Calculating the probability that a randomly selected person is a woman who is a vegetarian also: An Example, Statistical Note 1

Of the total participants in a training, 40% are women and of the total women participants 75% are vegetarian. A participant is selected at random who is a woman, what is the probability that she is a vegetarian?

Calculating the probability of a "AND" compound event that a randomly selected participant is a woman who is a vegetarian includes calculating the probabilities of other events. Several concepts are introduced while answering this question.

Let W be a simple event that a participant is a woman and the simple or the marginal probability of W represented by P(W) is 0.40, that is 40 percent of total participants are women.

Let V/W be a simple event that a participant is a vegetarian among the women participants. The conditional probability of vegetarians among women participants symbolized by P(V/W) is 0.75, that is 75 percent of women participants are vegetarians. Here the occurrence of the event of vegetarian women participants is dependent on the event of occurrence of women participants.

Let (W intersection V) or (W and V) is a "AND" compound event that a participant is a woman and a vegetarian. Here, the multiplication rule of two dependent events is applied. The joint probability of two dependent events is the product of a marginal probability and the conditional probability. In this case, the joint probability of an woman participant who is a vegetarian indicated by P(W intersection V) or P(W and V) in which both events of women participants and vegetarian women among all women participants occur is the product of P(W) and P(V/W) and that is equal to 0.40 multiplied by 0.75, equal to 0.30. It means that 30 percent of the total participants are women who are vegetarians.

Results Chain, An Exemplary Case – Basan Shrestha

I have prepared an imaginary article that could be used as a case for training and orientation on results chain to identify the goal, outcomes and interventions.

An international development agency was interested to undertake a public health project in one of the least developed countries. Experts from the agency reviewed the findings of Demographic Health Survey (DHS) and found that Nepal was one of the least developed countries having high child mortality rate for decades. DHS 1996 of Nepal data showed that the under-5 mortality rate was 118 deaths per 1,000 live births. Although that was reduced to 61 in a decade (DHS 2006) but the rate was still high. Under-5 mortality is defined as the probability of dying between birth and the fifth birthday. Then, the agency decided to undertake a project in Nepal, but was not clear which part of Nepal would be appropriate for the intervention. Then, the experts contacted Ministry of Health in Nepal to get the detailed dataset of child mortality rate in different parts of Nepal for past two decades. The MoH authority then directed the agency to New ERA in Nepal and ICF in the USA for the detailed dataset which had provided technical support to MoH to collect DHS data periodically. The international agency experts then reviewed the detailed dataset and found that one village in ………….foot hill had significantly higher under-5 mortality rate of 95 in 2006.  Then, the agency selected the village and undertook public health project for a decade. Afterward a decade, the same experts from the agency visited to evaluate the project achievement in 2017.

The experts started journey towards the village and noticed several changes. They noticed that open fecal discharge along the village roads were non-existent which was highly prevalent a decade ago. There were no bad smells, rather flowers were found blooming and spreading good smells along the roads. Every house in the village had toilet. Some houses had pit toilets and others had drained toilets. The experts interacted with the community members about the changes and noticed that some offenders in the communities a decade ago who did not favour to construct toilet in their houses were happy to share their positive attitude. Those offenders were turned to the change agents advocating for construction and use of toilet. The change agents formed the youth forum and provided training to youth volunteers and mobilized to orient community people through street drama. The change agents placed hoarding boards along the community roads and community displaying the message to encourage the construction and use of toilet and fined those defecating open. The fined money was used for awareness, cleaning and sweeping campaigns. The Village Development Committee (VDC) declared the VDC as the Open Defecation Free (ODF) zone.

The project had supported the community houses to construct biogas plants linking the animal sheds with the house toilets. The project had supported to purchase animals such as cows, buffaloes and goats for income generation as well as use of dungs for producing biogas. The project supported for fodder and forage for animals. That increased annual farm income from $1000 before the project to $1500 after the project. The household consumption of fuelwood for cooking purpose decreased from 800 kg of fuelwood to 300 kg per months after the project.

The project had supported to construct the toilets in the schools and public places and also the biogas plants linking the toilets. The project had supported to construct water collection ponds and electric water pumps for water supply in the schools and public places. Besides, the project had supported for household water supply system for drinking and sanitation purposes. As a result, people started to drink hygienic water and also use and clean toilet in their houses, schools and public places. The community people shared that the diarrhea and dysentery related check up and consultation visit to the health post reduced from every month to only two times a year. On the top of that, the District Public Health Office record showed that the under-5 mortality rate reduced to 35 over the decade. DHS 2016 shows that the current under-5 mortality rate for Nepal is 39.

