#pearson correlation coefficient

[correlationcoefficien]

Understanding Correlation Coefficient: A Tool for My Browser App Store Users

Learn what correlation coefficient is and how it can help you make informed decisions in My Browser App Store. Read on for a comprehensive guide.

Understanding Correlation Coefficient: A Tool for My Browser App Store Users

What is Correlation Coefficient?

Correlation coefficient is a statistical measure that measures the relationship between two variables. In simpler terms, it tells you how closely two variables are related. Correlation coefficient ranges from -1 to 1. If the correlation coefficient is 1, it means that the two variables are perfectly positively correlated. If the correlation coefficient is -1, it means that the two variables are perfectly negatively correlated. A correlation coefficient of 0 indicates that there is no correlation between the two variables.

How Does Correlation Coefficient Help in My Browser App Store?

In My Browser App Store, correlation coefficient can help you make informed decisions about which apps to download. For example, let's say you're looking for a new productivity app. You can use correlation coefficient to see which apps are most closely related to productivity. You can also use correlation coefficient to see which apps have a positive or negative impact on your device's performance. By using correlation coefficient, you can make more informed decisions about which apps to download and which to avoid.

How to Calculate Correlation Coefficient?

Calculating correlation coefficient can be a bit complicated, but it's not impossible. There are several methods you can use to calculate correlation coefficient, including the Pearson correlation coefficient and the Spearman correlation coefficient. The Pearson correlation coefficient is used to measure the strength of a linear relationship between two variables, while the Spearman correlation coefficient is used to measure the strength of a non-linear relationship between two variables.

Conclusion

Correlation coefficient is an important statistical measure that can help you make informed decisions in My Browser App Store. By understanding what correlation coefficient is and how it works, you can use it to your advantage when choosing which apps to download. Whether you're looking for a productivity app or trying to improve your device's performance, correlation coefficient can be a useful tool in your decision-making proc.

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Company Name: My Browser App Store

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[acd1sz]

Week 3: Generating a Pearson Correlation Coefficient

The process of generating a correlation coefficient is used to examine the dependence between a quantative explanatory variable and a quantative response value.

Q->Q

For this course, I will perform a Pearson correlation and analyze the results.

A correlation can be made visually with a scatterplott. With this one can see a general form /shape of the value mid line.

A pearson correlation coefficient is generally useful only when looking at linear shaped scatterplots, for curved it gives no good significant findings.

The coefficient “r” can range from -1 to +1, while a value next to (+/-)1 indicates a perfect relation between the variables, whilst a value near 0 indicate a very weak connection between the variables examined.

For my test I will use the gapminder dataset, as it already contains several quantative variables sorted to different countries of origin for the collected data.

I will examine to correlations, first between braest cancer rate “breastcancerper100TH”  and alcohol consumption rate “alcconsumption”, second between breast cancer rate and rate of individuals living in urban areas “urbanrate“.

To perform that with SAS this code is used:

The Proc Corr statement provides the correlation between (in this case 3) variables in an output table.

For visuallizing I included scatterplots for both relations I try to examine (breastcancer100TH as explanatory variable on the x-axis for both plots).

The output is the following:

We find for relation between breast cancer and alcohol consumption a r=0.493 with a p-value of <0.0001 indicating a moderate positive relation.

For the relation between breast cancer and urbanrate a r=0.57 with a p-value of <0.0001 indicating a moderately strong relation between the variables.

 When we square the examined r value, we get Coefficient of Determination (RSqaure) which tells us, how many values of the second variable we can predict with the first variable.

Here r-square for breastcancer and alcconsumption is 0.243, so we could predict about 24.3% of the breast cancer cases with the alcohol consumption rate.

The r-square for breastcancer and urbanrate is 0.325, so we could predict about 32.5% of the breast cancer cases with the urban rate.

So we can say that the higher the urbanrate or the alcohol consumption rate, the higher the breast cancer rate will be.

[darkmaga-retard]

Despite the endless barrage of propaganda by the mainstream media and global elites, it turns out that man-made fossil fuel emissions have “zero impact” on climate change.

A groundbreaking new study challenges the belief that human emissions are the primary driver of increasing atmospheric carbon dioxide (CO2) concentrations. The research, published in the Science of Climate Change, concludes that sea surface temperatures (SST) play a far more significant role than anthropogenic (human-caused) factors in determining annual changes in atmospheric CO2 levels.

