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Homework statistics
Title: Homework statistics chapter 7
Name: Date:
Introduction: The most usual applications of Statistics is
describing a set of data descriptive statistics, regression, and
hypothesis testing and inferential statistics. The two main
branches are descriptive and inferential statistics. People who
do not
have any formal training in statistics are more familiar with
inferential statistics than with descriptive statistics. Descriptive
Statistics Definition
The descriptive statistics is the type of statistical analysis which
helps to describes about the data in some meaningful way. The
statistics is used to describe quantitatively about the important
features of the data or information. The descriptive statistics
gives the summaries of the given sample as well as the
observations done. These summaries or descriptions can either
be
graphical or quantitative. Inferential Statistics Definition

Inferential statistics is the type of statistics which deals with
making conclusions. It inferences about the predictions for the
population. It also analyses the sample. Basically, the
inferential statistics is the procedure of drawing predictions and
conclusions about the given data which is subjected to the
random variations. Inferential statistics includes detection and
prediction of observational and sampling errors. This type of
statistics is being utilized in order to make estimates and test
the
hypotheses using given data. There are two major divisions of
inferential statistics: 1) Confidence Interval: The
confidence interval is represented in the form of an interval that
provides a range for the parameter of given population. 2)
Hypothesis Test: Hypothesis tests are also known as tests of
significance which tests some claim for the population by
analyzing
sample. In this paper we will focus on hypothesis to test the raw
and make a logical conclusion for example The Framingham
Heart Study dataset provided, using ANOVA multivariable
linear regression analysis using BMI as a continuous variable.
Before
we conduct the regression and ANOVA test I would like to
make the hypothesis. Null Hypothesis H0: The BMI is not
related
to the patient characteristics in the Framingham Heart Study.
Alternative Hypothesis H1: The BMI is related to the patient
characteristics in the Framingham Heart Study. Level of
significance is 0.05 Before using excel I excluded participants
with
missing data on analysis variables (age, sex, systolic blood
pressure, total serum cholesterol, current smoker, and diabetes =
cleaning the data). To perform simple linear regression
(ANOVA) I assumed BMI as independent variable and
independent
variables are age, sex, systolic blood pressure, total serum
cholesterol, current smoker, and diabetes and SEX is coded

1=male
and 2=female. The output of the regression:
From the regression output we can see that the significance F
value is 0.500 which is less than 0.05, we reject Null hypothesis
and concluded that the BMI is related to the patient
characteristics in the Framingham Heart Study. The R-squared
(R2) is a
statistical measure that represents the proportion of the variance
for a dependent variable that's explained by an independent
variable or variables in a regression model. For this model the R
square is 0.000 which indicates that there is 0% variance for a
dependent variable that's explained by an independent variable
or variables in a regression model. The significance of
individual variable is given below.
The individual variable is significance and concluded that for
individual variable The BMI is related to the patient
characteristics
in the Framingham Heart Study. References
Walpole, R. (1982). Introduction to Statistics. (3rd ed.).
Prentice Hall Publication. Downie, N. M. & Heath, R. W.
(1965).
Basic Statistical Methods (2nd ed.). Harper & Row Publisher
Reid, H. (2013, August). Introduction to Statistics. SAGE
Publication.
1
2
1
2 2

3
3
1 1
1
1
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biol
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biol
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1
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The most usual applications
of Statistics is describing a
set of data descriptive
statistics, regression, and
hypothesis testing and
inferential statistics. The
two main branches are
descriptive and inferential
statistics. People who do
not have any formal
training in statistics are
more familiar with
inferential statistics than
with descriptive statistics.
Descriptive Statistics
Definition
Original source

statistics hypothesis testing
and inferential statistics
The two main branches are
statistics People who do
not have any formal
more familiar with
with descriptive statistics
Descriptive Statistics
Definition
1
Student paper
The descriptive statistics is
the type of statistical
analysis which helps to
describes about the data in
some meaningful way. The
statistics is used to describe
quantitatively about the
important features of the
data or information. The
descriptive statistics gives
the summaries of the given
sample as well as the
observations done. These
summaries or descriptions
can either be graphical or
quantitative.

