Correlative Association Linkage Among Variables
.
a. Association: linkage among variables
b. Positive Association: values of two different variables increase simultaneously
c. Negative Association: inverse; one variable’s value increases as the other decreases
d. Non-linear Association: curvilinear; un-proportional increases/decreases between two variables
e. Dose-response Relationship: correlative association between an exposure and effect
2. A non-causal association is when one variable is related to but doesn’t cause the other variable (outcome); it is secondary and may include a third factor. For example, my grandma has diabetes, which runs in the family. That still doesn’t stop her from drinking sodas every day or eating cakes, candy, etc. (high sugar intake). A causal association is the reason for a certain disease (i.e. …show more content…
a. A statistic is based on a sample of a population, while a parameter describes the entire population.
b. A point estimate is a value that represents a population’s parameter. While a confidence interval estimate is a range of values with a probability of containing the population parameter.
c. Power demonstrates whether an association exists or not.
d. Statistical significance is more likely produced by large samples and small effects; it’s a specific cause that could have occurred by chance. On the other hand, clinical significance is a treatment’s noticeable effect on daily life. Just because something has statistical significance doesn’t necessarily mean that it’ll have clinical significance, too.
6. A multimodal curve displays multiple peaks of a condition’s frequencies. It is significant to epidemiology in that it allows you to determine latency of chronic diseases, shows changes in the host’s lifestyle or immune status, and of course, shows the frequencies. A multimodal curve would resemble waves if I were to sketch it.
7. Another name for unimodal is an epidemic curve. This type of curve is important for epidemiology because it helps identify the cause of disease
a. Association: linkage among variables
b. Positive Association: values of two different variables increase simultaneously
c. Negative Association: inverse; one variable’s value increases as the other decreases
d. Non-linear Association: curvilinear; un-proportional increases/decreases between two variables
e. Dose-response Relationship: correlative association between an exposure and effect
2. A non-causal association is when one variable is related to but doesn’t cause the other variable (outcome); it is secondary and may include a third factor. For example, my grandma has diabetes, which runs in the family. That still doesn’t stop her from drinking sodas every day or eating cakes, candy, etc. (high sugar intake). A causal association is the reason for a certain disease (i.e. …show more content…
a. A statistic is based on a sample of a population, while a parameter describes the entire population.
b. A point estimate is a value that represents a population’s parameter. While a confidence interval estimate is a range of values with a probability of containing the population parameter.
c. Power demonstrates whether an association exists or not.
d. Statistical significance is more likely produced by large samples and small effects; it’s a specific cause that could have occurred by chance. On the other hand, clinical significance is a treatment’s noticeable effect on daily life. Just because something has statistical significance doesn’t necessarily mean that it’ll have clinical significance, too.
6. A multimodal curve displays multiple peaks of a condition’s frequencies. It is significant to epidemiology in that it allows you to determine latency of chronic diseases, shows changes in the host’s lifestyle or immune status, and of course, shows the frequencies. A multimodal curve would resemble waves if I were to sketch it.
7. Another name for unimodal is an epidemic curve. This type of curve is important for epidemiology because it helps identify the cause of disease