Naturalnews.com reports: Using multivariate analysis and publicly available data from leading climate and energy organizations, Dao Ato’s study compares the impacts of sea surface temperature and human emissions on atmospheric CO2 concentrations. The analysis spanned from 1959 to 2022 and employed multiple linear regression techniques to evaluate the influence of sea surface temperature and human CO? emissions on the annual increase in atmospheric CO2.

The results reveal that sea surface temperature data, derived from NASA and the UK-HADLEY Centre datasets, was the most accurate predictor of CO2 concentrations. The regression model incorporating sea surface temperature explained approximately 66% of the variance in annual CO2 increases post-1959, with a remarkably high correlation between predicted and actual CO2 levels. The study found a Pearson correlation coefficient of 0.9995 between the CO2 concentrations predicted using sea surface temperature data from the UK-HADLEY Centre and actual measurements from NOAA, with a minimal prediction error of 1.45 ppm in 2022. In contrast, human CO2 emissions showed no significant correlation with annual changes in atmospheric CO2.

Ato’s study also found that human methane emissions, despite rising dramatically in recent decades, have not contributed to rising methane concentrations in the atmosphere through the 21st century.

[ozzgin]

How you come with that fuckin lore sheet (like not even in my messiest dreams I could connect all of your writings 😭😭)

There is a small chance that my need to organize literal figments of imagination might be related to my ‘tism. Can’t say for sure, I’ll have to run a correlation test in SPSS and get back to you once I have a Pearson coefficient.

I’m also uncertain whether you’re referring to the reader guide featuring my old stories, or the recent human kink compilation (I’m guessing the latter), but it sadly doesn’t change the verdict.

[transsexual-dandelions]

hey!! im gonna humor you (but also im just really interested), how’s your capstone? it sounds compelling:>

i think its SO cool and interesting but I may be biased i've spent the last like,, six months on this researching and planning and im starting statistical analysis next week (planning to do a t test several times over, if I have to do ANOVA i'll cry. I could settle for pearson correlation coefficient theyre not terrible)

(can you tell im a STEM guy through and through)

short version: i'm studying how often a pair of jeans should be washed based on accumulation of bacteria over a period of time, the project itself was conducted through seven consecutive days, being worn for a minimum of three hours a day, however I do believe this information could transfer over to "wears" instead, so instead of three consecutive days you could wear them three times over maybe two weeks and get similar results (in theory, this is inference made based on my decent amount of research)

longer version:

the whole reason clothes need to be washed beyond removing stains is because of odor. how does that odor get there? bacteria, baby! there is a large amount of bacteria residing on your skin (which is wonderful for your immune system, first line of defense!). The amount, types, and variety/biodiversity of bacteria is determined by many factors, including but not limited to pH, temperature, and moisture. how do these bacteria survive on your skin? sweat! sweat is inherently neutral and has no odor, it gains an odor as a byproduct of bacteria metabolizing it.

this odor is transferred over to clothes through the sweat, it wics into the textile and attaches to the fibers, bringing the bacteria along with it, they will just exist together for the most part, creating a malodor but nothing intense for now, the real fun part happens when the sweat dries. as the sweat dries, the solutes and compounds that the bacteria metabolize are severely concentrated, so the bacteria can access more of it quicker, giving it much more energy than it previously had access to, allowing irreversible adhesion to take place, which when built up can cause a lingering malodor, discoloration, loss of textile strength (thinning) not related to natural wear and tear,

the material a textile is made of will impact how sweat, sebum, and bacteria interact. most clothes are made with any combination of these three things: cotton, polyester, and spandex/elastane, each with different properties and attributes causing. ive read some journals/studies suggesting spandex to have a degree of antimicrobial properties, and for denim/100% cotton to have a degree of antifungal properties

cotton and polyester differ in many ways due to being natural vs synthetic, with one being hydrophilic and the other hydrophobic, different hygroscopicity, and general structure, as well as preparation for textile making. this results in different sebum distributions as it dries, polyester causing a uniform distribution with no respect for being face up/face down, while with cotton it dries into spherical droplets, face down, which could lead to implications and suggestions with and about bacterial behavior and odor formation (different bacteria cause different odors!)

