#cousera

[ofspringone]

these days i have been experimentig to find the best time of the day to study i have tried to stay awake after fajr prayer which now is 4 a.m however i failed because even though i take a nap during the day still i feel like zombie when i stay awake after fajr. And i can not study during the day for the love of god because there are so many distractions and noise so i decided to try night time studying. I was not keen on this because i was desperately trying to get shut eye before waking up for praying however it turned out to be the best time for me to study. so now i just do not sleep till fajr and study from 10 p.m to like 3 a.m. i liked studying at night because i surely know that i will sleep after ending the study session and praying. It is been 3 days, i feel good and studying does not bother me anymore.

[running-an-analysis-of-variance]

It is possible to draw several hypotheses between a categorical explanatory variable (such as the state an American citizen resides in) and a quantitative response value (such as the number of cigarettes smoked per week) using the nesarc-dataset and the method "Analysis of Variance" ("ANOVA").

In order to achieve this, we must first establish our null hypothesis, in my instance that there is no difference in the number of beers drunk in excessive beer consumption in a single sitting (5+ beers in a single sitting) for the categorical variable "Census division" from which the test subjects are drawn. In other words, if the null hypothesis Ho is correct, the median amount of excessive beer consumption per census division ought to be the same for all nine.

Ho = µ1 = µ2= µ3= µ4= µ5= µ6= µ7= µ8= µ9 The alternate theory that follows from it is:

Not all means are created equal.

I utilised the nesarc dataset and the data analytics tool "SAS" to run the test.

I start by importing the dataset and the variables that will be used in this test.

I explained the variables' names and the kind of data from the NESARC base that should be used when conducting the test.

A value for the monthly use is calculated, multiplied by the minimum number of beers consumed in those sittings to create the variable USQTY (usual quantity), which includes the days with excessive beer consumption (minimum of 5 per day).

Then I filter for middle-aged adults (25 to 55 years old) who have consumed beer at least once within the last year, only including relevant data.

The ANOVA is then performed after the data is sorted by the nesarc individuals' ID numbers.

I also used the SAS tool's "Duncan's new multiple range test" feature to run a post hoc analysis on the ANOVA data.

This demonstrates the validity of the ANOVA results for the chosen test case by demonstrating whether there are indeed significant differences in the means of more than two category variables.

The following are the outcomes:

As categorical explanatory values, the 9 different CENDIVS, with values ranging from 1 to 9, were used.

4048 of the 13713 arguments examined using the provided data met the test conditions and were chosen for the test.

The test yields a p-value of 0,0006 and an F-statistic of 3.42. It is much below our alpha value of 5%.

Because not all methods are equal, we can reject the null hypothesis and embrace the alternative.

The Duncan Post Hoc analysis confirms this:

There are three distinct groupings of means, none of which differ from the others appreciably.

This indicates that the null hypothesis must be rejected even when it is true, and it safeguards us against type 1 errors.

[malcom-analysis]

Gap Minder Study

After looking through the codebook for the gap minder study, I have decided that I am particularly interested in the life expectancy of individuals per country and factors that affects it. I am not sure which variables I will use regarding this relationship so for now I will include all of the relevant variables in my personal codebook.

At this point, I would continue to explore the code book for the data set you have selected.

While CO2 emissions is a good starting point to investigate co-relationships, I need to determine what it is about life expectancy that I am interested in. It strikes me that people in different parts of the world live longer than others. In some countries, it is expected that when you reach age 50 you could die any time soon. I really want to understand why. There are certain factors that influences it. CO2 emissions could be one of them or maybe several other factors.

I decide that I am most interested in exploring the association between CO2 emissions and life expectancy. I add to my codebook variables reflecting life expectancy (e.g. CO2 Emissions, Alcohol consumption rate, suicide rate per 100,000 etc).

[entrepalabrasmx]

Coursera lanza en el país el primer MBA online y en español

Con más de 900.000 estudiantes en Argentina y con un crecimiento interanual del 143%, Coursera anuncia un nuevo programa junto con la Universidad de Palermo para llegar a más estudiantes de habla hispana

 A medida que el COVID-19 acelera la transformación digital, existe una creciente demanda mundial para de profesionales empresariales impulsados por la tecnología. De esta forma, Coursera, la plataforma de educación en línea más grande a nivel mundial, está incrementando su oferta en nuestro país y anunció hoy el lanzamiento de la Maestría en Dirección de Empresas (MBA) online de la Universidad de Palermo (UP).

