No Variation in a Sample An experiment was conducted to test the effects of alcohol. Re-searchers measured the breath alcohol levels for a treatment group of people who drank ethanol and another group given a placebo. The results are given below (based on data from “Effects of Alcohol Intoxication on Risk Taking, Strategy, and Error Rate in Visuomotor Performance,” by Streufert et al.. Journal of Applied Psychology, Vol. 77, No. 4). Use a 0.05 significance level to test the claim that the two sample groups come from populations with the same mean.
Treatment Group: n1 = 22,
Placebo Group: n2 = 22,
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Elementary Statistics (13th Edition)
- Sociodemographic differences in lung cancer worry. Hahn (2017) evaluated sociodemographic differences in how people worry about lung cancer. Some of the differences observed across demographic of interest were between males and females [t[45]=0.69; higher mean worry among men], smokers and nonsmokers [t[45]=2.69; higher mean worry among smokers]. However, at least one of these results were not statistically significant. Use the information provided to state which t test or t test did not reach the .05 level of significance in this study. (a) Compute the proportion of variance using omega-squared. (b) Suppose the pooled sample standard deviation for this test is 0.74. Using this value, compute estimated Cohen's d.arrow_forwardPassive exposure to environmental tobacco smoke has been associated with growth suppression and an increased frequency of respiratory tract infections in normal children. Is this association more pronounced in children with cystic fibrosis? To answer this question, 43 children (18 girls and 25 boys) attending a 2-week summer camp for cystic fibrosis patients were studied (New England Journal of Medicine, Sept. 20, 1990). Among several variables measured were the child's weight percentile (y) and the number of cigarettes smoked per day in the child's home (x). a. For the 18 girls, the coefficient of correlation between y and x was reported as r = –.50. Interpret - this result. b. Refer to part a. The p-value for testing Ho: p = 0 against H;: p # 0 was reported as p = .03. n Interpret this result. c. For the 25 boys, the coefficient of correlation between y and x was reported asr = -.12. Interpret %3D %3D this result. d. Refer to part c. The p-value for testing Ho: p = 0 against Ha: p #…arrow_forwardA recent study investigated whether the number of positive health behaviors (e.g., exercising, eating healthy) is predictive of a person's score on a happiness index, such that as positive health behaviors go up, happiness goes up. The results from the study, which was conducted with 12 people, were as follows: r = 0.63 and SSY = 56.0. What is the MSregression? What is the MSresidual? What is the calculated test statistic? Given the value of your calculated test statistic and your critical test statistic, do you reject the null?arrow_forward
- Ost watched Ani... Question 2 Y Part 1 of 4 A doctor in Cleveland wants to know whether the average life span for heart disease patients at four hospitals in the city differ. The data below represents the life span, in years, of heart disease patients from each hospital. Perform an ANOVA test with a 9% level of significance to test whether the average life span of heart disease patients in Cleveland differs depending on the hospital that treats them Life Span of Patients Treated at Hospital 1: 7.4, 7.8, 7.7, 7.5, 8, 8.2, 7.8, 8.6, 8, 7.8, 8.3, 8.3, 8, 7.6, 8.2, 7.9, 7.3, 8, 8.6, 7.3, 8.3, 8, 7.8, 8, 7.8, 8.1, 8.1, 8, 7.6, 7.6, 7.7, 7.4, 7.7, 7.8, 7.8 Life Span of Patients Treated at Hospital 2: 7.9, 7.9, 8.2, 8, 8.1, 8.5, 8.3, 8.4, 8, 8.2, 7.7, 8, 8, 7.8, 7.9, 8.1, 8.1, 7.8, 7.9, 8, 8.5, 8.3, 8.2, 8.3, 7.8, 7.9 Life Span of Patients Treated at Hospital 3: 8.2, 8.1, 7.4, 8.7, 8.6, 8.2, 7.9, 8.1, 8.1, 8.3, 8.3, 8, 7.6, 8, 7.4, 8.6, 8.2, 8.2, 7.9, 7.7, 8.1, 7.9, 8, 8.3 Life Span of…arrow_forwardFemale college student participation in athletics has increased dramatically over the past few decades. Sports medicine providers are aware of some unique health concerns of athletic women, including disordered eating. A study compared disordered-eating symptoms and their causes for collegiate female athletes (in lean and non lean sports) and nonathletes. The sample mean of the body dissatisfaction assessment score was 13.4 (s=7.9) for 15 lean sports athletes (those sports that place value on leanness, including distance running, swimming, and gymnastics) and 7.4 (s=5.8) for the 67 non-lean athletes. Assume equal population standard deviations. Find the standard error for comparing the means. Construct a 95% confidence interval for the difference between the mean body dissatisfaction for lean sport athletes and non lean sport athletes. Interpret.arrow_forwardTo illustrate the effects of driving under the influence of alcohol, a police officer brought a DUI simulator to a high school. Student reaction time in an emergency was measured with unimpaired vision and also while wearing special goggles to simulate the effect of alcohol on vision with the following results: Normal: 4.47, 4.24, 4.58, 4.65, 4.31, 4.8, 4.55, 5, 4.79 Impaired: 5.77, 5.67, 5.51, 5.32, 5.83, 5.49, 5.23, 5.61, 5.63 Is there evidence to suggest that there is a difference at alpha = 0.05level of significance.arrow_forward