the dyes used for clothes can also impact bacteria, it can accelerate or decrease bacterial growth/quantity/malodor, for example black jeans will gain a unique malodor that blue jeans may have. different dyes and dying processes can impact the integrity of the textile to begin with, such as stonewashing or acid wash which compromise the health of it, and traditional/classic indigo dye will strengthen/better it, with its natural antimicrobial properties. ultimately, different dyes will also have chemical properties that will affect how bacteria, sweat, and sebum interact and absorb (adsorb?)

jeans can have a different washing rate than say, t shirts, for a few reasons

one, denim is in reference to how the fibers are weaved to form the textile itself, a different structure means sweat and sebum (oil from your skin) will interact differently two, where and how you sweat! sure you sweat everywhere for the most part, but it accumulates and acts differently in some places compared to others, the most prominent sweating is at the armpits so it has all these processes happening quickly creating a greater need, compared to your legs where it may not be happening as quickly, if at all

so with all that background knowledge, what did I actually test?

I had a person wear the same pair of jeans for seven days, minimum of three hours a day, medium wash, indigo dye, 100% cotton. i took samples on day 0, 1, 2, 6, and 7. I could not get data for days 3-5 due to reasons out of my control, but it ultimately may not matter because there was no seemingly visible difference in amount of bacteria compared to day zero and one until day seven! at day seven it was intense, easily reaching fifty colonies per plate (two sections per day, each section gets played five times), compared to the one to ten colonies, <= 2mm on the first few days,

i predict that my statistical analysis will not show a significant difference (probably p value <0.05) until day seven, but i may be entirely wrong either way, i think its going to be very interesting!

in theory it would have applications in determining optimal washing frequency, based on a variety of factors (significant physical activity or sweating would decrease amount of time between washes) to create the longest possible lifespan of the jeans, especially with the effects of fast fashion and planned obsolescence

washing too frequently can also negatively impact the lifespan of a textile and make it degrade much quicker, and not washing it frequently enough will cause bio deterioration

I would love to do this project again another time, perhaps with more people, or exploring other %make ups, brands, and dyes/colors

[victoriajohnson2556]

Unraveling Data Mysteries: A Beginner's Guide to SPSS Exploration and Analysis

Statistics plays a pivotal role as the bedrock of empirical research, offering priceless insights into the intricate relationships that exist among variables. Within the realm of graduate-level statistical analysis, we navigate the labyrinth of data using the robust Statistical Package for the Social Sciences (SPSS). Our primary objective is to unearth patterns and relationships among variables, amplifying our comprehension of the underlying data structures. Join us as we embark on an illuminating journey through two intricate numerical questions that not only challenge but also showcase the potential of SPSS in untangling the multifaceted complexities of statistical analysis. If you are seeking assistance or struggling with your SPSS assignment, rest assured that this exploration might provide the help with SPSS assignment you need.

Question 1:

You are conducting a research study to analyze the relationship between students' hours of study and their final exam scores. You collect data from a sample of 100 graduate students using SPSS. The dataset includes two variables: "Hours_of_Study" and "Final_Exam_Score." After importing the data into SPSS, perform the following tasks:

a) Calculate the mean, median, and mode of the "Hours_of_Study" variable.

b) Determine the range of the "Final_Exam_Score" variable.

c) Generate a histogram for the "Hours_of_Study" variable with appropriate bins.

d) Conduct a descriptive analysis of the correlation between "Hours_of_Study" and "Final_Exam_Score" variables.

Answer 1:

a) The mean of the "Hours_of_Study" variable is 15.2 hours, the median is 14.5 hours, and the mode is 12 hours.

b) The range of the "Final_Exam_Score" variable is 40 points.

c) The histogram for the "Hours_of_Study" variable is attached, indicating the distribution of study hours among the graduate students.

d) The correlation analysis shows a Pearson correlation coefficient of 0.75 between "Hours_of_Study" and "Final_Exam_Score," suggesting a strong positive correlation between the two variables.

Question 2:

You are conducting a multivariate analysis using SPSS to examine the impact of three independent variables (Variable1, Variable2, Variable3) on a dependent variable (Dependent_Variable). The dataset includes 150 observations. Perform the following tasks:

a) Provide the descriptive statistics for each independent variable (mean, standard deviation, minimum, maximum).

b) Conduct a one-way ANOVA to determine if there are significant differences in the mean scores of the Dependent_Variable based on the levels of Variable1.

c) Perform a regression analysis to assess the combined effect of Variable2 and Variable3 on Dependent_Variable.

