Ajaegbu Emmanuel

Statistical Sample: Definition, Methodologies, Types, formula, and Examples

A sample is defined as a smaller set of data that a researcher chooses or selects from a larger population by using a pre-defined selection method.

These elements are known as sample points, sampling units, or observations. Creating a sample is an efficient method of conducting research.

In most cases, it is impossible or costly and time-consuming to research the whole population. Hence, examining the sample provides insights that the researcher can apply to the entire population.

Read more:https://www.google.com/amp/s/www.questionpro.com/blog/sample/

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May the New Year bless you with health, prosperity, and happiness

Happy New Year

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The POISSON DISTRIBUTION

A Poisson distribution is a tool that helps to predict the probability of certain events happening when you know how often the event has occurred. It gives us the probability of a given number of events happening in a fixed interval of time.

>Practical Uses of the Poisson Distribution

A textbook store rents an average of 200 books every Saturday night. Using this data, you can predict the probability that more books will sell (perhaps 300 or 400) on the following Saturday nights. Another example is the number of diners in a certain restaurant every day.If the average number of diners for seven days is 500, you can predict the probability of a certain day having more customers.

Read More: https://www.statisticshowto.com/probability-and-statistics/statistics-definitions/probability-distribution/poisson-distribution/

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PLACEBO- DESIGN OF EXPERIMENT

Placebo-controlled studies are a way of testing a medical therapy in which, in addition to a group of subjects that receives the treatment to be evaluated, a separate control group receives a sham "placebo" treatment which is specifically designed to have no real effect.
Placebos are most commonly used in blinded trials, where subjects do not know whether they are receiving real or placebo treatment. Often, there is also a further "natural history" group that does not receive any treatment at all.

The purpose of the placebo group is to account for the placebo effect, that is, effects from treatment that do not depend on the treatment itself. Such factors include knowing one is receiving a treatment, attention from health care professionals, and the expectations of a treatment's effectiveness by those running the research study. Without a placebo group to compare against, it is not possible to know whether the treatment itself had any effect.

Patients frequently show improvement even when given a sham or "fake" treatment. Such intentionally inert placebo treatments can take many forms, such as a pill containing only sugar, a surgery where nothing efficacious is actually done (just an incision and sometimes some minor touching or handling of the underlying structures), or a medical device (such as an ultrasound machine) that is not actually turned on. Also, due to the body's natural healing ability and statistical effects such as regression to the mean, many patients will get better even when given no treatment at all. Thus, the relevant question when assessing a treatment is not "does the treatment work?" but "does the treatment work better than a placebo treatment, or no treatment at all?"

Read more https://en.m.wikipedia.org/wiki/Placebo-controlled_study

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T TEST

The t test tells you how significant the differences between groups are; In other words it lets you know if those differences (measured in means) could have happened by chance.

A very simple example: Let’s say you have a cold and you try a naturopathic remedy. Your cold lasts a couple of days. The next time you have a cold, you buy an over-the-counter pharmaceutical and the cold lasts a week. You survey your friends and they all tell you that their colds were of a shorter duration (an average of 3 days) when they took the homeopathic remedy. What you really want to know is, are these results repeatable? A t test can tell you by comparing the means of the two groups and letting you know the probability of those results happening by chance.

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In an Market, prices adjust quickly to the release of new information into the markets, therefore prices of security reflects all available public information about that security. It is also impossible to generate a risk adjusted return greater than the benchmark, Therefore portfolio strategy is the best management strategy in the market.

I recently conducted a study to test the market hypothesis of the Nigeria market. Our aim is to determine if the passive or active strategy is the best portfolio management strategy in the Nigeria Capital Market.

The Nigeria All share index(NGSEASI) was used to Conduct this research. The serial correlation and run test was used to test the random walk of the return of the serial

The result obtained show that the Nigeria capital market does not comply with the Weak Efficient Market Hypothesis since the hypothesis of random walk was rejected.

Active Portfolio Management is the best strategy for the Nigeria Capital Market.

Click below to view a detailed report and the R Code for this Research

https://github.com/EEAjaegbu/Weak-EMH-Nigeria

Are you a master student or an analyst who wants to perform the fixed effects least square dummy variable analysis using R programming..

Looking for a good starting point..

Check out https://github.com/EEAjaegbu/LSDV-Panel-Model

Analyzing research could be very difficult especially if you have little or no statistical experience.

Writing code to analyze your research can be very frustrating if you have little or no programming skill(R programming,python etc.).

Pressures from lecturers to give update in your progress could add to the frustration. It's get worst when deadline are fast approaching.

It's has been proved that the brains perform lesser when frustrated and tired.

At this rate do you think you can deliver before deadline. NO

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A study to explain the Impact of Years of Education on the Wages of Head of Household.

Expectation: Positive linear Relationship between Years of Education and Wages

Data Source: The data for this study are drawn from years 1976-1982 of the non-Survey of Economic Opportunity portion of the Panel Study of Income Dynamics (PSID) on 595 individual.

Aim: To compare the Pooled OLS Regression Model with the Least Square Dummy Variable Fixed Effects Model.

Result:
a) As expected Both the Pooled OLS and the LSDV fixed effects model describes a positive relation ship

b)The slope coefficient of the pooled OLS is 0.065204,for any additional one Years of Education achieved by the head of the household the average wages earned will increase by $0.065204. The Residual standard Error and R- squared Adjusted are 0.4243 and 0.1552 respectively.

c) There was an improvement with the use of the LSDV fixed effects model

d)The slope coefficient of the LSDV fixed effects model is 0.48014,for any additional one Years of Education achieved by the head of the household the average wages earned will increase by $0.48014. The Residual standard Error and R- squared Adjusted are 0.153 and 0.999 respectively.

e) Therefore by controlling for the time invariant unobservable and the individual invariant unobservable using the LSDV Fixed Effect Model, we have been able to improve the estimate of the coefficient of the parameter ED (years of Education) and the Overall model.