Question

If a regression analysis was to be completed on body mass index (BMI), what could be an independent variable in that analysis? Why? If we could, what other independent variables should be included in the analysis? What statistic(s) would show the value of that regression in understanding BMI? Alternatively, find an article that uses regression analysis to study a medical concern. In that study, what was the dependent variable and what were the independent variable(s)? Further, how would you use this study to highlight the difference between correlations and causation?

Answer #1

For example, the body mass index (BMI) of women which is the measure of weight relative to height is recoreded to know the people with overweight and obese. People with high BMI are considered to be overweight and obese.

Dependent variable: Body mass index (BMI)

Independent variable: Duration of physical activity (in minutes), Consumption of particular kind of foods,

The correlation coefficient between dependent and independent
variable shows the relationship between these two variable(In
regression analysis r^{2} value is obtained where r is the
correlation coefficient).

The correlation coefficient tells the association between variables while the causation explains these association. The causation can be identified by the experiment when one independent variable changes while holding other variable constant.

If a regression analysis was to be completed on body mass index
(BMI), what could be an independent variable in that analysis? Why?
If we could, what other independent variables should be included in
the analysis? What statistic(s) would show the value of that
regression in understanding BMI? Alternatively, find an article
that uses regression analysis to study a medical concern. In that
study, what was the dependent variable and what were the
independent variable(s)? Further, how would you use...

If a regression analysis was to be completed on body mass index
(BMI), what could be an independent variable in that analysis?
Why? If we could, what other independent variables
should be included in the analysis? What statistic(s) would show
the value of that regression in understanding BMI?

A researcher conducted a study on mindfulness and body mass
index (BMI). She collected BMI scores from a large group of
participants who had completed a mindfulness training program and
then compared their scores to the national average BMI scores from
the general population. What type of test should be used to see if
taking the mindfulness program was beneficial for BMI scores
not?
Group of answer choices
One-sample t test
Dependent-means (paired-samples) t test
Independent-means (Independent-samples) t test
Pearson's...

A pharmaceutical science researcher would like to use BMI (body
mass index) to predict the insulin sensitivity. After analyzing the
linear regression, we got a summarized output from computer in
Table 4. Please answer following questions: (1 point) a) What is
dependent variable? b) What is independent variable? c) What is the
value of slope here? How to interpret the slope here? d) What is
the R square value? How to interpret the R square? e) Please write
down a...

In a regression analysis, _____ represents the proportion of
variations in the dependent variable, Y, could be explained by the
independent variables (all the Xs).
R2
F statistics
t statistics
p value

Your body mass index (BMI) is your weight in kilograms divided
by the square of your height in meters. High BMI is a common but
controversial indicator of overweight or obesity. A study by the
National Center for Health Statistics found that the BMI of
American young men (ages 20-29) is approximately Normal with mean
25.8 and standard deviation 4.2. To have a BMI higher than 90% of
all men aged 20-29, what would your BMI would need to be?

A regression analysis was performed and the summary output is
shown below.
Regression Statistics
Multiple R
0.7149844700.714984470
R Square
0.5112027920.511202792
Adjusted R Square
0.4904029110.490402911
Standard Error
8.2079903998.207990399
Observations
5050
ANOVA
dfdf
SSSS
MSMS
FF
Significance FF
Regression
22
3311.5863311.586
1655.7931655.793
24.577224.5772
4.9491E-084.9491E-08
Residual
4747
3166.4423166.442
67.37167.371
Total
4949
6478.0286478.028
Step 1 of 2:
How many independent variables are included in the regression
model?
Step 2 of 2:
Which measure is appropriate for determining the proportion of
variation in the dependent...

What socio-economic variables could serve as Independent
variables when conducting a multiple linear Regression with the
dependent variable "Happiness Index"? (I already have GDP/capita,
divorce rate, unemployment rate, urbanization rate) Maybe you also
can come up with an "interesting" one where there is not yet so
much Research on a potential correlation with Happiness. Thanks in
advance

(7) A regression analysis was used in a study about perceived
strength (str) and body condition (cond) among seniors, both
measures are in the range of 0-100. Answer questions based on the
given output
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
1
.880a
.704
.701
2.404
a. Predictors:
(Constant), str
ANOVAb
Model
Sum of Squares
df
Mean Square
F
Sig.
1
Regression
688.725
1
688.725
101.665
.002a
Residual
2553.465
414
6.168
Total...

Regression Analysis with a Minitab output
Assume that your company owns multiple retail outlets in cities
across the United States. You conduct a study to determine if daily
sales levels (in hundreds of dollars) can be predicted by the
number of competitors that are located within a one-mile radius of
each location and city population (in thousands of people).
Therefore, the dependent variable is SALES and the two independent
variables are NUMBER OF COMPETITORS and CITY POPULATION. Your
research team...

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