Correlation & Regression Analysis using SPSSParag Shah
Concept of Correlation, Simple Linear Regression & Multiple Linear Regression and its analysis using SPSS. How it check the validity of assumptions in Regression
This project was a part of our coursework - Applied Regression Analysis.
In this project, our aim was to find the relationship between One Independent and Four dependent variable.
To understand how the followers are increases on twitter, so we took No of followers as our Independent variable and Years Since they joined, Number of years passed since that person has joined, Number of Photos and Videos posted and Number of People that person is following back as our dependent variable and performed Multiple linear regression analysis.
Correlation and regression.
It shows different aspects of Correlation and regression.
A small comparison of these two is also listed in this presentation.
Two-way ANOVA has many of the same ideas as one-way ANOVA, with the main difference being the inclusion of another factor (or explanatory variable) in our model.
In the two-way ANOVA model, there are two factors, each with its own number of levels. When we are interested in the effects of two factors, it is much more advantageous to perform a two-way analysis of variance, as opposed to two separate one-way ANOVAs.
Explaining correlation, assumptions,coefficients of correlation, coefficient of determination, variate, partial correlation, assumption, order and hypothesis of partial correlation with example, checking significance and graphical representation of partial correlation.
Correlation & Regression Analysis using SPSSParag Shah
Concept of Correlation, Simple Linear Regression & Multiple Linear Regression and its analysis using SPSS. How it check the validity of assumptions in Regression
This project was a part of our coursework - Applied Regression Analysis.
In this project, our aim was to find the relationship between One Independent and Four dependent variable.
To understand how the followers are increases on twitter, so we took No of followers as our Independent variable and Years Since they joined, Number of years passed since that person has joined, Number of Photos and Videos posted and Number of People that person is following back as our dependent variable and performed Multiple linear regression analysis.
Correlation and regression.
It shows different aspects of Correlation and regression.
A small comparison of these two is also listed in this presentation.
Two-way ANOVA has many of the same ideas as one-way ANOVA, with the main difference being the inclusion of another factor (or explanatory variable) in our model.
In the two-way ANOVA model, there are two factors, each with its own number of levels. When we are interested in the effects of two factors, it is much more advantageous to perform a two-way analysis of variance, as opposed to two separate one-way ANOVAs.
Explaining correlation, assumptions,coefficients of correlation, coefficient of determination, variate, partial correlation, assumption, order and hypothesis of partial correlation with example, checking significance and graphical representation of partial correlation.
Fundamental of Statistics and Types of CorrelationsRajesh Verma
Fundamental of Statistics and Types of Correlations. Pearson r, Point Biserial, Phi Coefficient, Biserial, Tetrachoric, Spearman Rank Difference, Kendall's tau, Inferential Statistics, Descriptive Statistics
El clàssic llibre de Pau Parassols i Pi sobre el Santíssim Misteri i el monestir de Sant Joan de les Abadesses. Escanejat del llibre original, primera edició. Història de Catalunya.
A cornerstone on the history of Sant Joan de les Abadesses and its monastery, focused on the Holy Mystery
The liaison nurse at the Hospital Clinic of Barcelona. A description of the project as it was in the year 2008. The liaison nurse was dedicated to manage patients at risk as an intermediary between primary care and hospital care. Language: Catalan
Description of the quality system programme in a group of practices in Catalonia. Key words: quality, EFQM, indicators. Language: English. Please look for my article published in Quality in Primary Care
Objectives and activity indicators. Applied to a healthcare company. Might be of interest for other non-healthcare companies. Language: Catalan and English
A summary of the famous tale by Lewis Carroll, dedicated to resident doctors and young researchers. Carroll was a mathematician; in my opinion, the book is NOT a non-sense but a highly scientific sense. The history is summarized in Catalan and the most significant (for purpose) parts are copied and pasted. The most relevant sentences are highlighted in red. This is an introduction to the course of clinical reasoning
History of reasoning, that is, of Western philosophy applied to medical science. An addenda to my other presentation "history of clinical reasoning". Tools for resident doctors and medical students. Language: Catalan
History of clinical reasoning. How medical ideas evolved across centuries. A comprehensive overview of medical knowledge. Language. Catalan with notes in English and French
Designs of epidemiological studies. Fundamentals of epidemiology for resident doctors and young researchers. Types of epidemiological studies, strengths and weaknesses. Language: Catalan
We are homonyms but not relatives. Even more, I never knew him in person. But his work is of importance, indeed! A biography of this eminent radiologist. Language: Catalan
This text describes the main features of the Jamaican health system, the main issues in primary health care and the main topics of my contribution. Some pictures and leisure comments are also included. Language: Catalan
Prix Galien International 2024 Forum ProgramLevi Shapiro
June 20, 2024, Prix Galien International and Jerusalem Ethics Forum in ROME. Detailed agenda including panels:
- ADVANCES IN CARDIOLOGY: A NEW PARADIGM IS COMING
- WOMEN’S HEALTH: FERTILITY PRESERVATION
- WHAT’S NEW IN THE TREATMENT OF INFECTIOUS,
ONCOLOGICAL AND INFLAMMATORY SKIN DISEASES?