Answer 2:

a) Descriptive statistics for each independent variable are as follows:

Variable1: Mean = 25.3, SD = 3.6, Min = 20, Max = 30

Variable2: Mean = 45.8, SD = 5.2, Min = 40, Max = 50

Variable3: Mean = 60.4, SD = 7.1, Min = 55, Max = 70

b) The one-way ANOVA results indicate a significant difference in the mean scores of Dependent_Variable based on the levels of Variable1 (F(2, 147) = 4.62, p < 0.05).

c) The regression analysis reveals that Variable2 and Variable3 together account for 65% of the variance in Dependent_Variable (R² = 0.65, p < 0.001), suggesting a substantial combined effect of these variables on the dependent variable.

Conclusion:

SPSS serves as a powerful tool for unraveling the intricacies of statistical relationships. From exploring correlations between study hours and exam scores to conducting multivariate analyses, our journey through these graduate-level questions demonstrates the versatility and depth that SPSS brings to statistical exploration. As we navigate the depths of data analysis, we gain valuable insights that contribute to the ever-evolving landscape of statistical research.

[felidaereverse]

you want to compile datasets and create scatter plots and interpret trends you want to identify outliers and find lines of best fit and standard deviation you want to calculate pearson correlation coefficients you want to calculate p-values you want to examine a z-score chart you are in love with both one and two tailed t-tests and ANOVAs and MANOVAs and regression analysis and many many more

YOU ARE ENTERING MY BEAUTIFUL STATISTICS WORLD

[geekysteth]

Master Statistics 101: Correlation

What is Correlation? Correlation indicates that as one variable changes in value, the other variable tends to change in a specific direction. For example, the height and weight of an individual can be correlated – which means if the person’s height is on the taller side of the curve, the weight would also be high. What is Correlation?What are correlation coefficients?Pearson’s correlation…

[guailanchai]

Generating and Interpreting Correlation Coefficient

In this blog entry, I'll demonstrate how to generate and interpret a correlation coefficient between two ordered categorical variables. We’ll use a hypothetical dataset where both variables have more than three levels. This is particularly useful when the categories have an inherent order, and we can interpret the mean values.

Hypothetical Data

Assume we have two ordered categorical variables:

Variable X: Levels are 1, 2, 3, 4 (e.g., Satisfaction level from 1 to 4)

Variable Y: Levels are 1, 2, 3, 4 (e.g., Performance rating from 1 to 4)

Here is a sample dataset:

Syntax for Generating Correlation Coefficient

R Syntax:

Python Syntax (using numpy):

Output

R Output:

Python Output:

Interpretation

The Pearson correlation coefficient between Satisfaction (X) and Performance (Y) is approximately 0.83. This positive correlation indicates a strong direct relationship between the satisfaction level and the performance rating.

The R-squared value, calculated as the square of the correlation coefficient, is 0.6889. This means that approximately 68.89% of the variability in Performance can be explained by the variability in Satisfaction.

Summary

Correlation Coefficient: 0.83, indicating a strong positive correlation.

R-squared: 0.6889, suggesting that a significant proportion of the variation in Performance is explained by Satisfaction.

This analysis highlights the strong relationship between the ordered categorical variables and shows how a higher satisfaction level tends to be associated with higher performance ratings.

[crimsonforensicscience]

Stature Reconstruction from Handprint Dimensions in an Adult Nigerian Student Population

Abstract

Background: On regular basis, crimes are committed and the perpetrators of such offences roam the streets unidentified because of insufficient evidence to connect the suspect to the crime. Therefore handprints hold a cardinal role in linking offenders to crimes and its correlation to stature cannot be undermined as it widens the prospect and precision of human identification in medico-legal investigations.

Objective: The objective of the current study is to derive regression models that will predict stature from hand prints parameters amongst Nigerian adults.

Subjects and method: This cross-sectional research comprises of a total sample size of 230(100 males and 130 females) healthy adult Nigerians, aged between 18 to 36 years. This study employed direct and indirect method to acquire handprints dimensions (Handprint Length, Breadth, palm print length and digit length of left and right hand) following standard procedures. The data derived were subjected to series of statistical analysis using Statistical Package for Social Sciences (SPSS version 20 Chicago Inc) including descriptive statistics, independent and paired sample t-test, Pearson moment correlation coefficient and Durbin Watson regression.

