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أستاذ المادة جميلة علي عبد الصاحب الكريمي
15/05/2017 02:53:55
BIOSTATISTICS DESCRIBING DATA, THE NORMAL DISTRIBUTION SOLUTIONS 1. a. To calculate the mean, we just add up all 7 values, and divide by 7. In fancy statistical notation, 7 X X 7 i 1 i ?= = = 10.2 7 12.0 9.5 13.5 7.2 10.5 6.3 12.5 = + + + + + + years. b. To calculate the sample median, first rank the values from lowest to highest: 6.3 7.2 9.5 10.5 12.0 12.5 13.5 Since there are 7 values, an odd number, we can simply select the middle value, 10.5, to calculate the sample median. b. It’s a good thing we have calculated the sample mean we ned this to calculate the sample standard deviation! Recall the formula for SD: 6 (12.0 10.2) (9.5 10.2) ...............(12.5 10.2) 7 1 (X X) SD 2 2 2 7 i 1 2 i ? + ? + ? = ? ? = ?= = 2.71 years d. 1. sample mean – Would decrease, as the lowest value gets lower, pulling down the mean. 2. sample median – Would remain the same since the middle value is still 10.5 By replacing the 6.3 with 1.5, the rank of the 7 values is not affected. 3. sample standard deviation – Would increase. Because our minimum value has now gotten smaller, while the rest of the data points remain unchanged, the spread or variability in our data has increased; since SD is a measure of spread, it too will increase (prove it to yourself!). e. While the sample mean and sample standard deviations of the 14 observation will likely be different than the respective quantities from the sample with seven observations, it is not possible to predict how the values will differ (at least without seeing the data!) as neither the sample mean nor the sample mean values are linked explicitly to sample size. Recall, these sample quantities are estimating the same underlying population parameters whether they are computed from a sample of size 7, 14, or 1,000. In this example, the sample mean of the 14 observations is 9.9 years, smaller than the sample mean of 10.5 years for the original seven observations. The sample standard deviation of the 14 observations is 3.1 years, larger than the sample standard deviation of 2.7 years for the original seven observations. 2. This question is really about is calculating standard normal scores. Recall, SD Z Observed Mean ? = a. The boy who is 170 cm tall is above average by 3 8 24 8 170 146 = = ? SDs. b. The boy who is 148 cm tall is above average by .25 8 2 8 148 146 = = ? SDs. c. A third boy was 1.5 SDs below the average height. He was 146 – 1.5*8 = 14612 = 134 cm tall. d. If a boy was within 2.25 SD’s of average height, the shortest he could be is 146 – 2.25*8 = 128 cm tall, and the tallest he could be is 146 + 2.25*8 = 164 cm tall. e. 1. 150 cm – about average (.5 SDs above mean) 2. 130 cm  unusually short (2 SDs below mean) 3. 165 cm –unusually tall (2.4 SDs above mean) 4. 140 cm – about average (.75 SDs below mean) 3. These questions refer to the table relating normal scores to area (percent population) under the density curve. a. If individuals considered “abnormal” have glucose levels outside of 1 standard deviation of the mean (above or below) , then approximately 32% (31.73 to be exact) of the individuals will need to be retested. The “normal range” of glucose level would range from (90 – 38) mg/dL to (90 + 38) mg/dL, or from 52 mg/dL to 128 mg/dL. b. If individuals considered “abnormal” have glucose levels outside of 2 standard deviations of the mean (above or below) , then approximately 5% (4.55 to be exact of the individuals will need to be retested. The “normal range” of glucose levels would range from (90 – 2*38) mg/dL to (90 + 2*38) mg/dL, or from 14 mg/dL to 166 mg/dL. 4. A is the correct answer. Remember, in order to calculate the median, you must first order the values in the sample from lowest to highest. Doing so yields: 110 116 124 132 168 This sample is of size 5, and odd number, so the middle value of 124 is the sample median. 5. C is the correct answer. Here the sample mean, X = 64 inches, and the SD = 5 inches. Since we are given that the distribution of heights in 12 year old boys is normal, we know that 2 SDs above or below the sample mean will give us an interval containing approximately 95% of the heights in the sample. This interval would run from 64 – 2*5 to 64 + 2*5, or 54 inches to 74 inches. Within Z SDs of the mean More than Z SDs above the mean More than Z SDs above or below the mean Z 1.0 2.0 2.5 3.0 68.27% 95.45% 98.76% 99.73% 15.87% 2.28% 0.62% 0.13% 31.73% 4.55% 1.24% 0.27% 8. D is the correct answer. Remember, whether we calculate sample SD from a sample of 1,000 or a sample of 3,000, both are estimating the same quantity the population standard deviation. These two estimates should be about the same, and we cannot predict which will be larger. BIOSTATISTICS SAMPLING DISTRIBUTIONS, CONFIDENCE INTERVALS SOLUTIONS QUESTION 1. a. It can not be determined which researcher will get the bigger standard deviation – both sample SDs from