The agency experts visited local government agencies, the Village Development Committee office, ward offices and village Water Sanitation and Hygiene Committee to interact about the project support and changes. The local government authorities shared that they did not certify any household for sale or purchase of land without verifying that the house has constructed the toilet and used it. The authorities shared that they did not certify the new building construction without the provision of a toilet and did not issue the house construction completion certificate without verifying that the house has constructed a toilet also. The authorities shared that they certified the household members for education scholarship or any other government support after verifying that the house has a toil and members are using it. The project had supported the local government agencies and committee to develop the guidelines and monitoring support for verification of toilet construction and use. Overall, several interventions were attributable to the project contributing to achieve the goal.

Database Management in Excel

Database management is important in all sectors. Database is a list of data in fields (variables) and records (cases). Columns and rows in the Microsoft Excel worksheet are used as fields and records respectively for record keeping, inventories, monitoring and evaluation to convert data into information. There are many features on data input, processing and outputs for managing database in Excel.

Input and Process

Table format in the worksheet delineates the data entry range of cells bringing the cursor to the first field of the following record after completion of the former record. Adding a new field automatically comes under an array of data. The table is named such as “data”. The name works as the source of data in the formula bar for processing and analysis.

Dropdown menu lists the response options that helps minimise data entry errors, example, district names are arranged in dropdown menu in the district field. Input and alert messages can be added to inform to select the appropriate value. Dependent dropdown menu in the next field limits the response categories in a cell based on the value in the former field. For example, once Kathmandu is selected from the dropdown list in the „district‟ field, only the Village Development Committees (VDCs) or municipalities (MPs) within Kathmandu district are listed in the “vdc” field.

Data from different sources are connected to avoid multiple entries. For example, the data template has a field to record district name and another field records the zone. A separate table of district and zone is created and linked to the “zone” field that automatically enters zone name once district is selected.

Cells can be customised to accept only the numerical values between the assigned maximum and minimum values to avoid Invalid data. Besides, there is a feature that identifies the already existing data that are invalid given the criteria.

The unique identifier cells can be customised to accept only the nonduplicating values. Besides, values from several fields can be added to create the unique identifier. For example, the first and family names of staffs in the list, district name and VDC name are combined to create a unique identifier. Duplicate values can be highlighted and removed, and the unique identifiers can be counted to identify the duplicate values.

Texts in a field can be broken down into words and placed into several fields. For example, the full name of a person in a field can be broken down into the first name, middle name if available and family name. Separating family name will help analyse staffs by caste/ ethnicity. Serial numbers are auto filled to avoid typing error. Serial numbers can be formatted to create unique identifiers. For example, if a staff dataset has a “StaffID” field.

Serial numbers can be formatted to appear “S1” as text on the first cell entered with number “1” and the same format can be auto filled in remaining cells of that field. Directly writing “S1” on a cell can also be auto filled in remaining cells, but the data value remains as S1 not “1” as in cell format customised as above.

Date of joining an organisation, for example, is used to calculate the duration of service as of certain time in decimal number of years or number of years, months and days. Job duration can be categorised as “New staff (less than 5 years)”, “Middle year staff (5 to less than ten years)” and “Old staff (ten or more years)”.

Output

Data filtration criteria is defined. Staffs aged 25 years or more can be filtered. Texts such as first names beginning with “A” can be filtered. Excel calculates values that match the defined criteria. For example, in the staff list one may be interested to know the average age of female staffs from Kathmandu district. Here, “age” is calculated taking “female” and “Kathmandu” as the criteria.

Frequency table counts the number of records by response categories. The counts can be calculated in percentage. Frequency tables can be created manually as well as using interactive features. Numeric values can be grouped to create the frequency distribution by categories.

Data summaries and presentation can be made in some other forms. Example, top three older and younger staffs by gender can be tabulated to provide information for staff recruitment strategy.

Cross tables summarise data by categorical fields, such as number of staffs by sex and ethnicity. One can choose either numeric values or percentage figures for data summary in cross tables. The numeric values may include number of counts, sum, average, maximum and minimum numbers. Column percent or row percent is again a matter of choice. Cross table is updated when the database is edited or updated with new records.

New field based on the existing fields can be created just for tabulation purpose. For example, the staff list has age of staff and salary fields. The salary age ratio field can be created for tabulation purpose to know how different are the ratios for female and male staffs. Charts are prepared to display data in graphical form using features to highlight and graphically present data bars. Colorful icons are used to visualise data and highlight top or bottom values.

