Analysis of factors affecting inflation rate in Rwanda (1990-2009)

3.2 DATA ANALYSIS

3.2.1 Methods

Method is defined as an ordered set of rules and principles of intellectual operations to do the analysis to achieve a result (WELMAN J. C and KRUGER S.J., 2001:36).

At the completion of our work, we have chosen the analytical method, this method allows to systematically analyzing all information and data collected. It allowed us to systematically analyze the inflation's relationship with others macroeconomic variables, to interpret and draw the conclusion.

a. Preparing data for analysis

Data will be entered in the tables where they will be clearly viewed and checked for errors. After these, they will be entered in the computer (SPSS software) for analysis.

b. Exploring and presenting data

Once data will have been entered and checked for errors, analysis will take place .The exploratory data analysis approach for this study will emphasize the use of diagrams to explore and understand the sense of the data. During this process, research questions and objectives will be kept in mind. It will help to formalize the practice of looking for other relationships in data which have not been thought of initially .So, at this stage, they will be a request to structure and label clearly each diagram and table to avoid possible misinterpretation.

c. Analyzing quantitative data

This will involve choosing the appropriate statistics to describe the data and the choice of the adequate statistics to examine relationships among data.

At this step, the researcher will be concerned with answering the question»how do some variables relate to others?» During the statistical analysis, this question will be answered by testing the likelihood of the relationship between specific variables. Here, relationships between Gross domestic product, Money Supply (M₂), Interest rate, Exchange rate and Inflation will be discussed. Testing whether the variables are significantly associated, the help of a simple regression model does it.

d. The general model of inflation

CPI=f (M2, GDP, LR, ER, ...)

Where

CPI : Consumer Price Index

M2 : Money Supply

GDP : Gross Domestic Product

LR : Lending rate

ER : Exchange rate

Specification of the model

CPI = á M₂^â GDP^ã LR^ë ER^ä ì

The model will be linealized by using logarithmic function. Hence

Log CPI= log á+ â logM₂+ã log GDP +ë log LR +ä log ER +log ì

Whereby â, ã, ë and ä are parameters of the model and log ì is the error terms

e. Describing data using statistics/data measurements

According to GUJARATI (2006:126) there are two mutually complementary approaches of hypothesis testing which is concerned with developing rules or procedures for deciding whether to reject or not reject the null hypothesis, namely confidence interval and test of significance. Both these approaches predicate that the variable (statistic or estimator) under consideration has some probability distribution and that hypothesis testing involves making statements or assertions about the value(s) of the parameter(s) of such distribution.

At this step the, T-test (the Student test) will be used to test the significance of the variables and F-test (Fischer' test) will be used to test the significance of the model.

The t-test is probably the most commonly used Statistical Data Analysis procedure for hypothesis testing. Actually, there are several kinds of t-tests, but the most common is the "two-sample t-test" also known as the "Student's t-test" or the "independent samples t-test".

The statistics t-test allows us to answer this question by using the t-test statistic to determine a p-value that indicates how likely we could have gotten these results by chance. By convention, if there is a less than 5% chance of getting the observed differences by chance, we reject the null hypothesis and say we found a statistically significant difference between the two groups.

The p-value is a numerical measure of the statistical significance of a hypothesis test. It tells us how likely it is that we could have gotten our sample data even if the null hypothesis is true. By convention, if the p-value is less than 0.05 (p < 0.05), we conclude that the null hypothesis can be rejected. In other words, when p < 0.05 we say that the results are statistically significant.