How to upgrade from Teletalk 2G to 3G Service?

How to upgrade from 2G to 3G Service?



To upgrade from your existing 2G to 3G services, write 3G and send to 666. The upgradation fee is Free and respective 3G package will be activated and notified through SMS.

2G Package	Code	Upgradation Fee	Procedure	Migrated To
Bijoy, PCO	666	Free	3G -> 666	Bijoy 3G
Ekush , Agami		Free	3G -> 666	Ekush 3G
Shadheen, Shadheen66,  Standard		Free	3G -> 666	Shadheen 3G
Any 2G or 3G	555	Free	Y3G ->555	Youth 3G

Note: For example, if Bijoy 2G package subscribers want to upgrade from 2G to 3G, write 3G and send to 666 and Bijoy 3G will be activated.

How to downgrade 3G to 2G Service?
To downgrade from 3G to 2G, write 2G and send to 666 and respective package will be activated.
Package Migration

To migrate to Bijoy 2G/3G package, write bij, for Ekush 2G/3G package, write 21, for Shadheen 2G/3G package, write sha and send to 555.

Source: http://www.teletalk.com.bd

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Methods to test for the presence of Heteroscedasticity:

There are several methods to test for the presence of Heteroscedasticity:

1. Park test
2. Glejser test (1969)
3. White test
4. Breusch-Pagan test
5. Goldfeld-Quandt test
6. Cook- Weisberg test
7. Harrison-McCabe test
8. Brown-Forsythe test
9. White’s General Heteroscedasticity Test

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Remedial Measures of multicollinearity

Remedial Measures of  multicollinearity:

Multicollinearity does not actually bias results; it just produces large standard errors in the related independent variables. With enough data, these errors will be reduced.
In a pure statistical sense multicollinearity does not bias the results, but if there are any other problems which could introduce bias multicollinearity can multiply ( by orders of magnitude ) the effects of that bias. More importantly, the usual use of regression is to take coefficients from the model and then apply them to other data. If the new data differs in any way from the data that was fitted we may introduce large errors in predictions because the pattern of multicollinearity between the independent variables is different in new data from the data used for your estimates. We try seeing what happens if we use independent subsets of your data for estimation and apply those estimates to the whole data set.
In addition, we may:

1) Leave the model as is, despite multicollinearity. The presence of multicollinearity doesn't affect the fitted model provided that the predictor variables follow the same pattern of multicollinearity as the data on which the regression model is based.

2) Drop one of the variables. An explanatory variable may be dropped to produce a model with significant coefficients. However, you lose information (because you've dropped a variable). Omission of a relevant variable results in biased coefficient estimates for the remaining explanatory variables.

3) Obtain more data. This is the preferred solution. More data can produce more precise parameter estimates (with lower standard errors).

4) mean-center the predictor variables. Mathematically this has no effect on the results from a regression. However, it can be useful in overcoming problems arising from rounding and other computational steps if a carefully designed computer program is not used.

5) Standardize your independent variables. This may help reduce a false flagging of a condition index above 30.

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Epidemiology Uses of Epidemiology

Epidemiology

Epidemiology is derived from the word epidemic (Epi means Among, Demos means People and Logos means Study) which is a very old word dating back to the 3^rd century B.C.

Epidemiology is a strategy for the study of factors relating to the etiology, prevention and control of disease to promote health, and to efficiently allocate efforts and resources for health promotion maintenance and medical care in human population.

The scientific study of epidemics and epidemic diseases, especially the factors that influence the incidence, distribution and control of infectious diseases is called epidemiology.

Epidemiology is that field of medical science which is concerned with the relationship of various factors and conditions which determine the frequencies and distribution of an infectious process, a disease in a human community.

Epidemiology is the basic discipline in public health practices and is the basic science of the community medicine. Epidemiological studies often provided the knowledge necessary for prevention and control of disease in the community even before the related biochemical, microbiological and other information about its etiology has become available and can uncover a specific cause for a condition which is otherwise difficult by any other method.

Many authors define epidemiology differently as

• It is a branch of medical science which treats of epidemics (Parkin, 1973)
• The science of the mass phenomena of infectious diseases (Frost, 1927)
• The study of disease, any disease, as a mass phenomenon (Greenwood, 1934)
• The study of the distribution and determinants of disease frequency in man (MacMahon, 1960)
• The study of the distribution and determinants of health-related states and events in populations and the application of this study to control health problems (Last, 1983)

Uses of Epidemiology

i)                    Community diagnosis i.e. what are the major health problems occurring in a community.

ii)                  Establishing the history of a disease in a population. e.g. identifying the periodicity of an infectious disease.

iii)                Describing the natural history of disease in the individual e.g. natural history of HIV infection in the individual.

iv)                Describing the clinical picture of disease i.e. who gets the disease, who dies from the disease and what is the outcome of the disease.

v)                  Estimating risk e.g. what factors increases the risk of heart disease, automobile accidents.

vi)                Identifying syndromes and precursors e.g. the relationship of high blood pressure to stroke, kidney disease, and heart disease.

vii)              Evaluating prevention/intervention programs.

viii)            Investigating epidemics/disease of unknown etiology.

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The Petersen and Chapman Estimator and xample

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