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Introduction

There is no point denying the fact that economic indexes and data play a very big role for coun-try to position itself on a world arena. Among different economic indexes, we would like to highlight the one, that firstly is the most common for everyone and later has not the least im-portance while evaluating the health, growth and stability of the country and this economic index is GDP. There are different factors that affect countries’ GDP and that “help” it to prosper or, vice versa, to reduce it’s output. Considering all the circumstances and things that can influence GDP we would want to mark out the following, that we will be talking about in the process of our statistical experiment, so they are institutions, infrastructure, macroeconomic environment, health and primary education in a country.

Economic growth of the country always has been one of the most important issues for the coun-tries. This index determines at what level of development each country is. Therefore, policymakers study all factors that may have influence on it. However, the main point is to spot which of the factors have most significant impact on economic growth.

Our research is aimed to investigate:

1. What Factors influence on economics growth.

2. How Growth of economy can affect to GDP changes.

In order to make our analysis we take a sample of 36 countries all over the world. Some of them are developed while others are developing countries. Our investigation is based on cross – sec-tional data analysis, because we observe different variables without respect to any time differences. This cross – sectional sample gives us precise snapshot of all countries at one specific period of time, in our case 2010.

Cross-sectional data

Cross-sectional studies make comparisons at a single point in time, whereas longitudinal studies make comparisons over time. The research question will determine which approach is best. Study design depends greatly on the nature of the research question. In other words, knowing what kind of information the study should collect is a first step in determining how the study will be carried out (also known as the methodology). Let’s say we want to investigate the relationship between daily walking and cholesterol levels in the body. One of the first things we’d have to de-termine is the type of study that will tell us the most about that relationship. Do we want to com-pare cholesterol levels among different populations of walkers and non-walkers at the same point in time? Or, do we want to measure cholesterol levels in a single population of daily walkers over an extended period of time? The first approach is typical of a cross-sectional study. The second requires a longitudinal study. To make our choice, we need to know more about the bene-fits and purpose of each study type.

Conclusion

Generally, we have 27 countries out of 36, whose values lie in the corridor of only ±2% deviations from the predicted values. Other 9 countries' values were significantly different from predicted ones.

From now we analysed these countries more specifically in order to understand why our expected and actual analysis were not the same.

Summarizing everything up, we can conclude that our predicted values did not perfectly coincide with actual ones. We found out that by using simple, multiple regression models and by building a hypothesis. We assumed that all our independent variables in the aggregate would have great impact on economic growth. This was our null hypothesis:

Y = β１+ β2 * GDP + β3 * Unemployment rate + β4 * Labor force participation + β5 * Education spending + β6 * Population size + β7 * Ln GDP

However, in the process of analysis we rejected it. Furthermore, during our research, we found out that education spending and population size are the key determinants of economic growth. Ac-cording to what was said previously, in this case, we fail to reject hypothesis.

Also, we investigated which internal and external factors did affect economic growth and were they considerable or not.

1) What is the difference between the population and sample regression functions?

The population regression function (PRF) is a  linear function  that is derived from the sample regression function (SRF) which represent the population and sample regression lines, respectively. The SRF can be expressed as: the estimated dependent variable (Y) equals the estimated beta1 parameter value plus the estimated beta2 parameter value multiplied by the explanatory variable (X) plus the estimated residual. From this function, the PRF can be expressed as: the dependent variable (Y) equals the beta1 paramenter value plus the beta2 paramater value times the explanatory variable (X) plus the stochastic error. These functions serve purposeful during regression analysis, which ultimately determines how the "average value of the dependent variable varies with the given value of the explanatory variable." The stochastic version of the PRF is critical for empirical studies - stochastic meaning that the disturbance term is added to the function in order to completely estimate the PRF