CHAPTER THREE METHHODOLOGY 3.0 Introduction This chapter focuses on the methodology that will be used in the study. The methodology was divided into two parts which is empirical model and econometric methodology. The econometric methodologies that have used include ordinary least square method and diagnostic test. Diagnostic test include stationarity test, multicollinearity test, test for model specification and significance of the model, heteroscedasticity test and correlation test. First, we check for stationary for each variable, if running an OLS regression on non-stationary series results in spurious regression. Second, we will test the variable either multicollinearity or no multicollinearity problem. If the independent variable are …show more content…
”An alternative but complementary approach to the confidence-interval method of testing statistical hypotheses is the test of significance approach” (Gurajarati& Porter, 2009, p. 115). As studied by Gujarati and Porter (2009), the test statistic and sampling distribution of such a statistic under the null hypothesis is the key idea of t-test. Confidence interval computed as 100 (1-∝) %. Reject null hypothesis (H0) if the test statistic is not in the confidence interval, this means that the test is statistically insignificant. However, not reject the H0 if the test statistic lies in the confidence interval. This means the test is statistically significant. 3.2.2 Test for Stationarity Before running an ordinary least square (OLS) regression, all the variables we used need to test whether the variables are stationary or non-stationary. If running an OLS regression on non-stationary series results in spurious regression. Augmented Dickey Fuller (ADF) test will be used to detect the stationary variables. The hypotheses under the ADF unit root test are: 〖 H〗_0: β=1 (The variable is nonstationary) H_1: β<1 (The variable is …show more content…
We reject H_0 in BG test if the test statistic is greater than the critical value. This show that the model is suffers from higher order of correlation. If the test statistic smaller than critical value, H_(0 )is not reject. This means that the model do not have any autocorrelation problem. Durbin Watson (DW) test DW test will be used to detect the first order correlation. Time series data mostly involved in regression problem and exhibit positive autocorrelation. The hypothesis usually under Durbin-Watson test is: H_0 : p = 0 H_(1 ): p > 0 The test statistic is d = (∑_(i=2)^n▒〖(μ_i - μ_(i-1))〗^2 )/(∑_i^n▒μ_i^2 ) or p= (∑_(i=2)^n▒〖μ_i μ_(i-1) 〗)/(∑_i^n▒μ_i^2 ) and, d ≈ 2(1 - p) Where μ_i = y_i- 〖ŷ〗_i and y_i and 〖ŷ〗_i are, respectively, the observed and predicted values of the response variable for individual i. The serial correlations will increase since d becomes smaller. Upper and lower critical value, d_(u ) and d_L have been tabulated for different of m (the number of independent variables) and n (number of observations). If d < d_L reject H_0 : p = 0 If d > d_(u ) do not reject H_0 : p = 0 If d_L < d < d_(u ) test is inconclusive or p = 1 (extreme positive autocorrelation), d ≈
This value was 0.019589 and with this statistic we were able to look at our hypothesis again. We could successfully conclude that our null hypothesis of the urine having no effect on the mice was wrong. This is because of P-value is <0.05 and represents that are data was statistically
X$ then the resulting error would have been precisely what really wanted to
In the second model our dummy variable membership in the EU was substituted by the dummy variable membership in the economic and monetary union and therefore we want to investigate whether it is advantageous to be a member of the Economic and Monetary Union or not. Now we denote year by t and country by i and use the following estimation for our basic model: lnfdiit = β0 + β1(wages)it + β2(lnpop)it + β3(lnpatent)it + β4(gdp_growth)it + β5(lnelectric)it + β6(openness)it + β7(unemployment)it
WK5Assgn3 +Dohnji +N.doc Part I T-TEST GROUPS= Worknow (0 1) /MISSING = ANALYSIS
If it is less than
In the next two paragraphs, I will show you the relationship
4.1 SEX-Dose sex differ will have influence on student’s score? From the histogram graphs above, we can confirm that mean in four subjects are trend to normally distributed. To test whether the mean for reading, writing, math and science is the same for males and females, an independent samples t-test tool is suitable because it can compare the means of a normally distributed interval dependent variable for two independent groups. H0: Females have no significant difference and high mean score than males in reading score H1:
33.385 million =10.235 million * exp (0.12*10) is the value of the of the population when the rate changes by an increase of 2%. 38.950 million =10.235 million * exp (0.14*10) is valuation of the future population when the rate increases by 4% from its original rate of 10% for a total rate of 14%. Each input is accurate in comparison with its Excel counterpart, however, the Word calculations will have greater precision due to the estimation of the of the Excel counterparts.
The p-value for the number of correct answers was 0.09288. This p-value indicates no significant difference between the two sets of data. The t-critical value for the number correct t-test was 2.0117 and the t-statistic was 1.71525. The t-statistic did not meet the t-critical value and therefore, the data also confirms there is no significance between the scores.
My auto-ethnographic paper will be grounded on a conflict that I have with myself, that has unfortunately been ingrained in my brain since I stepped foot at this institution. I identify as a Canadian born Chinese student, contentedly graduating this year at the University of British Columbia. I often question my positionality in this community as an asian at a predominantly white school that is on the traditional territories of the Aboriginal peoples. I sometimes get apprehensive or uncomfortable, I sometimes feel as if it is too complicated to talk to my white sorority sisters about and I know the conflict I have is also a conflict for many people of color. However, being a GRSJ student has allowed me to explore this in my own comfort in
After trying over 10 stats to find two that have a moderate correlation, only two of them were close. Finding a strong correlation is very difficult. This is because of all the little factors that
Cut-off date 27 February. Part1: Essay. ‘Evaluate the contribution of a qualitative approach to research on friendship’. Part2: DE100 project report – Method.
ANTH150 Mini Essay 2: Fieldwork Observation Word Count: 734 I conducted my ethnographic observations over the course of a few days. During my fieldwork observation, I recorded observations of customer behaviour, the general layout of the restaurant, culture significance, and décor. Siam Corner is located in Rouse Hill on Resolution Place. While entering, you can immediately feel the intimate environment of the restaurant and sense the sudden shift from the streets of Sydney to a Thai restaurant. It is viewed as an upscale restaurant with excellent service.
Ethnography is the study of social interactions, behaviours, and perceptions that occur within groups, teams, organisations, and communities. The central aim of ethnography is to provide rich, holistic insights into people’s views and actions. (Reeves et al, 2017). The term ethnography has come to be equated with virtually any qualitative research project where the intent is to provide a detailed, in-depth description of everyday life and practice. Qualitative research can be described as social science research in comparison to quantitative research is statistically orientated research (Hoey, 2017).