Question

f. In Logistic Regression identify at least 3 variables for which you could calculate a logistic regression. Describe the variables and their scale of measurement. Which variables would you include as the predictor variables and which as the outcome variable? Why? Which regression method would you use and why? What would the output tell you about the relationship between the variables?

This is the full question

Answer #1

•List three variables (X1, X2, X3) you’d include in a Multiple
Regression Model in order to better predict an outcome (Y)
variable. For example, you might list three variables that could be
related to how long a person will live (Y). Or you might list three
variables that contribute to a successful restaurant. Your
Regression Model should have three variables that will act as
“predictors” (X1, X2, X3) of a “criterion” (Y’). Note that the
outcome or criterion variable (e.g....

b. Spearman’s Correlation. Identify two
variables for which you could calculate a Spearman’s
correlation coefficient. Describe the variables and their scale of
measurement. Now, assume you conducted a
correlation and came up with a significant positive or negative
value. Create a mock r value (for example, .3 or -.2). Report your
mock finding in APA style (note the text does not use APA style)
and interpret the statistic in terms of effect size and R2 while
also taking into account...

Which of the following variables would be used as a dependent
variable in logistic regression?
a) The number of days of school a student missed in a week
b) The distribution of whites, blacks, and Hispanics in a
sample
c) The birth weight of newborn babies
d) The presence or absence of cardiovascular disease

When you are presented with a Pearson’s correlation
coefficient between two variables for which an increase in one
predicts a decrease in the other, and vice versa, the Pearson’s
number will be
zero; the Pearson number is only meaningful if the
variables move in the same direction as one another
close to -1 if the correlation is strong, negative but
near zero if the correlation is weak
close to -1 if the correlation is strong, close to +1
if the...

Identify a research question from your professional life or
career specialization (youth development) that can be addressed by
an independent samples t test.
Indicate why a t test would be the appropriate analysis for
this research question.
Describe the variables and their scale of measurement.
Discuss the expected outcome (for example, "The Group 1 mean
score will be significantly greater than the Group 2 mean score
because…")

“Being bilingual protects from dementia.”
A). Identify the implied dependent and independent
variables.
B). Does the headline demonstrate a positive or negative
correlation between the two?
C). Is there a plausible mechanism (confounding factor)
through which we could explain why the independent variable has
a
causal impact on the outcome?
D). Now try to think of reasons why this could be a spurious
relationship instead and construct alternative explanations.

Suppose a null model is applied to describe the relation of two
random variables, you may add one more variable into the model as a
predictor, and that would be the second model.
a. Draw two scatter plots to illustrate the fitting of the two
models, which can show simple regression has a better fitting.
b. Tell how the R^2 changes and how adjusted R^2 change.
c. Write an estimator of the coefficient of the null model.

“Can using artificial sweeteners increase your BMI?”
A). Identify the implied dependent and independent
variables.
B). Does the headline demonstrate a positive or negative
correlation between the two?
C). Is there a plausible mechanism (confounding factor)
through which we could explain why the independent variable has
a
causal impact on the outcome?
D). Now try to think of reasons why this could be a spurious
relationship instead and construct alternative explanations.

Think about an issue at work or in some other facet of your life
in which a regression analysis to come up with a way to predict a
certain quantitative outcome (or dependent) variable might be
useful. Then, discuss this situation, describe the variables that
would be involved, and how regression analysis could be
beneficial.

Which statement explains why correlation could be 0 even if a
strong relationship between two variables existed?
Group of answer choices
Since the correlation is 0, there is no strong relationship
between the two variables; and a scatterplot would be
misleading.
Correlation can be 0 even if there is a strong linear
relationship between the variables.
Correlation only measures the strength of the relationship
between two variables when the units of the two variables are the
same.
Correlation does not...

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