STATISTICAL ANALYSIS

STATISTICAL ANALYSIS

[Name of the Institute]

Statistical Analysis

Question # 1

A Latin term which means that different elements remain unaltered. Ceteris paribus is regularly utilized as an assumption when directing a wide assortment of budgetary examinations. By holding everything else consistent, the ceteris paribus surmise makes it conceivable to recognize the reason and impact connection between two variables. Unwinding the ceteris paribus supposition is the essential explanatory method utilized as a part of the relative statics investigation of matters of trade and profit.

If one for instance the exports increases and other factors remains the same then the GDP also increases similarly if the exports increases (Pesaran, 2010) and all the other factors remains unchanged then the GDP decreases. GDP is affected by number of factors for instance import, export, investment, consumption and government expenditure.

The possible solutions are for instance if the export increases, government expenditure are less, investment increases and income and consumptions also increases then GDP will be increased and vice versa. Therefore there is need to increase the investment, income and export that will have greater impact on the GDP

Individual significance for females = 0.227/0.168 = 1.3511 which is insignificant at 1% and 5% but remains significant at 10%.

Individual significance for education = 0.082/0.008 = 9.879 which is highly significant at 1%, 5% and even 10%. Thus this variable significantly impacts the wages.

Individual significance for the interaction effects = 0.0056/0.0131 = 0.4274 which is insignificant at 1%, 5% and even 10%. Thus this variable does not impact the wages.

The interaction variable female education might cause the results be affected from autocorrelation.

The least square equation shows that the coefficient for gender is found to be insignificant at 95% confidence and it also have a negative impact on the overall wages among men and women i.e. less wages are offered to females. Further education is a positive factor for wages and it (Keane, 2010) is found to highly significant fewer than 95% confidence and it significantly impacts the overall wages. The female education similarly creates a negative impact i.e. lower female education creates higher wages for them.

Individual significance for females = 0.227/0.168 = 1.3511 which is insignificant at 1% and 5% but remains significant at 10%.

Individual significance for education = 0.082/0.008 = 9.879 which is highly significant at 1%, 5% and even 10%. Thus this variable significantly impacts the wages.

Law of Demand

The quantity of demand increases with decrease in prices.

The consumer will purchase more goods and services at higher price

The price and the quantity supplied are directly proportional to each other.

The sensitivity of demand of the product for a consumer and the changes in price of a good is that is measured by the price elasticity of the demand.

While these generalizations may be demonstrated regarding more level sciences, this is not being focused here. What is essential here is that the sum of these generalizations is non-widespread. That is, there are (true and just conceivable) scenarios in which the above generalizations don't hold, despite the fact that all the conditions get that ...