the sample with n = 100, and with n = 1,000 are estimating the same quantity – the population standard deviation. Therefore, the two estimates should be similar, and it is not possible to tell which will be larger , prior to calculating the values. Standard deviation does not depend on sample size, but will vary from random sample to random sample. b. Standard error does depends on sample size, however; the larger the sample size, the smaller the standard error of the mean (SEM). Therefore, the SEM calculated from the sample with n = 1,000 will likely be smaller the SEM calculated from the sample with n = 100. c. Extreme values are more likely in larger samples – therefore, the investigator with the sample of n = 1,000 is more likely to have the tallest man. d. Extreme values are more likely in larger samples – therefore, the investigator with the sample of n = 1,000 is more likely to have the shortest man. QUESTION 2. a. In this study of 60 year old women with glaucoma, n = 200, X=140 mmHg, and SD = 25mm Hg. Since n is large, we can use the Central Limit Theorem to aid us in constructing a 95% confidence interval for the population mean blood pressure, ?. Its “business as usual” via the formula: X±2*(SEM), where SEM = n SD = 200 25 = 1.77 mm Hg Plugging in our sample values gives us: 140 ±2*(1.77) ?? (136.5 mm Hg, 143.5 mmHg) b. If a second study yielded the same sample statistic values, but were done with 100 women, what would happen to the width of the 95% confidence interval? Well, we know since this sample is smaller than the previous example, the SEM will be larger, leading to a wider confidence interval. In nonmathematical terms, our sample contains less information than a sample of 200 women, and therefore will yield a less precise (more uncertain) estimate of the population mean. The proof is as follows: X±2*(SEM), where SEM = n SD = 100 25 = 2.5 mm Hg Plugging in our sample values gives us: 140 ±2*(2.5) ??(135 mm Hg, 145 mm Hg) 3. A is the correct answer. Here the sample is of size n = 500, which is large enough to ensure that the Central Limit Theorem kicks in . By the Central Limit theorem, the sampling distribution the of the sample mean from a sample of 500 will be normally distributed. 4. D is the correct answer. No general statement can be made as we do not know whether or not the sample of 200 women who agreed to participate from the original random sample of 300 was still representative of all 18 year old females. If these 200 women are inherently different from the other 100 nonparticipants, the results shown are biased. 5. B is the correct answer. The more confident we want to be, the wider our confidence interval. Ninetynine percent confidence is higher than ninetyfive percent confidence; therefore the 95% confidence interval is not so wide as the 99% confidence interval. 6. C is the correct answer. The sample is random, i.e. representative – therefore, the sample distribution should mimic the larger population distribtion, which is rightskewed. 7. B is the correct answer. We would expect the two samples to have SD values that are similar. but, recall that the standard error (SE) is the standard deviation divided by the squareroot of the sample size. Because Sample B is much larger (N=2000) than Sample A (N=100), we would then expect the SE of Sample B to be smaller than the SE of Sample A. 8. A is the correct answer. This question is asking about the shape of the sampling distribution of the sample mean, based on samples of size 100: As the sample size is large (n=100) the Central Limit Theorem applies and the sampling distribution should be normal: hence a histogram based on the sample means of 3,000 random samples should be approximately normal : note it is not the number of samples that determines whether the Central Limit Theorem “kicks in “ but the size of each of the samples. 9. B is the correct answer. A very straightforward application of the formula x ± 2SE(x)  you are given sample s.d. of 25 ounces, and know that the sample size is 100 – the estimated standard error of the sample mean is 2.5. 10 25 100 = 25 = = n s all you need do is plug in: x ± 2SE(x) = 120±2(2.5) = 120±5 = (115, 125). 10. The correct answer is C. In this sample, pˆ , the estimated proportion of Baltimoreans with health insurance, is .65 1000 650 = , or 59%. As 1000*.65*(1.65)?228, we can use the normal approximation for the 95% CI for a population proportion, given info from a random sample. The standard error of this estimate is .015 1000 (.65)(1 .65) ? ? Applying the formula pˆ ± 2SE(pˆ ) , yields as 95% confidence interval of (.62,.68), or 62% to 68% for the proportions of Baltimoreans with health insurance. BIOSTATISTICS HYPOTHESIS TESTING SOLUTIONS QUESTION 1. (answers will vary, of course) A sample of 107 patients with onevessel coronary artery disease was given percutaneous transluminal coronary angioplasty (PTCA). Patients were given exercise tests at baseline and after 6 months of follow