Participants included 508 students (51% female) in Grades 6 (35%), 7 (30%), and 8 (35%) at the outset of the study attending a middle school in a moderately sized, middle -class community in the northeastern United States. Engagement in NSSI (non-suicidal self-injury) was determined by an affirmative response to the item "Have you harmed or hurt your body on purpose (e.g., cutting or burning your If nullina out hoir)? Eo
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- Football and Brain SizeA study examines a possible relationship of football playing and concussions on hippocampus volume, in μL, in the brain. The study included three groups: controls who had never played football (Control), football players with no history of concussions (FBNoConcuss), and football players with a history of concussions (FBConcuss). The data is available in FootballBrain, and the side-by-side boxplots shown below indicate that the conditions for using the F-distribution appear to be met. b) Use technology to construct an ANOVA table. What is the F-statistic? What is the p-value? Round your answer for the F-statistic to two decimal places, and your answer for the p-value to three decimal places.F-statistic = ?p-value = ? Group Hipp LeftHipp Years Cogniton Control 6175 2945 0 Control 6220 3075 0 Control 6360 3125 0 Control 6465 3160 0 Control 6540 3205 0 Control 6780 3340 0…A researcher wants to see if gender and/or income affect the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. The independent variables are gender, income, and the interaction of gender and income. The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affect the total amount of help offered. Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA. His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect. The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables affected total help if he rejects the null hypothesis. What is one research hypothesis (there are three possible hypotheses here name them all if you can but naming at least one is required)?A researcher wants to see if gender and/or income affect the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. The independent variables are gender, income, and the interaction of gender and income. The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affect the total amount of help offered. Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA. His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect. The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables affected total help if he rejects the null hypothesis. Is there significance for either gender or income?
- A public health researcher is interested in some factors that influence heart disease. In a survey of 68 randomly selected localities, he gathered data on the percentage of people in each locality who bike to work “Biking”, the percentage of people in each locality who smoke “Smoking”, and the percentage of people in each locality who have heart disease “Heart.Disease”. The researcher wants to find which explanatory variable will be a better predictor of the response variable, “Heart.Disease”. Investigate the relationship between the explanatory variables and response variable to help the researcher find the better predictor. The dataset is called “Heart Disease.” Simple linear regression results: Dependent Variable: Heart. DiseaseIndependent Variable: BikingHeart. Disease = 18.115809 - 0.20845321 BikingSample size: 68R (correlation coefficient) = -0.94616452R-sq = 0.8952273Estimate of error standard deviation: 1.5273175 1) Which of the two explanatory variables would be the better…Design a two-factor ANOVA with variables of interest (personal, academic, career). In addition state your 3 hypothesis (2 main effects, 1 interaction). Example: Two-Factor ANOVA to evaluate the effect of Video Game Violence and Grade on Aggression Factors1. Factor A: Video Game Violence level (3 levels: low, moderate, high) 2. Factor B: Grade level (4 levels: 1st grade, 2nd grade, 3rd grade, 4th grade) 3. Dependent Variable: Aggression Hypothesis Tests1. Video Game Violence level affects Aggression2. Grade level affects Aggression 3. Interaction between Video Game Violence level and Grade level: video game violence level will affect aggression but only for 3rd and 4th graders but not for 1st and 2nd graders. INCLUDED ABOVE IS AN EXAMPLE. THIS IS NOT AN ESSAY QUESTION OR INCOMPLETE. THIS HAS BEEN REJECTED TWICE PLEASE DON'T DO THE SAME.Researchers studying the effect of antibiotic treatment compared to symptomatic treatment for acute sinusitis randomly assigned 166 adults diagnosed with sinusitis into two groups. Participants in the antibiotic group received a 10-day course of an antibiotic, and the rest received symptomatic treatments as a placebo. These pills had the same taste and packaging as the antibiotic. At the end of the 10-day period patients were asked if they experienced improvement in symptoms since the beginning of the study. The distribution of responses is summarized below. Antibiotic Placebo Total Yes 72 60 132 No 13 21 31 Total 85 81 166 A simulation was conducted under the assumpton that the proportion of self-reported improvement with the antibiotic treatment is equal to the proportion of self-reported improvement with the palcebo. 100,000 simulated differences were generated to construct the null distribution shown. The value ˆpa,∼p^a,∼ represents the proportion of self-reported…