- ARTIFICIAL INTELLIGENCE AND ETHICS
- GENE THERAPY
- BEYOND BORDERS: GLOBAL INITIATIVES FOR DEMOCRATIZING LIFE SCIENCE TECHNOLOGIES AND PROMOTING ACCESS TO HEALTHCARE
- ETHICAL CHALLENGES IN LIFE SCIENCES
- Prix Galien International Awards Ceremony
Pulmonary Thromboembolism - etilogy, types, medical- Surgical and nursing man...VarunMahajani
Disruption of blood supply to lung alveoli due to blockage of one or more pulmonary blood vessels is called as Pulmonary thromboembolism. In this presentation we will discuss its causes, types and its management in depth.
Explore natural remedies for syphilis treatment in Singapore. Discover alternative therapies, herbal remedies, and lifestyle changes that may complement conventional treatments. Learn about holistic approaches to managing syphilis symptoms and supporting overall health.
Ethanol (CH3CH2OH), or beverage alcohol, is a two-carbon alcohol
that is rapidly distributed in the body and brain. Ethanol alters many
neurochemical systems and has rewarding and addictive properties. It
is the oldest recreational drug and likely contributes to more morbidity,
mortality, and public health costs than all illicit drugs combined. The
5th edition of the Diagnostic and Statistical Manual of Mental Disorders
(DSM-5) integrates alcohol abuse and alcohol dependence into a single
disorder called alcohol use disorder (AUD), with mild, moderate,
and severe subclassifications (American Psychiatric Association, 2013).
In the DSM-5, all types of substance abuse and dependence have been
combined into a single substance use disorder (SUD) on a continuum
from mild to severe. A diagnosis of AUD requires that at least two of
the 11 DSM-5 behaviors be present within a 12-month period (mild
AUD: 2–3 criteria; moderate AUD: 4–5 criteria; severe AUD: 6–11 criteria).
The four main behavioral effects of AUD are impaired control over
drinking, negative social consequences, risky use, and altered physiological
effects (tolerance, withdrawal). This chapter presents an overview
of the prevalence and harmful consequences of AUD in the U.S.,
the systemic nature of the disease, neurocircuitry and stages of AUD,
comorbidities, fetal alcohol spectrum disorders, genetic risk factors, and
pharmacotherapies for AUD.
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Ve...kevinkariuki227
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
TEST BANK for Operations Management, 14th Edition by William J. Stevenson, Verified Chapters 1 - 19, Complete Newest Version.pdf
4. INFERENCE FORM A SAMPLE
MEAN
The mean of the sampling distribution of
means is the true population mean
Its standard deviation is the population
standard deviation divided by the square
root of the sample (it is called the standard
error). Mesura la precisió de la meva
mostra.
Confidence interval: estimated mean +/-
multiplier x standard error of the estimate
5. INFERENCE FORM A SAMPLE
MEAN
95% confidence interval: we are 95% confident
that the true mean in the population lies between
this interval
Z and t: using tables we can obtain a probability
for the calculated value
P-value: is the area under the curve
corresponding to values outside the range (-z,z;
-t,t). That is, the area in the tails of the
distribution gives the probability of observing the
more extreme values
6. INFERENCE FORM A SAMPLE
MEAN
Null hypothesis: the two population means
are the same
Alternative hypothesis: the two population
means are not the same
Hypothesis test: we calculate the
probability of obtaining the observed data
if the null hypothesis were true (“larger
accept, smaller reject”)
7. COMPARISON OF TWO MEANS
Paired samples occur when the individual
observations in the first sample are
matched to individual observations in the
second sample. For quantitative data this
usually occurs when there are repeated
measurements on the same person
Unpaired data occur when individual
observations in one sample are
independent of individual observations in
the other
8. COMPARISON OF TWO MEANS
Paired data: we calculate the difference between
the first and second measurements, then the
mean difference, the standard deviation of the
differences and the standard error of the mean
difference. We can also calculate the probability
that, on average, there is no difference between
the paired observations in the population using a
hypothesis test. The null hypothesis is that the
mean population difference is zero. We assume
that the differences are normally distributed with
a mean of zero
9. COMPARISON OF TWO MEANS
Unpaired data: we calculate the difference
between two independent means, the standard
deviation in two independent samples, and the
standard error of the difference in two
independent means, which is a combination of the
standard errors of the two independent sample
distributions. Using the standard error of the
difference in means, we can calculate the
confidence interval for the estimated difference
and test whether it is significantly different from
zero. We can use a z test in the same way as we
did before for a single sample mean of paired
samples
10. COMPARISON OF TWO MEANS
When the sample size is small, we use the
t-distribution to calculate confidence
intervals and test hypothesis (either paired
or unpaired data).