Original source
some meaningful way The
data or information The
observations done These
quantitative
1
Student paper
Inferential Statistics
Definition
Original source
Inferential Statistics
Definition
2
Student paper

Inferential statistics is the
type of statistics which
deals with making
conclusions. It inferences
about the predictions for
the population.
Original source
Inferential statistics
Inferential statistics is the
type of statistics which
deals with making
conclusions It inferences
about the predictions of the
population
1
Student paper
It also analyses the sample.
Basically, the inferential
statistics is the procedure
of drawing predictions and
conclusions about the given
data which is subjected to
the random variations.
includes detection and
prediction of observational
and sampling errors. This
type of statistics is being
utilized in order to make
estimates and test the
hypotheses using given

data.
Original source
It also analyses the sample
Basically, the inferential
statistics is the procedure
of drawing predictions and
conclusions about the given
data which is subjected to
the random variations
includes detection and
prediction of observational
and sampling errors This
type of statistics is being
utilized in order to make
estimates and test the
hypotheses using given
data
2
Student paper
There are two major
divisions of inferential
statistics:
Original source
There are two major
divisions of inferential
statistics
2

Student paper
The confidence interval is
represented in the form of
an interval that provides a
range for the parameter of
given population.
Original source
it is represented in the
form of an interval that
provides a range for the
parameter of given
population
3
Student paper
Null Hypothesis H0:
Original source
null hypothesis, H0
3
Student paper
Alternative Hypothesis H1:
Original source
— alternative h., H1

1
Student paper
Introduction to Statistics.
Original source
Introduction to Statistics
1
Student paper
Prentice Hall Publication.
Original source
Prentice Hall Publication
Student paper
100%
Student paper
100%
1

Student paper
Basic Statistical Methods
(2nd ed.). Harper & Row
Publisher
Original source
(2nd ed.) Harper & Row
Publisher
1
Student paper
Original source
Introduction to Statistics
What Is Listening?
Listening is the process of hearing someone speak, processing
what you’re hearing, and demonstrating that you understand the
speaker’s intent.
Effective listening has three dimensions: sensing,
processing/evaluating, and responding.
Sensing involves hearing the words and receiving the nonverbal
signals such as body language and facial expressions.
Processing/evaluating involves understanding the meaning,

interpreting the implications, evaluating the nonverbal cues, and
remembering the message.
Responding involves the listener sending the speaker verbal or
nonverbal signals that he or she is being heard.
The act of simply “hearing” is a passive activity. The act of
listening—truly listening— demands attention, concentration,
and effort.
There are several different types of listening:
Passive listening occurs when you are trying to absorb as much
of the information presented as possible. You act as a sponge,
taking in the information with no or little attempt to process or
enhance the messages being sent by the speaker.
Attentive listening occurs when you are genuinely interested in
the speaker’s point of view. You are aware something can be
learned from the interaction. In attentive listening, you make
assumptions about the messages being relayed by the speaker
and fill in gaps with assumptions based on what you want to
hear rather than on what the speaker is actually saying. At this
level of listening, you don’t check to see whether what you
heard is what the speaker intended to say. Many barriers and
biases can hinder this form of interaction.
Active or empathetic listening is the most powerful level of
listening and requires the largest amount of work. In active
listening, communication is a vibrant, two-way process that
involves high levels of attentiveness, clarification, and message
processing. In active listening, you not only hear and react to
the words being spoken but also paraphrase, clarify, and give
feedback to the speaker about the messages being received.
Barriers to Effective Listening