Results: The present results for stature records 176.36±8.13cm and 164.38±6.62 for males and females sample respectively. Values of handprints dimension showed a range of positive Pearson moment correlation coefficient (r) of 0.31 to 0.73 which represent weak to strong r value. The regressions formulas derive were observed to be more reliable in multiple linear regression equations when reconstructing stature than single linear equation due to different levels of standard error of estimates (SEE) and coefficient of determination (R2) using>99% accurate estimation rate of the equations.

Conclusion: The regression models derived can effectively reconstruct stature which may be useful to a forensic expert saddled with the task of human identification among disaster victims or crime scene.

Read More About This Article: https://crimsonpublishers.com/fsar/fulltext/FSAR.000551.php

Read More Crimson Publishers Google Scholar Articles: https://scholar.google.com/citations?view_op=view_citation&hl=en&user=BcljX0IAAAAJ&cstart=20&pagesize=80&authuser=1&cit crimsonpublishers ation_for_view=BcljX0IAAAAJ:_Ybze24A_UAC

[jedesvaz]

Data Analysis Tools: Week 3 Generating a Correlation Coefficient

Going back to the research question, lets try to find the correlation using PISA Score as the dependent variables and other variables like students in class, school size, Minutes of talked English per week and check how the results vary.

Summary

Students in class: Neither the scatterplot nor the Pearson's r coefficient show a strong relationship between the number of students in class and the PISA score. Despite of this, it tends to be positive, suggesting that classes with more students help them in PISA score. Moreover, according to the scatterplot, it seems that there is no difference of performance in PISA between small or big classes.

School size: It does not seem that the size of schools affects in PISA Score.

Minutes of talked English per Week. This is the biggest coefficient, but it is still weak.

[cristianrdc]

Data Analysis - Week4 - Moderation test using Pearson correlation

On week 4, for the analysis of the statistical interaction, the Pearson correlation approach was chosen to test the moderating variable (moderator). The dataset 'Gapminder' was selected.

The goal of this study was to verify the effect of the moderating variable on the relationship between the explanatory variable (X) and the response variable (Y).

Describing all variables:

Explanatory variabel (X) called "Income per Person": 'incomeperperson'

Response variabel (Y) called "Life expectancy": 'lifeexpectancy'

Third variable (the moderator) called "polity score": 'grp'. Here, three groups were created.

Exercise goal: Will the "polity score" (measure of a country's democratic and free nature) affect the strength of the relationship between "Income per Person" and "Life expectancy"?

"polity score" Group 1 (Low value):

Correlation Coefficient (r): 0,36162

p-Value: 0,0982

In low value group 1, the association between "Income per Person" and "Life expectancy" is 0,36162 (positive correlation) with a non significant p-value at 0,0982 (greater than 0,05). Therefore, the association between "Income per Person" and "Life expectancy" is not significant for low value polity score.

Mapping this result to a scatter plot diagram we can visualize the non-significant relationship:

"polity score" Group 2 (Medium value):

Correlation Coefficient (r): 0,45335

p-Value: 0,0020

For medium value group 2, the association between "Income per Person" and "Life expectancy" is 0,45335 (positive correlation) with a significant p-value at 0,0020 (less than 0,05). Which means, a significant association between "Income per Person" and "Life expectancy" for medium value polity score .

Mapping this result to a scatter plot diagram we can visualize the significant relationship:

"polity score" Group 3 (High value):

Correlation Coefficient (r): 0,68347

p-Value: <0,0001

Finally, for the high value group 1, the association between "Income per Person" and "Life expectancy" is 0,68347 (positive correlation) with a significant p-value <0,0001 (less than 0,05). We can confirm a significant association between "Income per Person" and "Life expectancy" for the high value polity score.

Mapping this result to a scatter plot diagram we can visualize the significant relationship:

Based on the results shown above, it was possible to see a a significant relationship between the "Income per Person" and "Life expectancy" among the medium and high values polity scores, whereas the oppositve was confirmed for low value polity score.

[darkmaga-retard]

Despite all the hysteria surrounding human emissions, it turns out that fossil fuel emissions have a negligible impact on atmospheric CO2 levels, especially when compared to natural phenomenon like natural fluctuations in sea surface temperatures. A groundbreaking new study challenges the long-standing belief that human emissions are the primary driver of increasing atmospheric carbon dioxide (CO?) concentrations. The research, published in the Science of Climate Change, argues that sea surface temperatures (SST) play a far more significant role than anthropogenic (human-caused) factors in determining annual changes in atmospheric CO? levels. This research calls into question every climate change agenda proposed by global governments and institutions.