- A well-known psychologist has established what she calls her Generalized Anxiety (GA) scale. The GA scale, which is a scale from 0 to 10, measures the "general anxiety" of an individual, with higher GA scores corresponding to more anxiety. We'd like to make predictions about individuals' sleep behavior based on their GA scores. We've collected bivariate data that give the GA score (denoted by x) and the number of hours of sleep last night (denoted by y) for each of the 11 adults participating in a study. The least-squares regression equation for our data is y = 8.92-0.25x. We have used this equation to predict tonight's sleep time for a woman whose GA score is 6.2. We're now interested in both a prediction interval for her sleep time and a confidence interval for the mean sleep time of individuals with her GA score. We have computed the following for our data. • mean square error (MSE)≈ 0.601 (6.2-x)² ● 11 + 11 2 Σ (x₁ - x)² i=1 ≈ 0.1110, where x₁, x2, Lower limit: X11 Based on this…arrow_forwardFoot ulcers are a common problem for people with diabetes. Higher skin temperatures on the foot indicate an increased risk of ulcers. The article "An Intelligent Insole for Diabetic Patients with the Loss of Protective Sensation" (Kimberly Anderson, M.S. Thesis, Colorado School of Mines), reports measurements of temperatures, in °F, of both feet for 181 diabetic patients. The results are presented in the following table. Left Foot Right Foot 80 80 85 85 75 80 88 86 89 87 87 82 78 78 88 89 89 90 76 81 89 86 87 82 78 78 80 81 87 82 86 85 76 80 88 89 Construct a scatterplot of the right foot temperature (y) versus the left foot temperature (x). Verify that a linear model is appropriate. b. Compute the least-squares line for predicting the right foot temperature from the left foot temperature. If the left foot temperatures of two patients differ by 2 degrees, by how much would you predict their right foot temperatures to differ? Predict the right foot temperature for a patient whose left…arrow_forwardThe cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen cadets, each bought new two years ago, and each sold used within the past month . For each cadet in the sample, we have listed both the mileage x (in thousands of miles) that the cadet had on its odometer at the time it was sold used and the price y (in thousand dollars) at which the cadet was sold used. The least-squares regression line for these data has equation of y=41.79-0.50x. This line is shown in the scatter plot below. A) for these data, used selling prices that are greater than the mean of the used selling prices tend fo be paired with mileages that are ___ the mean of the mileages ? B) according to the regression equation, for an increase of one thousand miles in cadet mileage, there is a corresponding decrease of how many thousand dollars in the used selling price?arrow_forward
- The Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, Ŷ=42.39-0.52x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. The least-squares regression line for these data has equation Ŷ=42.80-0.53x. This line is shown in the scatter plot below. (The 2nd picture contains the rest of the data as it would not fit in the first pic and it includes the question as well.)arrow_forwardThe Cadet is a popular model of sport utility vehicle, known for its relatively high resale value. The bivariate data given below were taken from a sample of sixteen Cadets, each bought new two years ago, and each sold used within the past month. For each Cadet in the sample, we have listed both the mileage x (in thousands of miles) that the Cadet had on its odometer at the time it was sold used and the price y (in thousands of dollars) at which the Cadet was sold used. With the aim of predicting the used selling price from the number of miles driven, we might examine the least-squares regression line, y = 41.51-0.49x. This line is shown in the scatter plot below. Mileage, x (in thousands) Used selling price, y (in thousands of dollars) 24.2 27.6 26.9 30.0 28.1 25.5 40+ 20.5 30.4 15.5 34.5 21.1 31.0 24.1 29.8 30- 23.4 28.3 37.8 23.3 27.7 29.5 20- 23.6 33.2 39.3 21.3 23.3 31.3 Mileage (in thousands) 25.7 26.4 34.4 25.4 29.4 28.8 Send data to calculator Send data to Excel Based on the…arrow_forward
- Glencoe Algebra 1, Student Edition, 9780079039897...AlgebraISBN:9780079039897Author:CarterPublisher:McGraw HillBig Ideas Math A Bridge To Success Algebra 1: Stu...AlgebraISBN:9781680331141Author:HOUGHTON MIFFLIN HARCOURTPublisher:Houghton Mifflin Harcourt