Despite its importance in promoting effective communication,
active listening is often neglected. Many factors contribute to
difficulties in listening:
■ Physiological limitations. Listening can be fatiguing. We are
visual beings—about 80 percent of learning occurs though sight.
Because the eye is victorious over the ear in the constant
process of providing stimulation to the mind, many people don’t
know how to be effective listeners.
■ Speaking/listening gap. The average person speaks at a rate of
120–150 words per minute (wpm), while our brains have the
capacity to process information at 275–300 wpm. We can think
at up to 500 wpm, which is why it is easy to tune in and out of
conversations. Optimal speaking rate is about 275–300 wpm
even though the other person’s thinking rate is higher. One
strategy to address this is to develop additional presentation
techniques to hold the other person’s full attention.
■ Inadequate background information. Most listeners hate to
admit when they haven’t heard all of the information necessary
to engage in conversation, so they stumble along hoping to
catch up. They seldom do.
■ Selective memory. Some employees treasure every accolade
and never hear a single criticism. Others hear only the
complaints and never the praise. We have a tendency to hear—
and remember—what we want. There’s a reason ad agencies run
commercials over and over on TV and on the Web. Without
reinforcement of key messages, it’s easy to forget entirely or to
remember only selectively what a company has paid millions for
you to remember. Interestingly, steps taken to increase viewers’
recall, such as adding an attractive spokesperson or using flashy
video, result in viewers remembering the attractive person and
not the product or what it can do for them!

■ Selective expectation. If you expect dishonesty, poor work
attitudes, or inattention, you’ll probably get them. This is an
example of the “self-fulfilling prophecy.” Many employees
expect not to be listened to. So many managers are preoccupied
with im- mediate tasks and seldom have the time to devote to
individual employee concerns that employees become
accustomed to not being heard and understood. Often, they’ll
just give up and resign themselves to the short, nonattentive
interactions with managers to which they’ve become
accustomed, or withhold information, expecting it wouldn’t be
attended to anyway.
■ Fear of being influenced or persuaded. Some managers hold
certain beliefs so dear to their hearts that they are biased—
unable to entertain another’s point of view about a matter.
Typically, managers who feel this strongly about an issue have
a tendency to turn off speakers who dispute their cherished
beliefs even before the position is fully explained.
■ Bias and being judgmental. When you don’t like a person, it’s
hard to hear what he or she says. Sometimes this bias is based
on wrong or incomplete information, such as “She’s only 17,
what could she know?” or “He’s a bigot, so why should I listen
to him?” When we make a negative judgment about the speaker,
we typically stop or severely curtail our desire to listen to the
speaker.
■ Boredom. Thought processes are four to five times the usual
speed of speech. When you can guess what an employee is
going to say seconds or even minutes before he or she speaks,
your thoughts wander. When you return, the speaker may have
gone on an unexpected track whose beginnings you lost and
whose point you never do understand.
■ Partial listening and distractions. You may hear the literal
words, but miss the con- notation, facial expressions, or tone of

voice. In essence, you get only part of the message. Perhaps you
were trying to remember an important point when an employee
interrupts to ask if he or she can leave to deal with an
emergency at home. Chances are you didn’t give that employee
your full attention—even though your empathetic response to
the situation would have gone a long way.
■ Rehearsing. Many of us use the time during another’s talking
to come up with a bulletproof rebuttal. If you do this, you aren’t
really listening. Sometimes we are so intent on winning an
argument that the conversation veers in a different direction
during our “rehearsal,” resulting in our losing the segue for and
impact of our carefully crafted rebuttal.
■ Selective perception. Perception is the process by which you
take in and process stim- uli according to your own experiences
or attitudes. As such, you create your own real- ity, apart from
what may actually be occurring. Since communication has a
great deal of room for individual interpretation, from the
meaning of words to the interpretation of nonverbal signals,
your perception can easily distort the true message or its intent.
Perception can be influenced by a number of factors such as
your needs, opinions, personality, education, or environment.
Selective perception is a process in which you select or pay
attention to only that information that adheres to or reinforces
your own beliefs, views, or needs, causing severe distortion of
messages.
■ Interference from emotions. Communication is susceptible to
interference by emotions. Though we use communication to
express our emotions, not everyone is able to understand,
control, or explain his or her feelings adequately or fully.
Emotions are neither right nor wrong, but rather an expression
of human reactions. By observing nonverbal cues, you are better
able to interpret the true level and type of others’ emotional
states. You can then utilize empathy to neutralize emotional