Sea surface temperatures dictate atmospheric CO2 levels, not fossil fuel emissions

Using multivariate analysis and publicly available data from leading climate and energy organizations, Dao Ato's study compares the impacts of sea surface temperature and human emissions on atmospheric CO? concentrations. The analysis spanned from 1959 to 2022 and employed multiple linear regression techniques to evaluate the influence of sea surface temperature and human CO? emissions on the annual increase in atmospheric CO?.

The results reveal that sea surface temperature data, derived from NASA and the UK-HADLEY Centre datasets, was the most accurate predictor of CO? concentrations. The regression model incorporating sea surface temperature explained approximately 66% of the variance in annual CO? increases post-1959, with a remarkably high correlation between predicted and actual CO? levels. The study found a Pearson correlation coefficient of 0.9995 between the CO? concentrations predicted using sea surface temperature data from the UK-HADLEY Centre and actual measurements from NOAA, with a minimal prediction error of 1.45 ppm in 2022. In contrast, h

[academicstrive]

Essential Statistical Methods and Tools for Researchers

AcademicStrive #Researchers #ResearchArticle #OpenAccess

Key statistical methods are integrated into the research and data analysis sphere, and they are more and more being dependent on the precise utilization of the statistical methods to come up with the right interpretation of the insights. Academic Strive, as a Registered Open Access Publisher, is the one-stop-shop that researchers can refer to for the availability of the top-notch materials, which includes data sources, that they need for their projects. The blog article reviews the statistical methods that are made easy and the software technologies that support these analyses, which, in turn, make the research process richer.

Introduction to Statistical Analysis

Statistical Analysis is the process of the collection, organization, interpretation, and transmission of data. It gives the reason for proving scientific truths, which are strong, without any doubt. Consequently, bright whites of the eyes de-classify. At Academic Strive, we lay emphasis on statistical literacy as it is the learning curve for researchers and professionals from different fields.

Common Statistical Methods

Descriptive Statistics: Definition: Descriptive statistics are the ones that sum up the dataset and give a textual description of the main features of a dataset. Key Techniques: Measures of central tendency (mean, median, mode), measures of variability (range, variance, standard deviation). Application: It serves as a data snapshot, which informs the user of the data state.

Inferential Statistics: Definition: Inferential statistics make predictions or inferences about a population that has been sampled from the data that is in actuality available. Key Techniques: The hypothesis testing, confidence intervals, regression analysis, and ANOVA belong to the list. Application: As it is vital for investigating the relationships between variables as they make predictions.

Correlation and Regression Analysis: Definition: To check the shared situation between two or more variables, these ways are used. These methods assess the relationship between two or more variables. Key Techniques: The Pearson correlation coefficient, Spearman's rank correlation, linear regression, and multiple regression are some important methods of statistical methods. Application: It finds a lot of uses in the realm of the social science areas, particularly in economics and psychology, where it refers to uncovering the relationships and predicting the possible changes.

Non-Parametric Methods: Definition: They are non-parametric methods, that is, they do not make assumptions about distribution for the data. Key Techniques: Chi-square test, Mann-Whitney U test, Kruskal-Wallis test would be other measures of this research. Application: When no alternative hypotheses are possible, like when the data are non-normal. Software Tools for Statistical Analysis

SPSS (Statistical Package for the Social Sciences): Features: With advanced data techniques and the main thing being a user-friendly interface, it is hard to find a comparison for an SPSS product. There is, however, a comprehensive range of statistical tests and robust data management capabilities. (The) statistical package Usage: It is used not only in the social sciences, but also in the health sciences and marketing research as well.

R: Features: Extensive statistical high-level packages programming language as well as built-in extensive statistical analysis can be used with the R programming language. Usage: It is those people who are into statistics and data science that are the most likely to opt for it, because of its adaptability and hands-on approach.

SAS (Statistical Analysis System): Features: High-level! -d 8,9 Usage: This software is an effective tool mostly applied in clinical trials, treatment of diseases, pharmaceutical research, and as well as the case

Stata: Features: A logically  Usage: Widely used in economics, sociology, and epidemiology research.

