Determine whether the description corresponds to an observational study or an experiment. Research is conducted to determine if there is a relation between kidney function in adults and childhood-onset diabetes. Does the description correspond to an observational study or an experiment? Observational study O Experiment

Determine whether the description corresponds to an observational study or an experiment. Research is conducted to determine if there is a relation between kidney function in adults and childhood-onset diabetes. Does the description correspond to an observational study or an experiment? Observational study O Experiment

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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Using the data from the table Disease or illness less than $35,000 $35,000 - 49,999 $50,000 - 74,999 $75,000 - 99,999 $100,000 or more Coronary heart disease 8.1 6.5 6.3 5.3 4.9 Stroke 3.9 2.5 2.3 1.8 1.6 Emphysema 3.2 2.5 1.4 1.0 0.8 Chronic bronchitis 6.3 4.0 4.4 2.2 2.4 Diabetes 11.0 10.4 8.3 5.6 5.9 Ulcers 8.7 6.7 6.5 4.7 4.4 Kidney disease 3.0 1.9 1.3 0.9 0.9 Liver disease 2.0 1.6 1.0 0.6 0.7 Chronic arthritis 33.4 30.3 27.9 27.4 24.4 Hearing trouble 17.2 16.0 16.0 16.2 12.4 Vision trouble 12.7 9.8 7.5 5.7 6.6 No teeth 11.6 7.8 5.5 4.2 4.1 A) create a line graph plotting Annual family income on the x-axis and prevalence of disease or illness on the y-axis. Include all data points for each disease on the same chart ( i.e.? plot the prevalence for each disease or illness as it's own line on a single chart) B) do any of the chronic disease in the table appear to have a dose-response relationship to annual family income? Justify your…
tab Determine if there is a significant correlation between the sets of data at a 10% significance level. 31 29 57 35 60 62 49 62 50 35 37 32 57 20 X y 55 38 Negative Critical Value, tcrit Submit Question esc Positive Critical Value, tcrit Test Statistic, ttest C 2 F Test Conclusion: Reject Ho. There is enough evidence to suggest a significant (positive or negative) linear correlation between the data sets. W = Fail to Reject Ho. There is not enough evidence to suggest a significant (positive or negative) linear correlation between the data sets. 3 H [three decimal accuracy] [three decimal accuracy] [three decimal accuracy] a E A 4 do *5 R T < 6 Y
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Aspirin III: Decision and Conclusion Regarding the experiment in the data frame Aspirin from the abd package, the researchers wanted to know whether or not taking aspirin affects one's risk of developing cancer. Recall that they defined their parameters as follows: p1 = the proportion of ALL individuals who would develop cancer, if all of them were to take aspirin like the subjects in the Aspirin group did. p2 = the proportion of ALL individuals who would develop cancer, if all of them were to take a placebo, like the subjects in the placebo group did. They ran the code for a two-sided significance test and got the following results: ## ## ## Inferential Procedures for the Difference of Two Proportions p1-p2:## cancer grouped by treatment ## ## ## Descriptive Results:## ## yes n estimated.prop## Aspirin 1438 19934 0.07214## Placebo 1427 19942 0.07156## ## ## Inferential Results:## ## Estimate of p1-p2: 0.0005805 ## SE(p1.hat - p2.hat): 0.002586 ## ##…
carefully weigh the data before casting your vote. As you review the data, you ask yourself, Assuming both teams handled roughly the same number of customers, which chart impresses me more? Which team is the most consistent? Which team, over time, has better average scores? Growing scores? Declining scores? What does the Descriptive Statistics ToolPak tell me about the data? Which team is showing improvement? Can I estimate (or visually predict using the best fit line) which team will have better future performance? If so, which team? How do I know? …and, for whom should I vote?
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In what ways would you measure causality in research? Select all that apply. Measuring the changes observed Controlling confounding influences Observing the effects on the dependent variable(s) You can't really measure cause and effect.
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Tips It’s commonly believed that people use tips toreward good service. A researcher for the hospitalityindustry examined tips and ratings of service qualityfrom 2645 dining parties at 21 different restaurants. The correlation between ratings of service and tip percent-ages was 0.11. (M. Lynn and M. McCall, “Gratitude and Gratuity.” Journal of Socio-Economics 29: 203–214)a) Describe the relationship between Quality of Serviceand Tip Size.b) Find and interpret the value of R2 in this context.
Question Use the data given below: x y 2.3 3.9 4 1.4 2.9 3.5 4.7 5 additional data point (10.3, 9.3) Explain why correlations should always be reported with scatter diagrams? Choose one of the following to answer a) the scatter diagram can be used to distinguish between association and causation. b) the scatter diagram is needed to determine if the correlation is positive or negative. c) the scatter diagram is needed to see if the correlation coefficient is being affected by the presence of outliers.
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