responses, paving the way to begin work on understanding the
content of the communication. The emotional state of both the
sender and the receiver must be considered in eliminating
problems in the communication process.
Nonverbal communication is conveying meaning or expressing
feelings consciously or subconsciously through means other
than words. Since most of us are visually oriented and live in an
environment dominated by visual images, it is not surprising
that research indicates that over 55 percent of interpersonal
communication is conveyed nonverbally.
Nonverbal communication cues fall into two broad classes.
Nonverbal visual cues include facial expressions, eye blinks,
eye contact, gaze aversion, nodding, smiles, postural shifts,
physical positioning, and other bodily behaviors.
Paraverbal communication cues include aspects related to
speech, such as pitch, pauses, tone of voice, inflection, and
voice volume. How often have you experienced talking with
someone who says he or she is listening even though the person
is watching television or texting? Or having someone say they
agree to something while shaking their head in disagreement?
Conversely, have you experienced empathy from someone as
evidenced by the person nodding in support rather than sharing
their agreement with you verbally? We often communicate
nonverbally in ways that contradict what we’re saying verbally.
Nonverbal messages are often sent sub-consciously, leading
others to believe they hold more of the true meaning than the
verbal message.

DECISION MAKING MATRIX
Use the following table to choose four mutually exclusive
options to pursue in the first year after your graduation from
college. In other words, what will you do with your life after
you graduate?
Step 1. At the bottom of this page, briefly describe each option.
Step 2 In the column entitled “Decision Factor,” briefly
describe the aspect or criterion that differentiates each choice.
For example, one decision factor might be “income in first
year.” Another might be “intellectual challenge,” and so on.
Bear in mind that each decision factor must be applicable (but
not necessarily the best) for each outcome. For example, if one
outcome option is to join the army, you cannot use a decision
factor like “overcome fear of guns.” If you did, the only
possible column that could get the check mark would be the one
for “Join the army.”
Step 3: Using the criteria in step 2, place an X in the column for
each row that will most thoroughly fulfill that criterion/factor.
That is, which option is the best for that decision factor?
Step 4: Add the number of X’s in each column and record that
number at the bottom of the table in each column.
I WOULD PREFER some decisions factors would be: Getting a
job as international student (going to Optional practical training
-OPT) and some others you decide.
Options ABCD TOTALS:

Decision Factor
options
A
B
C
D

Option A:
_____________________________________________________
_________________ Option B:
_____________________________________________________
_________________ Option C:
_____________________________________________________
_________________ Option D:
_____________________________________________________
_________________
Questions to answer also :
Based on your work with the decision-making process
1. Did you find it relevant/useful?
2. Would you use it regularly at work or personal? Why or why
not?

Source: This exercise was developed and contributed to this
book by Dr. Brian K. Miller, Assistant Professor, Management
Program, College of Business, James Madison University,
Harrisonburg, Virginia (2004).
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Homework statistics
Title: Homework statistics chapter 9
Name: Date:
Introduction: The most important application of statistics is
describing the data using descriptive and inferential statistics.
The two main branches are descriptive and inferential statistics.
People who do not have any formal training in statistics are
more familiar with inferential statistics than with descriptive
statistics. (Walpole, 1982). The descriptive statistics is the type
of statistical analysis which helps to describes about the data in
some meaningful way. The statistics is used to describe
quantitatively about the important features of the data or
information. The descriptive statistics gives the summaries of
the
given sample as well as the observations done. These summaries
or descriptions can either be graphical or quantitative. For
example the data about Framingham Heart Study dataset
provided, using descriptive summary we can make some logical
conclusion about the data, The descriptive summary is given
below for both male and female. For male data set:
For Female data set:
In this part using the provided Dataset, I will compare the risk
factors in men and women where I will use the following patient
characteristics: Age, systolic blood pressure, diastolic blood
pressure, use of anti-hypertensive medication, current smoker,
total
serum cholesterol, mg/dL, body mass index (BMI), and
diabetes, The means for each risk factor is given in the above
descriptive
summary table. 1) Hypothesis Test: Hypothesis tests are also
known as tests of significance which tests some claim for the

