#cousera

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.

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.

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".

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.

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%.

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%.

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.