Data Analysis And Interpretation Of The Data Essay

This article is well written with good general flow of thought and easy for the reader to follow. Survey methodology is employed to capture data for quantitative analysis.1

To avoid spurious results, unit root tests using Augmented Dickey-Fuller (ADF) (Dickey and Fuller, 1981) and Philips-Perron (PP) are performed to determine the time-series properties of the variables employed in the analysis. Two or more variables are said to be co-integrated when they exhibit long run equilibrium (relationship) if they share common trend(s).Therefore Auto-regressive distributed lag bounds approach (ARDL) is used to test it. The choice is based on several

Data used in this study is from a previously prepared collection for a different study and hence does not require any instruments. Use of Microsoft Excel and graphpad, an online software, helped in calculating the results and analyzing the data.

Paper 1: - An abstract of paper 1 gives overall view of the study, method of study conducted, selected participants and their numbers. It also provides the evidence that how the data and results were collected. The abstract is showing the objectives and have

Data analysis was done using the SPSS software (Statistical Package for the Social Sciences, version 18.0, SPSS). The plan of data analysis is as follows.

Also, this estimation allows the using of a lagged dependent variable to check whether there is a possible cointegration in the economic variables, with the possibility of one variable forecasting another (Campbell & Shiller, 1988). Including the lagged dependent variable can reduce the occurrence of autocorrelation arising

After careful data analysis, the need for instructional improvement on strategies to address the needs of English language learners it is apparent. Improving literacy skills is critical in decreasing achievements gaps of this subgroup. As mentioned previously, Pinewood maintains a school grade of a B; nevertheless, a focus on strategies to meet the needs of ELL students will benefit all students. With effort from all stakeholders, a focus on instructional strategies to improve learning of English learners could result in decreasing the achievement gap as well as increasing the school grade to an A, since many of these students count for the lowest 25%. Correspondingly, Marchand-Martella, Klingner, and Martella (2010) justify that

Data was recorded and analyzed by Statistical Package for Social Science SPSS v.11. Correlation studies and ANOVA used for statistical analysis. The P-value less than 0.05 considered as statistically significant.

Table: 1, represents the results of regression analysis carried out with the dependent variables of cnx_auto, cnx_auto, cnx_bank, cnx_energy, cnx_finance, cnx_fmcg, cnx_it, cnx_metal, cnx_midcap, cnx_nifty, cnx_psu_bank, cnx_smallcap and with the independent variables such as CPI, Forex_Rates_USD, GDP, Gold, Silver, WPI_inflation. The coefficient of determination, denoted R² and pronounced as R squared, indicates how well data points fit a statistical model and the adjusted R² values in the analysis are fairly good which is more than 60%, indicates the considered model is fit for analysis. Also, the F-Statistics which provides the statistical significance of the model and its probabilities which are below 5% level and hence proves the model’s significance.

Columns (2)-(4) in the table 3 show the results of different models when dependent variable is ETD1. The OLS estimates in column (2) have expected signs except the estimate of World\_GDP. The estimate of Exchange\_Vol shows that if the standard deviation increases by 1, then the ratio of export to domestic sales goes down by 0.03562 or approximately 3.6 percent. This estimate is statistically significant at 5 percent level (p-value of the estimate is 0.012). The estimate of Lag\_Exchange\_Vol is also highly statistically significant (p-value is 0.000) and shows that if the previous years standard deviation rises by 1, then the ratio of export to domestic sales falls by 0.09816 or approximately 9.8 percent. This reflects the fact that

According to the measured result of the OLS method, it is essential to discuss for the rationality of the regression econometric model at the moment. Apparently, we can see from the eighth table that R-squared is 0.8384 and Adjusted R-squared is 0.8297, which are close to 1. The Goodness of Fit measures fitting degrees of the model equation for the sample data on the whole. R-squared can assess, in other words, to what extent the regression model could interpret the sample data. Clearly, this regression equation has high quality of the fitting degree, suggesting that the Japanese CPI and the Indian CPI combined to have good explanatory power towards the bilateral exchange rate. However, this finding is not very encouraging because of the signs of the coefficients, which are contrary to the qualitative analysis in the APPP mathematical model. To be specific, the coefficient for the log of the Japanese CPI should be positive, but the result gained from OLS output shows that β1 is -2.1834 namely negative. A possible

First of all, variables should be given in log levels in order to alleviate the problem of serial correlation and the elasticity of the coefficients. The results of ADF unit root test in levels concludes that all three variables - seasonally

To modify the regression model before running the results, I do some diagnostic tests for the obtained series. Table A2 in the appendix I shows some descriptive statistics and diagnostic tests of data. First, the series average of these three areas is all strictly positive and greater than 1.5. The standard error of JPY series is 0.907 which is close to 1 while others are a long way from unity (0.284 and 0.331). In addition, the Jarque-Bera test statistics of all series are too large, which shows that all EUR, GBP and JPY series are not normally distributed. Second, p-values of Ljung-Box Q-statistic tests for level and squared residual in all series are 0. LB(Q) presents that all series have an autocorrelation for residuals and LB(Q2) shows that all series are conditionally heteroskedastic. In this way, ARCH or GARCH model can be utilized to improve the original model (3). Third, the unit root test. On 23

The stationary property of time series is tested by using Phillips-Perron (PP) unit root tests as PP-test has greater power than the Augmented Dickey and Fuller (ADF) test (Banerjee et al 1993). Another advantage of the PP tests over the ADF test is that the PP tests are robust to general forms of heteroskedasticity in the error term ut (Phillips and Perron 1988). Besides, unlike the ADF technique, the user does not have to specify a lag length for the test regression in the PP technique (Debnath, A., and N. Roy 2012). The result of unit root tests on the natural logarithms of per capita GDP and per capita electricity consumption is shown in

tools and approach as the main analytical methods. It includes OLS regression after testing stationarity, co-integration and causal relationships among the variables for time series data-set.