Se trata de uno de los mejores programas de MBA en América Latina que ocupa el puesto número 1 en Argentina y cubre temáticas tales como Marketing digital, desarrollo de productos digitales y business analytics. Disponible online en Coursera, este título en español prepara a los profesionales de negocios para puestos gerenciales de alto nivel y esfuerzos empresariales.

Algunas cifras interesantes acerca de Coursera a modo de contexto:

En el último año, en Argentina, los alumnos de Coursera se inscribieron en 1.1 millones de cursos.

Las inscripciones tuvieron un crecimiento del 815% en comparación con el año anterior.

A nivel global, desde el lanzamiento de los MBAs, en 2016, más de 1,000 estudiantes se han graduado de ellos.

“La Universidad de Palermo es líder en educación en América Latina, reconocida por su innovación, planes de estudio enfocados en el desarrollo profesional y cuerpo estudiantil de gran diversidad. Nos sentimos honrados de asociarnos con la UP para que los estudiantes de habla hispana de todo el mundo puedan obtener un MBA de una de las mejores escuelas de negocios de la región”, dijo Betty Vandenbosch, Vicepresidente Ejecutiva de Contenido y Credenciales de Coursera. "Esta Maestría provee a los futuros líderes de habilidades digitales, experiencias colaborativas remotas y una red profesional internacional que los prepara para tener éxito en el mercado laboral actual”.

Por su parte, Gabriel Foglia, decano de la Facultad de Negocios de la Universidad de Palermo, afirma: "Estamos encantados de anunciar el lanzamiento del MBA online de la Universidad de Palermo con Coursera. Esta alianza reafirma nuestro compromiso con la educación en negocios de alta calidad. La Maestría en Dirección de Empresas de la UP se ha consolidado como una de las más innovadoras de la región, formando líderes de negocios y empresariales que generan un impacto real en sus comunidades y una diferencia la sociedad. Coursera nos permitirá potenciar nuestras capacidades, incorporar la mejor tecnología educativa existente y llegar a un universo amplio de estudiantes de todo el mundo".

Las solicitudes para el MBA ya están abiertas, y el primer curso comienza en mayo de 2021. Para conocer más, por favor visite https://www.coursera.org/degrees/mba-palermo.

[dheedataanalysis]

Data Management for the GapMinder Dataset

The Data Management steps taken in this study is described in this post.

Summary of Data Management Steps

For all the variables used in this research, missing data/ empty strings are replaced by NaN.

Frequency Tables are drawn to compare between the datasets before and after the Data Management Steps.

It can be observed that the NaN values took the frequency of its respective missing data, and all other observation frequency remained the same.

‘urbanrate’ is categorized into four different bins with the same interval, and ‘incomperperson’ is categorized using quartiles.

Other Data Management processes like creating secondary variables are not used because, the main variables ‘incomeperperson’, ‘urbanrate’, and ‘relectricperperson’ used in this study have quantitative and distinct obesrvations.

Outline of Code

The step to step of code cells are explained here.

Cell 1: Import the modules needed for Data Management

Cell 2: Load the GapMinder dataset into the programming environment stored in data, and create your own datasets with variables relevant to the study (data_new, data_nan).

Cell 3: Replacing the missing data “ “ in the ‘incomeperperson’ variable with np.nan.

From the Output of this cell, it is noticed that the missing data of frequency count of 23 in the first dataframe, is replaced by NaN of frequency 23 in the second dataframe.

Cell 4: Replacing the missing data “ “ in the ‘urbanrate’ variable with np.nan.

From the output, it is evident that the missing data of frequency 10 in the first dataframe, is replaced by NaN of the same frequency in the second dataframe.

Cell 5: Replacing the missing data “ “ in the ‘employrate’ variable with np.nan.

From the output, we see that the missing data of frequency 35 in the first dataframe, is replaced by NaN of the same frequency count in the second dataframe.

Cell 6: Replacing the missing data “ “ in the ‘co2emmisions’ variable with np.nan.

From the output, it is noticed that the missing data of frequency 13 in the first dataframe, is replaced by NaN of the same frequency count in the second dataframe.

Cell 7: Replacing the missing data “ “ in the ‘relectricperperson’ variable with np.nan.

