Answer:
See explanation below.
Explanation:
<em>Study Design - Partially double-blinded study</em>
- The <u>cohort study </u>will be composed of 80 adults (aged ≥18 years) with a clinical diagnosis of constant coughing present as a result of a cold, which would be measured by semiautomated ambulatory cough monitor.
- This study will be divided into an experimental group and a control group:
- No medication (natural drops; <em>e.g.</em> water; saline solution) - <em>Control group </em>
- Cough treatment - <em>Experimental group</em>
Participant cough will be recorded <u>every hour</u> (until 8 hours are reached) for two consecutive days (for repetitions) in both groups using semiautomated ambulatory cough monitor and analyzed statistically.
Experimental group: <u>If coughing is significantly (statistical analysis) reduced within the 8 hours </u>(<em>after repetitions</em>) and a symptom improvement is observed, <u>the hypothesis that cough drops are effective is valid</u>.
Control group: To determine that cough drops treatment is effective, the no-treatment group should score worst and participant should not show significant improvement.
*<em>In scientific experiments, repetitions are important to prevent bias and error.</em>
D.) <span>Unlike DNA, RNA is "a single stranded molecule"
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B. the speed of the water
Explanation:
The independent variable here is the speed of the water being poured through the tube.
An independent variable is the variable is the whose change does not rely on another.
- The speed of the water is the cause of the sand deposition.
- This is the independent variable.
- The quantity of sand that is deposited is the dependent variable.
- It relies on the speed of the water.
- The dependent variable is the effect of the speed of water being poured.
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Resulting factors are called Second-order factors
<h3>
What is factor analysis?</h3>
- Factor analysis is a statistical approach for describing variability in seen, correlated variables in terms of a possibly smaller number of unobserved variables known as factors.
- It is possible, for example, that fluctuations in six known variables mostly reflect variations in two unseen (underlying) variables.
- Factor analysis looks for such joint fluctuations in response to latent variables that are not noticed.
- Factor analysis may be regarded of as a specific form of errors-in-variables models since the observed variables are described as linear combinations of the possible factors plus "error" terms.
- It may help to deal with data sets where there are large numbers of observed variables that are thought to reflect a smaller number of underlying/latent variables.
- It is one of the most commonly used inter-dependency techniques and is used when the relevant set of variables shows a systematic inter-dependence and the objective is to find out the latent factors that create a commonality.
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B. Consumers produce Co2 for which plants need to allow photosynthesis to turn back into oxygen.
