A study about lung capacity was conducted. The outcome variable is forced expiratory volume (FEV), which is, essentially, the amount of air an individual can exhale in the first second of a forceful breath. The data recorded include: FEV (liters), Age (years), Height (inches), and Sex. The data are in FEV4.csv. (As with many older studies, this study considered Sex as a binary variable. This thinking has been changing in recent years, and I think the field of statistics has been more progressive in this regard than other STEM fields) (A) What would an Age × Sex interaction mean in this context? (B) Create an appropriate plot to visualize the relationship among FEV, Age, and Sex. Include it here. (C) Based on the plot, does there seem to be an Age × Sex interaction? Briefly explain. (Two or three words will suffice) (D) Obtain the linear regression model relating FEV to Age and Height. Write the regression equation. (E) Estimate the mean FEV for 14-year-old children who are 66 inches tall. Include an interval that characterizes the expected range of FEV values and state an interpretation of this interval with appropriate units. (F) Test H0​:βAge ​=βHeight ​=0 in a model that predicts FEV using all three predictors. Give the statistic and P-value as well as your conclusion. (G) Assess any evidence for confounding of the relationship with FEV among the two quantitative predictor variables (Age and Height). Include the following four correlation coefficients, and summarize the results in plain terms: - Pearson correlation between FEV and Height - Pearson correlation between FEV and Age - Partial correlation between FEV and Height controlling for Age - Partial correlation between FEV and Age controlling for Height - Conclusion: (H) Compute variance inflation factors for the model with all three predictors and state an interpretation of these. You can use the viff ) function in the car R package (I) You are asked to quantify the relationship between FEV and the predictors (Age, Height, Sex) in school-age children. Try to find the "best" model - which set of predictors best explains FEV? Or you might prefer a simpler model, sacrificing predictive power for interpretability. - You should consider interaction terms, and may want to consider polynomial terms and variable transformations as well. One way to approach this: Start with a full model that includes all interaction terms, including the 3-way interaction. If an interaction term does not seem important, you can remove it from the model (unless a higher-order interaction term is important), then run a new regression. Continue this iterative process until you arrive at a model where all terms are meaningful. List the variables (and interactions, and polynomials terms, or transformed variables etc if any) in your final model. Justify why you think this is the best model. (J) Are the regression assumptions/conditions met for your model \& results to be valid? Address them each. You should include some, but not all, relevant R output - pick the ones you find most important or interesting.

Answers

Answer 1
Answer:

Answer:A) Age × Sex Interaction:

In this context, an Age × Sex interaction would mean that the relationship between age (in years) and FEV (forced expiratory volume) is different for males and females. In other words, the effect of age on FEV is not the same for both sexes.

B) Visualization:

To visualize the relationship among FEV, Age, and Sex, you can create scatterplots or box plots. You might want to create separate plots for males and females, plotting FEV against Age. This will help you see if there are any notable patterns or differences between the sexes.

C) Age × Sex Interaction Assessment:

Based on the plot, you can assess whether there appears to be an Age × Sex interaction. Look for patterns where the relationship between Age and FEV differs between males and females. If the lines or patterns on the plots for males and females diverge or cross, this suggests an interaction.

D) Linear Regression Model:

You can use linear regression to relate FEV to Age and Height. The regression equation might look like:

FEV = β0 + β1 * Age + β2 * Height + ε

E) Mean FEV Estimation:

To estimate the mean FEV for 14-year-old children who are 66 inches tall, you would substitute the values into the regression equation obtained in part D and calculate the predicted FEV. The interval can be constructed based on the standard error of the prediction.

F) Hypothesis Testing:

For testing H0: βAge = βHeight = 0, you can perform an F-test or assess the significance of each coefficient in the regression model. The statistic, P-value, and conclusion can be derived from the regression output.

G) Confounding Assessment:

Calculate Pearson correlations between FEV and Height and FEV and Age. Then calculate partial correlations controlling for the other predictor. Assess if controlling for one predictor changes the relationship between FEV and the other predictor.

H) Variance Inflation Factors (VIFs):

Compute VIFs for the model with all three predictors (Age, Height, Sex). VIFs help identify multicollinearity. Interpret VIF values to assess whether multicollinearity is a concern.

I) Model Selection:

Starting with a full model, gradually remove interactions and terms that do not contribute significantly to the model's explanatory power. Consider the AIC or BIC to guide model selection. Justify your choice of the final model based on statistical significance and interpretability.

J) Regression Assumptions:

Address regression assumptions such as linearity, independence of errors, homoscedasticity, and normality of residuals. Use diagnostic plots and statistical tests to assess these assumptions and make corrections if necessary.

Please note that this is a complex statistical analysis project that involves data manipulation, visualization, and modeling. You may need to use statistical software like R, Python, or specialized statistical packages to perform these tasks and draw meaningful conclusions from your data.


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Please select the word from the list that best fits the definitionThe intended and recognized consequence of some element of society.
symbol
latent function
dysfunctional
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Answers

Select the text from the list.

The list consists of the elements in the society and the words that are best used to show the elements of the society at large are those that include society at large.

These elements include the connection the society has with each other and is guided by the way they interact and the social norms they have.

This is the answer is the ideal type.

  • The elements are made of a group of mater and these mater forms the base of the society is this collection of the various people and thus is a complex of the functions like the political, economic and social.
  • The aspect of society test seems to recognize as a consequence include the ideal type.

Learn more about the select the word.

brainly.com/question/20342052.

_____ send information from the body sensors to the Central Nervous System. A: Afferent nerves
B: Cranial nerves
C: Autonomic nereves

Answers

Afferent nerves send information from the body sensors to the Central Nervous System.Thus, option A is correct.

Afferent nerves are sensory nerves that carry information from the body's sensory receptors to the central nervous system (CNS). This information can be about the environment, such as touch, pain, temperature, and vision, or it can be about the body's internal state, such as blood pressure, heart rate, and blood sugar levels.

Cranial nerves are a special type of afferent nerve that originates in the brain. They are responsible for a variety of functions, including vision, hearing, smell, taste, and facial expression.

Autonomic nerves are a type of efferent nerve that controls involuntary functions of the body, such as heart rate, blood pressure, and digestion. They do not carry information from the body to the CNS.

So the correct answer is A: Afferent nerves.

Learn more about Nervous System on:

brainly.com/question/8695732

#SPJ3

Answer:

A.

Explanation:

The neurons responsible for taking information to the CNS are known as afferent neurons.

The average 8- to 18-year-old spends __________ per day on average in front of a screen doing very little to no physical activity.

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3 hours or more...
About three or more hours

An expecting mother's STI can put the life of the unborn baby at risk.
T/F

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True, an expecting mother's STI can put the life of the unborn baby at risk. 

STI stands for sexually transmitted infections, such as HIV and hepatitis B. Both of those can affect an unborn baby if the mother has it. The risks of every unborn baby vary. One risk could be death, blood infections, deafness, or blindness. 

true a mother with sti can most definitely harm the baby if its passed on

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Answers

Arthralgia.

The definition of arthralgia directly translates to pain in a joint!

Where would the following activity best prymid?

Answers

no such word as prymid