population by analyzing sample. In this paper we will focus on
hypothesis to test the raw and make a logical conclusion for
example The Framingham Heart Study dataset provided, using T
statistics. Null Hypothesis: H0 the risk factors for heart disease
listed as patient characteristics are not related to if the patient is
male or female in the Framingham Heart Study. Alternative
Hypothesis: H1 the risk factors for heart disease listed as
patient characteristics are related to if the patient is male or
female in
the Framingham Heart Study. Level of significance is 0.05 SEX
is coded 1=male and 2=female. The output of the test is:
The null hypothesis is rejected for SYSBP, AGE, DIABP,
CURSMOKE and DIABETES For these variables we concluded
that the risk
factors for heart disease listed as patient characteristics are
related to if the patient is male or female in the Framingham
Heart
Study. The null hypothesis is fail to reject for. TOTCHOL, BMI
and CIGPDAY For these variables we concluded that the risk
factors
for heart disease listed as patient characteristics are not related
to if the patient is male or female in the Framingham Heart
Study. References
Walpole, R. (1982). Introduction to Statistics. (3rd ed.).
Prentice Hall Publication. Downie, N. M. & Heath, R. W.
(1965).
Basic Statistical Methods (2nd ed.). Harper & Row Publisher
Reid, H. (2013, August). Introduction to Statistics. SAGE
Publication.
1
1

2
3 1
1
3
1
Student paper
The most important
application of statistics is
describing the data using
statistics. The two main
branches are descriptive
and inferential statistics.
People who do not have
any formal training in
statistics are more familiar
with inferential statistics
than with descriptive
statistics.
Original source
statistics hypothesis testing
and inferential statistics
The two main branches are
statistics People who do

not have any formal
more familiar with
with descriptive statistics
1
Student paper
some meaningful way. The
data or information. The
observations done. These
quantitative.
Original source
some meaningful way The

data or information The
observations done These
quantitative
Student paper
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slideserve
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slideserve
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2
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1) Hypothesis Test:
Original source
The alternative hypothesis
1
3
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Introduction To Statistics -
1
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Prentice Hall Publication.
Original source
Prentice Hall Publication
1
Student paper
(2nd ed.). Harper & Row
Publisher

Original source
(2nd ed.) Harper & Row
Publisher
3
Student paper
Original source
Introduction To Statistics -
Module 9
Critical Thinking Assignment (105 points)
Using the Framingham Heart Study Dataset, you will compare
the risk factors in men and women where you will use the
following patient characteristics: age, systolic blood pressure,
diastolic blood pressure, use of anti-hypertensive medication,
current smoker, total serum cholesterol, mg/dL, body mass
index (BMI), and diabetes, by determining the means for each
risk factor. Create a table that summarizes your results.
H0 The risk factors for heart disease listed as patient
characteristics are not related to if the patient is
male or female in the Framingham Heart Study. (Null
Hypothesis)
H1 The risk factors for heart disease listed as patient
characteristics are related to if the patient is male or female in
the Framingham Heart Study. (Alternative Hypothesis)
Steps of R Studio Analysis shown on page 67 in Introductory
Statistics with R.

> x <- rnorm (50)
> sd < (x)
> var < (x)
> median (x)
Steps of Excel Analysis
To conduct your analysis of the data, sort the data by the
Sex/Gender variable and sort by smallest to largest.
Compute the means and standard deviations for continuous
variables using AVERAGE(range) and STDEV(range) functions
– Compute n(%) for dichotomous variables using
COUNT(range) and COUNTIF (range, criteria) functions
modifying ranges accordingly.
Present your findings by cutting and pasting your results table
in a Word document that includes a title page, introduction, a
discussion where you interpret the meaning of the table, and a
conclusion. Your submission should be 3-4 pages to discuss and
display your findings.
Provide support for your statements with in-text citations from a
minimum of four scholarly, peerreviewed articles. Two of these
sources may be from the class readings, textbook, or lectures,
but the others must be external. The Saudi Digital Library is a
good place to find these sources and should be your primary
resource for conducting research.
Follow APA and Saudi Electronic University writing standards.
Review the grading rubric to see how you will be graded for
this assignment.
You are strongly encouraged to submit all assignments to the
TurnItIn Originality Check prior to
submitting them to your instructor for grading.
References
· Hazra, A., & Gogtay, N. (2016). Biostatistics series module 6:
Correlation and linear regression. IndianJournal of
Dermatology, 61(6), 593-601.
● Hazra, A., & Gogtay, N. )2017). Biostatistics series module

