Explore Correlation and Regression Using the explore function within SPSS gives a clearer view of how the data is represented. The explore function helps to assess the variables within the investigation by visually identifying various aspects of the data, therefor revealing the true nature of the data (Field, 2013). For example, the data in the Chamorro-Premuzic.sav file can be evaluated in a number of ways, for example analyzing the data based on the gender grouping allow one to visualize the…
Using the figures in the table above, the following regression equation was obtained. Y = 0.668 + (-0.016) X1 + (0.185) X2 + (0.224) X3 + (0.118) X4 + (0.270) X5 Interpretation 1. A constant value of 0.668 indicates that if there is no independent variable (Reliability, Responsiveness, Assurance, Empathy, and Tangible), the dependent variable (customer satisfaction) is positive. 2. X1 regression coefficient = -0.016 means that if the service of dimensions X1 has decreased; the level of customer…
Multicollinearity Multicollinearity is one of the common problems in spatial regression analysis. Sometimes some or all of the explanatory variable are highly correlated in the sample data, which means that it is difficult to tell which of them is influencing the dependent variable (Barrow, 2009, p. 306). Hence, to check whether the independent variables are correlated with each other, a correlation matrix for the three indicators was measured using excel. The correlation matrix in table 2…
Data analysis method First, using Kolmogorov-Smirnov Test to examine the normality of the variables in each of the hypotheses and assumptions of one to five using regression analysis and using software Spss18 and then fourth hypothesis using structural equation modeling (Path Analysis) using software Amos22 to confirm or refute the hypothesis of the research study. Hypotheses: 1. Empowerment dimension effect on turnover intention. 2. Interoperability dimensions have an effect on…
DISCUSSION Various studies all over the world have been conducted on the estimation of stature from the human skeleton by applying variable methods for the estimation but the easiest and the most reliable method is by regression analysis. In the present study the correlation of body height with both foot lengths and knee height along with a consideration of the age factor varying between 19years to 54 years. The right foot length from 20.7cms to 29.6 cms and the left foot length from 20.5 cms to…
As an entity that sells tickets to games, they need to forecast the demand for those tickets in order to help maximize their revenue potential. The chapter talks about regression analysis which the Orlando Magic uses, more specifically multiple regression analysis. The magic applied the multiple-regression model using several key variables that can affect ticket sales to forecast demand more accurately for games. Some of the independent variables the magic used in their forecasting model…
linear relationship between my X and Y variables is even worse than the linear relationship for equation 1. In addition, there are no significant changes in the slope coefficients (β_1and γ_2) for X_1 and X_2. This indicates a consistency among my regressions that provides further evidence to accept my null hypothesis that the more contacts improve sale premia. At all levels of alpha and with all equations, the F-test requires that I accept the null hypotheses that H_0: α_1=0 and H_0: α_2=0.…
comprehensive training program covering store operations, marketing, finance, and human resources. Training consists of a five-day Franchise Development Program and four-days of Pizza Prep School. Test the statistical significance of the variables and the regression equation indicating how it will impact your decision to open the pizza…
Interpretation: The regression model for beer sales share is: From the second hypothesis test, the null test of f-test can be rejected, which means one of the coefficient is non-zero, thus, the model is succeed. In addition, it shows that EDUC, SQRDHHSIZE, HSIZEAVG are all…
RESULT & OBSERVATION The hypothesis for statistical testing must be written as Null hypothesis (H0) and Alternate (Research) hypothesis (H1) form. H0 1= There is no significant relationship between body height, foot length and knee height in both the sexes. H1 1= There is relationship between body height, foot length and knee height and are significant in both the sexes. H0 2= Body height is equally…