Mathematical analysis of an economy's financial data assists in the determination and implementation of necessary fiscal policies or practices. Such analysis employs both graphical and numerical descriptive measures to accurately analyze data, and convert it into useful statistical information. Thus, is the purpose of this report to examine data pertaining to the inflation rates of twenty-eight OECD member-nations, in particular comparing the inflation rate of Australia to those of Japan, the United States, and the United Kingdom, and to present this information employing the aforementioned methods of data analysis. .
Initial analysis of Australia's data indicates the nation's inflation rate has, in general, decreased throughout the subject period. The 1990 measurement (7.28%) was by far the highest, with subsequent years usually remaining below 2%, bar measurements of 4.64% and 2.61% in years 1995 and 1996 respectively. The high 1990 rate of inflation can possibly be attributed to financial fallout from the global recession of 1989. This theory is supported by a comparative analysis of the inflation rates of other OECD nations. Graph (i) shows the progressive inflation rate of Australia compared to the rates of three leading economies - those of the United Kingdom, the United States and Japan. From this, it is seen that Australia's inflation rates seem to mirror these foreign markets. All economies have seen inflation decline throughout the 1990's, with drastic decreases in the early part of the decade as a global economic recovery stabilized world markets.
Deeper insight into these comparisons can be found by application of various numerical descriptive measures. Table (i) shows statistical information on the inflation rates of all OECD member nations, sorted by mean inflation over the subject period. From this, it is seen that Japan has the lowest average inflation rate of all nations (1.
Quantitative analysis employees mathematical and statistical tools as well as quantitative information or data. ... Quantitative analysis consists of seven steps that are used in its approach. ... Quantitative analysis models consist of two types of variables: 1. ... The next step, step six, is to perform a sensitivity analysis. ... Implementation should be based on the quantitative analysis results. ...
Pitfalls of Data Analysis The paper entitled "Pitfalls of Data Analysis" written by Clay Helberg M.S. discusses some common pitfalls of the use of statistics and offers ways to ensure that statistics are clear and accurate (Helberg 1). ... The paper then goes on to consider three broad classes of statistical pitfalls. 1. ... The next example of bias illustrated by Helberg is the use of statistical assumptions. ... Helberg states that improper use of statistical power leads to errors. ... The final categories of statistical abuses are problems with interpretations of results. ...
Statistical Data Analysis Equality is something that many races, ethnic groups, religious factions and simply "different people" have strived for since anyone can remember. Even specific genders have desired to be treated equally. Women of all races have struggled for numerous years for equalit...
External Analysis 4 2.1 Industry Overview 4 2.2 Porter's 5-Forces Analysis 4 2.3 GDPEST Analysis 7 3. Internal Analysis 8 3.1 Financials 8 3.2 Value Chain 10 3.3 VRIS 10 3.4 Core Competence/Strategic Intent 11 4. ... External Analysis 2.1 Industry Overview One organization under the model market is Lego. ... Statistically, kids perform perhaps not play just as much as they utilized to. ... A Five-force analysis for the model market will more determine Lego's existing position within the model sector. ...
Statistical methods used: We found that the statistical method most commonly used in entry mode research is logistic regression. Logistic regression analysis was used in a total of 66 studies. ... Other statistical methods used were multinomial logit (in 20 studies), discriminant analysis (7), PROBIT (7), multiple regression (6), and exponential estimation (5). ... ANOVA (4), TOBIT (4), t-tests (4), structural equation modeling/LISREL (2), events analysis (1), and univariate analysis (1) were also used to test entry mode statistical models. ... Logistic regression (page 275) analysis has been ...
Chi Square is a non-parametric test of statistical significance for bivariate tabular analysis. ... Time Diptera (flies) Coleoptera (beetles) Evening 40 20 Late Evening 10 30 From the above bivariate tabular analysis, we can hypothesized that insects trapped by the pitcher plants is dependent to the time of the day, hence more flies are trapped in the evening as opposed to beetles which are mostly trapped in late evening. ... If our chi square value is larger than the critical value in that cell, our data present a statistically significant relationship between the v...
Method I tested my hypothesis by conducting several different statistical techniques. The first statistical technique I used was descriptive statistics. This statistical technique displayed for me histograms and stem-and-leaf plots. ... Results The first statistical measure I used was descriptive statistics. ... The statistical analysis computed by the SPSS did not give me the .05 or .01 critical values of r in order to reject or accept the hypothesis. ...
Statistical manipulation is well suited to quantitative data, as numbers are needed as the unit of analysis. We can also say that statistical results are more reliable with large amounts of data and the leads to quantitative research being well suited to large-scale projects (Denscombe, 1998). ... In the analysis of the data, it has to be defined and then manipulated according to its type, like nominal data, interval data or ratio data. The type of data suggests statistical methods that can be used and the intention (to find an association or a difference) determines the method used. ... ...
Statistical manipulation is well suited to quantitative data, as numbers are needed as the unit of analysis. We can also say that statistical results are more reliable with large amounts of data and the leads to quantitative research being well suited to large-scale projects (Denscombe, 1998). ... In the analysis of the data, it has to be defined and then manipulated according to its type, like nominal data, interval data or ratio data. The type of data suggests statistical methods that can be used and the intention (to find an association or a difference) determines the method used. ... ...