Excel: Features: Widely accessible, simple and relatively effective for basic statistical analysis and data manipulation. Usage: However, in the context of

Conclusion

Mastery in statistical methodologies and tools for data analysis is a must for the researchers in quest for substantial and influential researches. Academic Strive is the ultimate provider to researchers giving open access to relevant and the latest information, which significantly uplifts their statistical skills and is compelling in real-world settings. As they project the right scientific methods and software, scientists can verify the reality and accuracy of their discoveries, thereby making a significant contribution to the advancement of knowledge in various fields. For more insights and resources, visit Academic Strive and explore our comprehensive range of academic publications and guides.

[crimsonpublishers-pediatrics]

Fear of the Second Wave of COVID-19 Pandemic and Quality of life in a Sample of Teenage Egyptian Female Students_ Crimson Publishers

Fear of the Second Wave of COVID-19 Pandemic and Quality of life in a Sample of Teenage Egyptian Female Students by Shewikar Farrag In Research in Pediatrics & Neonatology

Abstract Go to Objective: The study aimed to (1) determine the levels of quality of life and fear of the second wave of the Corona pandemic among a sample of adolescent students in the Egyptian society and (2) to study the extent of the pandemic fear contribution to predicting the two dimensions of quality of life (social and personal).

Methodology: Researchers used more than one instrument in order to assess participants’ fear and to explore its effect on their quality of life across various dimensions. Research Tool (I) An electronic survey was utilized with 435 female students, with an average age of 19.50, with a standard deviation of 1.46 years, while they utilized Research Tool II ‘’Amer’s quality of life scale” Amer AE [1] to assess participants’ quality of life (α=0.908), plus assessing the Fear of Corona Pandemic through the use of Research Tool III Amer Fear scale(2020c), which included 12 items (α=0.8971). data were analyzed using descriptive statistics, Pearson correlation coefficient and simple regression through the SPSS statistical package (V 26).

Results: Study results demonstrated low levels of quality of life, and moderate degree of fear of Corona pandemic as 73.10% of participants suffered from various degrees of fear symptoms ranged from medium to severe, plus a very weak positive correlation r= 0.107, p <0.05 between fear of The pandemic and the quality of social life, while showed a zero relationship between the quality of personal life and fear of the second wave of the pandemic contributed to explaining that 0.9% of the variance in the quality of social life, which is a very weak effect, while fear of the pandemic supported the quality of social life at a very low level. Recommendations: Research investigators recommend that Governmental Institutions need to raise community awareness and develop programs to reduce the level of pandemic fear among young people that have a specific impact on adolescent mental health and their future.

For more open access journals in Crimson Publishers please click on: https://crimsonpublishers.com/rpn/

[correlation-between-gdp-and-co2]

Data Analysis Tools – Week 4: Pearson Correlation with a Moderator

Topic

In this blog post, the association between income per person (explanatory variable) on oil consumption per person (response variable) under the moderator of total CO2 emissions of different countries is investigated.

Since the moderator (total CO2 emissions) is a quantitative variable, it is split into 2 categories. The first category includes half of the countries with the smaller CO2 emission (lower median = “1”). The second category includes half of the countries with the higher CO2 emission (upper median = “2”).

Results of the Pearson correlation under consideration of the moderator

Group 1: Countries in the lower median of CO2 emission

For countries that belong to the median with the lower CO2 emission (group “1”), the p-value of the association between income per person and oil consumption is approximately 0.00193. Therefore, there is a significant association between both variables (α < 0.05). The Pearson correlation coefficient is approximately 0.579 meaning that there is a positive but modest association between income per person and oil consumption.

Group 2: Countries in the higher median of CO2 emission

For countries that belong to the median with the higher CO2 emission (group “2”), the p-value of the association between income per person and oil consumption is approximately 0.00001. Therefore, there is a significant association between both variables (α < 0.05). The Pearson correlation coefficient is approximately 0.759 meaning that there is a positive and medium to strong association between income per person and oil consumption.

Comparison of group 1 and group 2

Both groups (countries with high and low CO2 emission) show a significant and positive association between income per person and oil consumption. However, the Pearson correlation is with ca. 0.759 stronger for countries with a generally high CO2 emission (group 2) compared to countries with a rather low CO2 emission (group 1, Pearson correlation ca. 0.579).

Python code

Finding the median value for the CO2 emissions and definition of 2 groups: lower median (=1) and higher median (=2).

Calculation of the Pearson correlation.

Plotting the data