You will be applying what you have learned in this course by gathering data and running a statistical analysis.
To study about the relationship between height and the weight, you need to collect a sample of nine (9) people using a systematic sampling method.
What is the population of people?
Where and how are you going to collect your sample?
Does your sample accurately represent your population? Why or why not?
Collect the sample and record the data.
(CLO 1) Construct a confidence interval to estimate the mean height and the mean weight by completing the following:
Find the sample mean and the sample standard deviation of the height.
Find the sample mean and the sample standard deviation of the weight.
Construct and interpret a confidence interval to estimate the mean height.
Construct and interpret a confidence interval to estimate the mean weight.
(CLO 2) Test a claim that the mean height of people you know is not equal to 64 inches using the p-value method or the traditional method by completing the following:
State H0 and H1.
Find the p value or critical value(s).
Draw a conclusion in context of the situation.
(CLO 3) Create a scatterplot with the height on the x-axis and the weight on the y-axis. Find the correlation coefficient between the height and the weight. What does the correlation coefficient tell you about your data? Construct the equation of the regression line and use it to predict the weight of a person who is 68 inches tall.
Write a paragraph or two about what you have learned from this process. When you read, see, or hear a statistic in the future, what skills will you apply to know whether you can trust the result?
-Prothrombic inflammatory vascular environment The presence of this cluster of factors increases the risk of cardiovascular events. Childhood obesity predisposes to endothelial dysfunction, carotid intimal medial thickening, and the development of early aortic and coronary arterial fibrous plaques. Sleep apnea and obesity related hypoventilation might contribute to pulmonary arterial hypertension. MS has been a well-defined entity in adults but the definition in children is still variable. Prevalence rates in the pediatric age group vary depending on the criteria used. The International Diabetes Federation’s (IDF) criteria (17) for diagnosing metabolic syndrome requires the presence of central obesity plus any two of the other four factors: TABLE 6 10 to <16years ≥16years Obesity (WC)* ≥90th percentile ≥94cm (males) ≥80cm (females) Triglycerides ≥150mg/dl ≥150mg/dl HDL cholesterol <40mg/dl <40mg/dl (males) <50mg/dl (females) Blood Pressure SBP ≥130mmHg, or DBP ≥85mmHg SBP ≥130mmHg, or DBP ≥85mmHg, or Treatment of previously diagnosed Hypertension Fating plasma glucose ≥100mg/dl, or known T2DM ≥100mg/dl, or known T2DM *Country specific waist circumference standards should be used if available. For children aged 6 to <10yr, though MS cannot be diagnosed but further measurements should be made in children with a family history T2DM, MS, dyslipidemia, cardiovascular disease, hypertension and/or obesity. NCEP/ATPIII definition for children 12-18 years: Individuals with ≥3 of the following are considered at risk for MS: -Waist circumference ≥90th Percentile for age and sex – HDL cholesterol ≤40 mg/dl, -Triglycerides ≥110 mg/dl – Fasting plasma glucose >110 mg/dl, and -BP ≥90th percentile according to age and sex Waist circumference percentiles for the Indian Population were published recently by Khadilkar et al (18). They have suggested a cut-off of 70th percentile for WC, to screen for Metabolic Syndrome in Indian children. 5. Nonalcoholic Fatty Liver Disease (NAFLD):>GET ANSWER