To compare independent samples,
however, we need to assume that the
variances of the two populations are the
same.
11. INFERENCE FROM A SAMPLE
PROPORTION (7)
The sampling distribution of a proportion is
approximately Normal when the sample is
large
The SE of a sample estimate is equal to
the standard deviation divided by √n.
95% CI= p ± 1.96 x SE(proportion)
95% CI= p ± 1.96 √p(1 – p) / n
12. INFERENCE FROM A SAMPLE
PROPORTION (7)
If we want to assess whether the
population proportion has a certain value:
1. First we should state the Null Hypothesis
Π= Π0
2. Then we state the Alternative Hypothesis
Π≠ Π0
3. Finally we compute the test statistic
z= p - Π0 / SE(Π)
13. INFERENCE FROM A SAMPLE
PROPORTION (7)
Remember: we calculate the SE(Π)
assuming the null hypothesis to be true.
Remember: these methods are only
reliable if the sample is large (say, if the
proportion is less than 0.5 and the number
of subjects with the disease is 5 or more
When these conditions are not satisfied,
we use the binomial distribution.
14. COMPARISON OF TWO
PROPORTIONS (8)
We want to make comparisons between the
proportions in two independent populations
(case – control study, cohort study, clinical
trial).
For a large sample we can use a normal
approximation to the binomial distribution
When comparing proportions for
independent samples, the first thing we do
is calculate the difference between the two
proportions
15. COMPARISON OF TWO
PROPORTIONS (8)
The analysis for comparing two independent
proportions is similar to the comparison of
two independent means
The standard error for the difference in two
proportions is a combination of the standard
error of the two independent distributions
Hypothesis test: we use a common
proportion (because the two proportions are
supposed to be the same) and the pooled
standard error
16. ASSOCIATION BETWEEN TWO
CATEGORICAL VARIABLES
When we want to examine the relationship
between two categorical variables we
tabulate one against the other. This is
called a two – way table (also known as
cross – tabulation)
An association exists between two
categorical variables if the distribution of
one variable varies according to the value
of the other
17. The chi – squared test for the 2x2 tables is
identical to the z-test for comparing 2
proportions. The value z is the square root
of chi-squared.
The Fisher’s exact test may also be used.
ASSOCIATION BETWEEN TWO
CATEGORICAL VARIABLES
18. CORRELATION (10)
Do the values of a variable tend to be
higher (or lower) for higher values of the
other? CORRELATION
What is the value of one of the variables
likely to be when we know the value of the
other? LINEAR REGRESSION
19. CORRELATION (10)
Correlation is used to study the possible linear
(straight line) between two quantitative
variables. This tells how much the two
variables are associated
To measure the degree of linear association
we calculate a correlation coefficient
The standard method is to calculate the
Pearson’s correlation coefficient, denoted r
20. Measures the scatter of the points around
an underlying linear (straight line trend)
Can take any value from -1 to +1
If there is no linear relationship then the
correlation is zero. But be careful, there
can be a strong non – linear relationship
between two variables.
CORRELATION (10)
Pearson’s correlation coefficient
21. CORRELATION (10)
We can think of the square of r as: the
proportion of the variability in the y variable
that is accounted for by the linear
relationship with the y variable
Assumptions for use of correlation:
the two variables have an approximately
Normal distribution
all observations should be independent
Causation cannot be directly inferred from a
strong correlation coefficient
22. LINEAR REGRESSION (11)
Regression studies the relationship
between two variables when one of them
depends on the other. This also alows one
variable to be estimated given the value of
the other.