The output shows that the missing data of frequency 77 in the first dataframe, is replaced by NaN of the same frequency count 77 in the second dataframe.

Cell 8: Converting the datasets into numeric values, so we can use the value_counts function on them.

Cell 9: Categorizing the ‘incomeperperson’ observations into four classes using the quartiles function pd.qcut.

From the output, we can see that the low_income observations are 48, the low-middle 47, the high-middle 47, and the high income observations 48.

Cell 10: Categorizing the ‘urbanrate’ using bins of interval 18. The ranges 10-28, 29-46, 47-64, 65-82, and 83-100.

From the output, the frequency and percentage of each observation in its range is drawn in the dataframe. The ranging  works because python indexes starting from 0, and that is why the categories have an open bracket in the beginning.

Adjustments Made

In the Frequency Distribution for the GapMinder Dataset post, I used the groupby function in Python to get the frequency of the observations under each variable. Replacing the missing data with NaN, i need Python not to drop the NaN values. For this, dropna=False is written in the arguments. The Groupby function does not have this argument until Pandas version 1.1, so i decided to use the Value_counts function.

For easy comparison of the datasets before and after the data management step, i need Python to display the Frequency Distribution Dataframes side by side. So i displayed them by using IPython’s display_html function.

Panda’s to_numeric function is used instead of convert_numeric. The do the sane function but convert_numeric does not work on dataframes.

[dheedataanalysis]

Frequency Distribution for the GapMinder Dataset (Part 2)

This post is the concluding part of Frequency Distribution for the GapMinder Data set (Part 1)

Frequency Distributions for each Variable contd.

3. Employ Rate (employrate) observations

The output shows the frequency distribution table for the ‘employrate’ variable.

The variable is grouped by the 140 unique observations.

These values are continuous with most of the values occurring only once (Frequency = 1, Percentage = 0.469484%).

Missing data, denoted by an empty string is the mode of the distribution (Frequency = 35, Percentage = 16.431925%).

The Cumulative Frequency sums to the total observations (213) and Cumulative Percentage sums to 100%.

4. Residential Electricity per Person (relectricperperson) observations

The output shows the frequency distribution table for the ‘relectricperperson’ variable.

The variable is grouped by the 133 unique observations.

These values are continuous with most of the values occurring only once (Frequency = 1, Percentage = 0.469484%).

Missing data, denoted by an empty string is the mode of the distribution (Frequency = 77, Percentage = 36.150235%).

The Cumulative Frequency sums to the total observations (213) and Cumulative Percentage sums to 100%.

Part 1 and 2 contain all the Frequency Distribution information about the data set and variables under study in this research

[dheedataanalysis]

Frequency Distribution for the GapMinder Dataset (Part 1)

This post outlines the step to step Python code written for the Frequency Analysis.

Summary of the Frequency Distribution Observations

For this research, the variables are 'incomeperperson', 'urbanrate', 'relectricperperson', maybe 'employrate', and 'co2emission' for later study

All the variables have 213 observations

All the observations are of continuous value with most of them occurring only once(Frequency=1, Percentage=0.469484)

Missing data are denoted by empty strings in all variables

Missing data are the mode of all the Frequency Distributions but vary from variable to variable

Outline of Code

This is a step to step outline of the code cells and the respective output for easy understanding. All the Frequency Distribution values are tabulated for easy understanding and brevity.

Cell 1: Import the modules needed in this task.

Cell 2: Load the Gapminder dataset used in the task and store in data.

Output:

This Output shows the head and tail of the Dataframe, i.e. the first and last five(5) observations of our dataset.

Cell 3: Create a new dataframe with only the variables needed for the research analysis. For this, research as listed in the first post, only ‘incomeperperson’, ‘urbanrate’, ‘relectricrate’, and maybe ‘employrate’ will be used for current research analysis, while ‘co2emission’ will be used for later study.

The new dataset is stored with the name data_new.

The output of the code gives the head and tail of the new dataframe, i.e. the first and last five (5) observations in the dataframe.

Cell 4: A little bit of dataset information is printed in this cell.

The output gives the number of observations, variables and the dataset description.

For our new dataset with variables ‘incomeperperson’, ‘urbanrate’, ‘employrate’, ‘co2emission’, and ‘relectricperperson’

count is the total number of observations in the variables

unique is the number of unique values in under each variable

top is the most mode of the unique values

freq is the number of times the top (mode) appears in the observations.