- A researcher wants to see if gender and / or income affects the total amount of help given to a stranger who is sitting on the side of a busy road with a sign asking for help. The independent variables are gender, income, and the interaction of gender and income. The dependent variable is total help. He wants to know if one or both factors – or the interaction of the two - affects the total amount of help offered. Because he is analyzing two independent variables (gender and income), he used a factorial ANOVA. His results show the main effect of each of the independent variables on the dependent variable (total help) and the interaction effect. The researcher is using a 95% confidence interval which means that he wants to be at least 95% sure that his independent variables affected total help if he rejects the null hypothesis. Below is the data set followed by the results:Hospital re-admissions for patients with congestive heart failure (CHF) are relatively common and costly occurrences within the U.S. health infrastructure, including the Veterans Affairs (VA) healthcare system. Little is known about CHF re-admissions among rural veteran patients, including the effects of socio-demographics and follow-up outpatient visits on these re-admissions. Purpose: To examine socio-demographics of U.S. veterans with CHF who had 30 day potentially preventable re-admissions and compare the effect of 30 days VA post-discharge service use on these re-admissions for rural- and urban-dwelling veterans. (muus, Knedson, Klug, Gokun, Sarrazin, & Kaboli, 2010). 30 Day Re-admission Location of Residence Yes No Urban 17,872 3,792 Rural 9,428 1,906 When comparing the rates of re-admission for urban versus rural veterans, the authors obtained a p-value of 0.921 from a chi-square test of independence. What does this…Hospital re-admissions for patients with congestive heart failure (CHF) are relatively common and costly occurrences within the U.S. health infrastructure, including the Veterans Affairs (VA) healthcare system. Little is known about CHF re-admissions among rural veteran patients, including the effects of socio-demographics and follow-up outpatient visits on these re-admissions. Purpose: To examine socio-demographics of U.S. veterans with CHF who had 30 day potentially preventable re-admissions and compare the effect of 30 days VA post-discharge service use on these re-admissions for rural- and urban-dwelling veterans. (muus, Knedson, Klug, Gokun, Sarrazin, & Kaboli, 2010). 30 Day Re-admission Location of Residence Yes No Urban 17,872 3,792 Rural 9,428 1,906 The authors want to investigate re-admission rates for two-cohorts (urban and rural residence). Based on the information in the preceding table, create a table that provides…
- As part of the end-of-semester review of the marks obtained by third-year students in business statistics, the academic manager of Frontier Business School has captured the marks obtained by 10 randomly selected third-year students in their formative and summative assessments as shown below in Table.The academic manager wants to determine whether students’ performance (i.e., marks) in the formative assessment is a reliable predictor of students’ performance (i.e., marks) in the summative assessment. The relationship between the independent variable (IV) and the dependent variable (DV) is likely to be mediated by athird variable (MV). With regard to the statement above, which of the following is most likely to be correct?A IV = Students’ performance (i.e., marks) in the formative assessment; DV = Students’ performance (i.e., marks) inthe summative assessment, and MV = Students’ level of comfort with the venue of the assessments.B IV = Students’ performance (i.e., marks) in the summative…Variability in GMAT Scores. In 2016, the Graduate Management Admission Council reported that the variance in GMAT scores was 14,660. At a recent summit, a group of economics professors met to discuss the GMAT performance of undergraduate students majoring in economics. Some expected the variability in GMAT scores achieved by undergraduate economics students to be greater than the variability in GMAT scores of the general population of GMAT takers. However, others took the opposite view. The file EconGMAT contains GMAT scores for 51 randomly selected undergraduate students majoring in economics. GMAT Score 345 766 663 453 453 462 550 572 481 628 702 607 617 392 794 526 666 749 336 451 711 330 586 576 629 574 532 640 510 575 512 690 329 377 427 777 786 544 718 658 772 591 510 411 686 509 546 572…conduct a two-factor ANOVA to investigate the effects of gender (The name of the variable in SPSS is "gender" and it has two levels, "male" vs. "female") and "minority classification" (In SPSS, the name of the variable is "minority", its label is "Minority Classification" ,and it has two levels "yes" and "no"; referring to whether an individual is part of a minority or not ) on beginning salary (In SPSS the name of the variable is "salbegin", and its label is "Beginning Salary). Use α = .05 Question 4 Which two means are compared to determine whether there is a main effect of gender? Group of answer choices $13,091.97 and $17,016.09 $13,091.97 and $20,301.40 $17,673.01 and $14,678.94 $13,351.22 and $11,951.25 Question 5 Which two means are compared to determine whether there is a main effect of minority classification? Group of answer choices $13,351.22 and $11,951.25 $21,593.81 and $16,383.75 $13,091.97 and $20,301.40 $17,673.01 and $14,678.94 Question 6 Which of the…