Dashboard can be prepared using filter and connection features to link charts on dashboard to

visualise filtered values in different interactive charts.

Commuting on Our Narrow, Potholed Roads: PeopleSpeak, The Himalayan Times

The government needs to promote micro-bus for short routes and narrow roads, and big buses for longer routes and wider roads. Small vehicles are not a nuisance. Small vehicles such as tuk-tuk are seen driven in busy cities like Bangkok. Micro-buses can ply within the Ring Road during office hours. Big buses should be allowed on Ring Road and outside.
Basan Shrestha, Ghattekulo Marg
5 February 2017
https://thehimalayantimes.com/opinion/commuting-narrow-potholed-roads-nepal-vehicles-road-crowd/ 

Google Map of Village Development Committees and Municipalities I Visited in 20 Years

I had an opportunity to visit 168 Village Development committees (VDCs)/ Municipalities (MPs) spread in 40 districts of all five development regions of Nepal for academic and professional carrier development in more than 20 years from 1995 to mid-2018. 

The VDCs/ MPs are highlighted on the Google map with different colours for showing the VDCs/ MPs visited being associated with different organisations at different time intervals. Please visit the link: https://fusiontables.google.com/data?docid=1OmSL7SilJU8kHeuwhhAWmpB9Qn0yrpxVOI7WSISl#map:id=3.  

Besides, I've prepared another platform using the current Municipality names after declaration of Federal Democratic Republic of Nepal.

https://fusiontables.google.com/data?docid=1NVE-CKjb9K_K4HCjRjBilrRm-6X3lWd03O3rhT7w#map:id=8 

Hover over the map and click the finger pointer over a particular VDC/ MP, one can see the name of the region, zone, district and VDC/ MP; name of the organisation and objective of the visit and the year of visit.

I visited those VDCs/ MPs from 12 western districts to one eastern district.  The highest number of 45 VDCs/ MPs was in the western region and lowest number of one VDC in the eastern region. During that period, I visited being associated with ten different national and international organisations that included – Asian Institute of Technology based in Thailand; USAID's MEL Project (CAMRIS International), CARE Nepal, Forest Action Nepal, Lumle Agricultural Research Centre, Mercy Corps Nepal, Practical Action in Nepal, Tribhuvan University and Western Terai Landscape Complex Project (UNDP). The visits were primarily undertaken for needs assessment, monitoring and evaluation of activities in the communities related to agriculture and food security, community forest and buffer zone management, disaster risk reduction, reconstruction post earthquake 2015, girl’s education and saving and credit mechanisms.

I visited different clusters at different times of professional career. The first ten years (1995 to 2005) I visited mostly in western hills. The following five years (2005 to 2010), I'd the opportunity to visit mostly mid- to far-western terai communities. Afterwards, in next five years (2010 to 2015), I visited some far-western hill communities. In recent years (after 2015), I visited mostly in central hill communities. 

Freedom with Responsibility: PeopleSpeak, The Himalayan Times

in addition to letting them decide what they want to do, children must be aware of what right they are entitled to get and what responsibilities do they need to bear. Parents need to let their children decide what they want and how they want to fulfill their desire, but with guidance and support the children may expect from the superiors. This develops in children the confidence and decision making capacity to become a ‘chooser’ instead of simply a ‘user’ of goods and services. This will help grow the children to tackle the real-time opportunities and challenges. 
15 January 2017
Basan Shrestha, Ghattekulo Marg
https://thehimalayantimes.com/opinion/childhood-freedom-with-responsibility/

More Females but Richer Households

Basan Shrestha, Research, Monitoring and Evaluation Expert

basan_shrestha@yahoo.com, basanshrestha70@gmail.com

Female population increased and poverty rate decreased in Nepal. Sex ratio of males per 100 females decreased from 99.8 in 2001 population census to 94.2 in 2011. Nepal Living Standards Survey (NLSS) 2011 disclosed that poverty rate decreased from 30.8 percent in 2004 to 25.2 percent in 2011.  It indicated that households with more females were richer. Relationship between sex ratio and poverty rate was evident more in mid-west than in other regions of Nepal. Likewise, relationship was evident in rural areas.