Frequency Distributions for Each Variable

1. Income per Person(incomeperperson) observations

The output shows the frequency distribution table for the ‘incomeperperson’ variable.

The variable is grouped by the 191 unique observations.

These values are continuous with most of the values occurring only once (Frequency = 1, Percentage = 0.469484%).

Missing data, denoted by an empty string is the mode of the distribution (Frequency = 23, Percentage = 10.798122%).

The Cumulative Frequency sums to the total observations (213) and Cumulative Percentage sums to 100%.

2. Urban Rate (urbanrate) observations

The output shows the frequency distribution table for the ‘urbanrate’ variable.

The variable is grouped by the 195 unique observations.

These values are continuous with most of the values occurring only once (Frequency = 1, Percentage = 0.469484%).

Missing data, denoted by an empty string is the mode of the distribution (Frequency = 10, Percentage = 4.694836%).

The Cumulative Frequency sums to the total observations (213) and Cumulative Percentage sums to 100%.

Frequency Distribution Table of'employrate', 'relectricperperson' can be viewed in the Part 2 of this post

[dheedataanalysis]

Electricity Consumption in Urban Residential Areas.

I am working with the GapMinder Dataset to examine how factors like Gross Domestic Profit GDP per capita, Urban Population, and maybe Employment rate affect the Residential Electricity. I really want to know how these factors can be tweaked to bring epileptic power supply to a minimum in residential areas.

Further study will be done to find out the significance of all these on the CO2 emissions. 

Broad Topic: Electricity Consumption

I came up with questions like:

How does Urban population and GDP affect the Residential Electricity Consumption?

How do these factors relate stastically with each other?

How do they all influence the CO2 emission?

Questions 1 and 2 will be analysed first then 3.

Literature Review so far indicates the following

There are bidirectional relationships between the variables being considered that can be quantified using regression and statistical analysis.  (Liddle, Brant & Lung, Sidney. 2013)

GDP and Urban Population can be considered factors of Residential Electricity Consumption. (most of the papers in the bibliography show this).

Most of the analysis were done using Granger causality models and Simple Regression models.

Most of all, for short-term studies, high GDP, high Urbanization with some other factors like Aggregate Water Consumption equals High Electricity Consumption. (Saif Kayed Al-Bajjal & Adel Yacoub Shamayleh, 2018)

Variables

After the literature review, i began to get insights into variables that are required for my study, and conclusions that has been proven and drawn.

I included Residential Electricity Consumption, Income per Person, Urban Population, Employment Rate, CO2 Emission and so on into my codebook for my study.

Bibliography:

1. Lariviere, I, and Lafrance, G. Modelling the electricity consumption of cities: effect of urban density. United Kingdom: N. p., 1999. Web. Doi:10.1016/S014-9883(98)00007-3.

2. Saif Kayed Al-Bajjali, Adel Yacoub Shamayleh, Estimating the Determinants of Electricity Consumption in Jordan, Energy (2018), doi: 10.1016/j.energy. 2018.01.010 

3. V. Bianco, O. Manca & S. Nardini (2013): Linear Regression Models to Forecast Electricity Consumption in Italy, Energy Sources, Part B: Economics, Planning, and Policy, 8:1, 86-93 

4. Yi-Tui Chen. The Factors Affecting Electricity Consumption and the Consumption Characteristics in the Residential Sector—A Case Example of Taiwan. Sustainability 2017, 9, 1484. 

5. Ali Acaravci & Ilhan Ozturk, 2012. “Electricity consumption and economic growth nexus:

A multivariate analysis for Turkey,” The AMFITEATRU ECONOMIC journal, Academy of Economic Studies- Bucharest, Romania, vol. 14(31), pg. 246-257, February. 

6. Liddle, Brant & Lung, Sidney. (2013). Might electricity consumption cause urbanization instead? Evidence from heterogeneous panel long-run causality tests. Global Environmental Change. 24. 10.1016/j.gloenvcha.2013.11.013. 

7. Zhao, Xiaoli & Li, Na & Ma, Chunbo. (2011). Residential Energy Consumption in Urban China: A Decomposition Analysis. Energy Policy. 41. 644-653. 10.1016/j.enpol.2011.11.027. 

8. Misini, Shkumbin. (2017). The Effect of Economic Growth in Relation to Unemployment. 10.13140/RG.2.2.24711.85927.