STAT1070-Relationship Between Distance Lived and Face-to-Face Delivery Report Writing - Statistics Assignment Help

Assignment Task

Task

Question 1.

Suppose a university offers courses with both online and face-to-face delivery.In order to better understand whether distance lived from campus may play a role in the type of enrolment,the university randomly samples 50 students enrolled in online classes and 50 students enrolled in faceto-face classes and measures the distance each student lives from campus.The data are stored in the file enrolment,which contains the following variables:

• Distance: the distance the student lives from campus, in kilometres
• Enrolment: type of enrolment (online or face-to-face)

(a) Using appropriate graphs and statistics, describe the relationship between distance lived from campus and the type of enrolment.

(b)  Are the online and face-to-face samples paired or independent? Write a sentence justifying your choice.

(c) Is there evidence that the average distance lived from campus is different for students enrolled in online classes and students enrolled in face-to-face classes? Conduct the appropriate test in jamovi and include relevant output. Be sure to define any parameters you use, state the null and alternative hypotheses, observed test statistic, null distribution, p-value, decision and provide an appropriate conclusion in plain language.

(d) Report the 95% confidence interval using jamovi for the difference in average distance lived from campus. Write a sentence interpreting this interval in plain language.

(e)  Does this confidence interval from (d) support the decision made in part (c)?

(f) What are the assumptions of your analyses in parts (c) and (d)? Are these assumptions met? Justify why or why not for each assumption, with appropriate references to jamovi output where needed.

Question 2. 

To investigate whether a country’s income level has an impact on education, a random sample of 60 countries was obtained from ourworldindata.org, as contained in the file countries. The variables in the file are:

• Country: The name of the country.
• Continent: The continent to which the country belongs.
• Income Level: Economic development level, with 4 categories: low income, lower-middle income, upper-middle income, and high income.
• Satisfaction: The average life satisfaction score from residents of the country.
• Life expectancy (years): The life expectancy of the country, in years.
• % Unaffordable Diet: The percentage of the population for which a healthy diet is deemed unaffordable.
• School life expectancy: How many years of education a child of school entrance age can expect to receive if the current age-specific enrolment rates persist throughout the child’s years of schooling.

(a)  In Assignment 1, you explored the relationship between income level and school life expectancy using descriptive statistics. Is there evidence of a difference in average school life expectancy values among the four income levels? Be sure to define any parameters you use, state the null and alternative hypotheses, observed test statistic, null distribution, p-value, decision and provide an appropriate conclusion in plain language.

(b)  If appropriate, perform post-hoc tests to determine which income levels have significantly different average school life expectancies. If post-hoc tests are not appropriate, explain the purpose of a post-hoc test and why it’s not appropriate in this example.

(c)  What are the assumptions of the analysis performed in part (a)? State whether each assumption is reasonable with reference to appropriate jamovi output.

Question 3. 

Urine specific gravity (USG) is a measure of the weight of solids in water. Urine osmolality is a measure of the concentration of the urine. Osmolality and USG measurements are usually highly positively correlated, however, when large and heavy molecules (such as glucose and protein) are present in the urine the results can diverge.The USG test is easier and more convenient, and is part of a routine urinalysis.Osmolality is considered a more exact measurement of urine concentration than USG,however, USG is often used by clinicians in routine practice to predict urine osmolality.In the questions that follow, you will explore the idea of using USG to predict urine osmolality in the urine.omv data on 78 urine specimens.In the OMV file the variable USG represents urine specific gravity and Osmolality represents the osmolality (mOsm).

(a) Generate an appropriate scatter plot with a fitted regression line.

(b) Is there a statistically significant positive linear relationship between specific gravity and osmolality? Be sure to define any parameters you use, state the null and alternative hypotheses, observed test statistic, null distribution, p-value, decision and provide an appropriate conclusion in plain language.

(c)  State the assumptions necessary for a regression analysis to be appropriate. State whether each of them is satisfied with a brief justification.

(d) Write down the equation for the estimated regression line and provide an interpretation of the slope coefficient.

(e) Predict the osmolality for a USG value of 1.025.

(f)  Write down the R2 value for this regression and give an interpretation.

(g) Based on the R2 value do you think that using USG to predict osmolality is a sensible thing to do? Why or why not.

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