10: Brief overview of multivariate methods. Indian Journal of
Dermatology, 62(4), 358-366.
● Johnson, R. E., Kording, K. P., Hargrove, L. J., & Sensinger,
J. W. (2017). Adaptation to random and systematic errors:
Comparison of amputee and nonamputee control interfaces with
varying levels of process noise. PLoS One, 12(3), 1-19.
Retrieved from
https://doaj.org/article/c81b101b00284f2192fff26ea
a6a2142
Critical Thinking Assignment (105 points)
Using the Framingham Heart Study dataset provided, perform
the ANOVA multivariable linear
regression analysis using BMI as a continuous variable. Before
conducting the analysis, be sure that all participants have
complete data on all analysis variables. If participants are
excluded due to missing data, the numbers excluded should be
reported. Then describe how each characteristic is related to
BMI. Are crude and multivariable effects similar? What might
explain or account for any differences?
H0 The BMI is not related to the patient characteristics in the
Framingham Heart Study. (Null Hypothesis)
H1 The BMI is related to the patient characteristics in the
Framingham Heart Study. (Alternative
Hypothesis)
Upload the Framingham Heart Study dataset into R Studio.
(Refer to Chapters 7 (pp. 127-141) & 12 (pp. 207-209) in
Introductory Statistics with R or pages 111-122 in EXCEL
Statistics: A Quick Guide).
Excel instructions:
Exclude participants with missing data on analysis variables
(age, sex, systolic blood pressure, total serum cholesterol,
current smoker, and diabetes = cleaning the data). Conduct the
simple linear regression (ANOVA) by using the Excel
Regression tool in the Data Analysis Toolpak. Remember SEX

is
coded 1=male and 2=female.
RStudio instructions:
Exclude participants with missing data on analysis variables
(age, sex, systolic blood pressure, total serum cholesterol,
current smoker, and diabetes = cleaning the data). Conduct the
simple linear regression (ANOVA) by using RStudio you will
use the t.test () command and the summary () command to get
your mean and standard deviation.
Present your findings in a Word document by copying and
pasting the ANOVA table into the document.
Your paper must include a title page, an introduction, a
discussion where you interpret the meaning of the ANOVA test,
and a conclusion. Your submission should be 3-4 pages to
discuss and display your
findings.
Provide support for your statements with in-text citations from a
minimum of four scholarly, peerreviewed
articles. Two of these sources may be from the class readings,
textbook, or lectures, but the
others must be external. The Saudi Digital Library is a good
place to find these sources and should be your primary resource
for conducting research.
Follow APA and Saudi Electronic University writing standards.
Review the grading rubric to see how you will be graded for
this assignment.
You are strongly encouraged to submit all assignments to the
TurnItIn Originality Check prior to
submitting them to your instructor for grading.

References
·
·
·
· Estabrook, R., Massey, S. H., Clark, C. A., C., Burns, J. L.,
Mustanski, B. S., & Wakschlag, L. S. (2016). Separating
family-level and direct exposure effects of smoking during
pregnancy on offspring externalizing symptoms: Bridging the
behavior genetic and behavior teratologic divide. Behavior
Genetics, 46(3), 389-402.
● Li, R. H., Wacholtz, M. C., Barnes, M., Boggs, L., Callery-
D'Amico, S., Davis, A., . . . Bierer, B. E. (2016). Incorporating

ethical principles into clinical research protocols: A tool for
protocol writers and ethics committees. Journal of Medical
Ethics, 42(4), 229-
234.

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