiber areas than women. Secondly, Flexibility was next on the agenda. During this lab we performed several test which included modified sit and reach, shoulder rotation, zipper test, and range of motion. This lab I particularly enjoyed as I classified under Good and Excellent in all the techniques along with a passable range of motion in my joints that were tested. What possibly helped me excel in that flexibility lab could of been the same factor that hurt me in the muscular strength. Lifting weights without the proper stretching activities can limit your mobility and create stiffness. One of the more recent labs and quite possibly my least favorite was determination of our body composition. Now prior to

If I was analyzing the results of an exercise machine that provided an average 10 pound weight loss result, I would seek several pieces of information. First, I would want to know the number of the population surveyed. I would also seek information regarding those who implemented a change in diet along with the use of the exercise machine. In order to lose weight, one must burn more calories than they take in (iVillage, 2004). Thus, one must implement a healthy diet to increase the probability of weight loss. I would need to know the time frame for which the loss occurred. Did the 10 pound loss happen in a weeks or months? Verification of actual exercise machine usage should be a required element for reference. The population represented within

All subjects were almost from the same socio economic group and were found to be physically fit for the type of programme they were selected. The subjects were divided into two groups (experimental group and control group) at random by drawing the lots. The age of these subjects range between 40 to 55 years. All of them were taking part in routine physical activity programme as per the classes of the school.