NLSS 2010/11 estimated poverty line at Nepalese rupees 19,261 per capita annual consumption. Small Area Estimation of Poverty report published in 2013, using data from NLSS 2011 and Population Census 2011 estimated poverty rates of 976 sub-districts (Ilaka) which constituted 3,968 Village Development Committees (VDCs) and municipalities (MPs) located in 75 districts of all five regions including – 1,215 VDCs/ MPs of 19 districts in centre, 907 of 16 districts in east, 876 of 16 districts in west, 581 of 15 districts in mid-west and 389 of nine districts in far-west. Sex ratios for VDCs/ MPs in those Ilakas were taken from census report 2011 and compared with poverty rates to establish overall relationship, regional difference and rural-urban difference in predictability of poverty rate based on sex ratio.

In nutshell, households with more females were richer although relation between sex ratio and poverty rate was not very strong. It could be because households with more females might have male members away for employment. However, there could be many other factors that explained variation in poverty rates. Thus, sex ratio is important but not sufficient to predict poverty rate.

Overall Relation

In a regression analysis between sex ratio as explanatory variable and poverty rate as response variable regression coefficient was statistically significant indicating with 95 percent confidence that for each reduction in sex ratio, poverty rate will decrease by 0.28 percent. It indicated that bigger number of females in a family higher chances of its being rich. However, sex ratio poorly predicted, only 4 percent of variation in poverty rate as indicated by coefficient of determination.

Poor predictability of poverty rate could be because 64 percent VDCs/ MPs had more females as their sex ratios were less than national average ratio. Unlike, 46 percent VDCs/ MPs were rich as they had poverty rates less than national average rate, which was significantly lower than 53 percent of VDCs/ MPs that had sex ratios less than national average ratio had also poverty rates less than national average revealing that families with more females are likely to be rich. Among all VDCs/ MPs, average sex ratio ranged from 145.1 males per 100 females (Manang VDC of Manang district in west) to 64. 5 (Sari VDC of Pyuthan district in mid-west). Average poverty rate ranged from 72.8 percent (Kankada and Raksirang VDCs of Makwanpur district in centre) to 0.5 percent (Imadol VDC, Lalitpur district in centre).

Regional Difference

In a regression analysis, coefficients were significant indicating with 95 confidence that for each reduction in sex ratio, poverty rate decreased by 0.70, 0.50, 0.46 and 0.23 percentage points respectively in mid-west, east, west and centre. More females in a family had higher chances of being rich. Coefficient in far-west (0.02) was not significant. Sex ratio determined moderately to poorly variation in poverty rate by 26, 15, 7 and 3 percent respectively in mid-west, west, east and centre. Sex ratio hardly determined poverty rate in far-west indicating that far-western households had almost similar well-being status whether households had more or less number of females.

West had highest number of females as 83 percent VDCs/ MPs had sex ratios lesser than national average ratio, followed by far-west (78 percent), east (72 percent), mid-west (59 percent) and centre (42 percent). West had highest number of rich households as 61 percent western VDCs/ MPs were richer as they had  poverty rates less than  national average rate, followed by centre (55 percent), east (54 percent), mid-west (23 percent) and far-west (1 percent). Those proportions of VDCs/ MPs were significantly lower than proportion of VDCs/ MPs that had sex ratios less than national average ratio had also poverty rates less than national average in four regions (mid-west, centre, west and east) except in far-west. It revealed that in VDCs/ MPs of those reasons families with more females were likely to be rich in those four regions.

Rural-Urban Difference

Government designates VDCs and municipalities as rural and urban areas respectively. In a regression analysis, coefficient was significant in rural areas indicating with 95 percent confidence that with a unit decrease in sex ratio poverty rate will decrease by 0.28 percent. Thus, more females in a family increased their well-being status. In urban areas,  coefficient was almost nil and statistically insignificant indicating with less than 95 percent confidence that with unit decrease in  sex ratio  poverty rate will hardly decrease by 0.1 percent. Irrespective of decrease in sex ratio poverty rate will almost remain stagnant. Urban households had almost similar well-being status whether households had more or less number of females.  

Rural areas had more females than in urban areas, as 64 percent VDCs and 47 percent MPs had their sex ratios were less than  national average ratio. Likewise, rural areas were poorer than urban areas, as 46 percent VDCs and 81 percent MPs had poverty rates less than national average rate. Rural areas that had significantly higher number of females were richer as well as 52 percent VDCs that had sex ratios less than national average ratio had also poverty rates less than national average. Unlike, urban areas that higher number of females were not significantly richer as 81 percent MPs that had  sex ratios less than  national average ratio had also poverty rates less than  national average.

Conclusively, more females in a household meant household was rich. Those households might have their male members out for employment. However, relationship between sex ratio and poverty rate was not very clearly seen as there could be many factors determining poverty rates.