up. Exercise tests were performed up to maximal effort until symptoms, such as angina, were present. A paired ttest was used to assess whether there was a significant change in duration of exercise after 6 months of PTCA treatment, and a 95% confidence interval was constructed for the mean difference (after  before) in exercise duration. Exercise duration increased 2.1 minutes (95% CI 1.5 – 2.7 minutes) on average after the PTCA treatment. There was evidence that exercise was significantly higher after exposure to 6 months of PTCA treatment (p < .001). As there was no comparison group of individuals not receiving PTCA, we cannot prove PTCA as the cause of this increase in exercise duration. It is not known whether there would have been a similar 6month change without PTCA. QUESTION 2.(answers will vary, of course) A sample of 171 women between 75 and 80 years old were classified into one of two groups based on whether the subject took Vitamin E supplements at the time of enrollment. Each woman was subsequently given a test to measure cognitive ability. Higher scores on this test indicate better cognition. A two sample ttest was used to compare mean cognition test scores between the two groups of women, and a 95% confidence interval for the difference was constructed. The average test score amongst the women taking vitamin E was 27 (sd = 6.9) as compared to a mean score of 24 (sd = 6.2) for women not taking the supplements. The average score difference between the two groups is 3.0 points (95% CI 1.0 – 5.0 points). The cognition scores were statistically significantly the women taking Vitamin E supplements. (p < .01) As the women were randomized to the vitamin and placebo groups, the results of this study strongly suggest a positive relationship between Vitamin E consumption and increased cognition amongst elderly women. If the study was not randomized, and women selfselected to be in the Vitamin E group, the statistical comparisons could still be made, but the scientific conclusions would be harder to make without further analyses (the type of which is coming in 612!). The writeup of the results would be similar, but the last sentence in the second paragraph in part 1 would change to something like the following paragraph: “However, because women were not randomized to take the vitamin supplements but were selfselected into the vitamin exposure groups, it is not possible to attribute the higher scores to Vitamin E. It is possible that the women taking Vitamin E differed on multiple factors when compared to the women who were not taking the supplement. The difference in test scores could be attributable, at least partially, to some of these other factors.” 3. The correct answer is C. Because the 95% confidence interval does not include zero, we would reject null hypothesis of a true mean difference of zero at the ? = .05 level. Testing Ho: ?2  ?1 = 0 is equivalent to testing Ho: ?2 = ?1, the equality of the two means. 4. The correct answer is A. The data collected in this example is paired data, and a pvalue would be obtained from the paired ttest. The test statistics would be: tan _ _ _ _ ( ) _ _ diff diff se X X s dard error of mean difference t = observed mean difference = where X= 15, and se( X) = 10 40 100 40 = = 4. So Z = 15/4 = 3.75. Since t > 2, we know p < .05 5. The correct answer is B. The standard error of a statistic is a measure of the variability of that statistic across different sample sizes – the variability of the sampling distribution. Therefore, the standard error of a statistic is the standard deviation of the sampling distribution. 6. B is the correct answer. Despite the fact that we are computing before/after differences we ultimately are comparing these differences between two independent groups: those randomized to the diet program, and those randomized to exercise. Since we are making a comparison of mean changes between two independent groups, the appropriate test is the 2 sample unpaired ttest. 7. The correct answer is E. This is a hard, but important question : choice a is just flatout incorrect, based on the definition of the pvalue, and choices bc are impossible to ascertain from just a pvalue, as it imparts no information about the direction/magnitude and clinical or scientific significance of the results of a study. 8. A is the correct answer. As the 95% confidence interval for the mean difference does not include, the resulting pvalue would be less than .05. 9. C is the correct answer. The chisquared is the correct statistical test for comparing two population proportions based on information from two (large) samples – both the sample meet the “large sample” criteria.
المادة المعروضة اعلاه هي مدخل الى المحاضرة المرفوعة بواسطة استاذ(ة) المادة . وقد تبدو لك غير متكاملة . حيث يضع استاذ المادة في بعض الاحيان فقط الجزء الاول من المحاضرة من اجل الاطلاع على ما ستقوم بتحميله لاحقا . في نظام التعليم الالكتروني نوفر هذه الخدمة لكي نبقيك على اطلاع حول محتوى الملف الذي ستقوم بتحميله .
