Data analysis

Case 1: Forecasting In this section, the paper focuses on analyzing historical data with a view of forecasting expected monthly sales. The case requires monthly sales to be projected, given the assumption that the actual monthly sales are correlated with the number of hits on the company’s website in the previous month. Consequently, the historical data on actual sales and number of hits are both used to forecast the expected sales over a period of three months. Notably, forecasting entails…

An excellent analysis allows the marketer to sidestep pitfalls, lure investors and most significantly, attract clients. It is desirable to realize that launching new products may necessitate the creation of business plans and approaches for different purposes. Where the launching of an item is an internal plan, there is no need for industrial data to corroborate the forecasted market because market analysis might be necessary. If the firm is seeking external funding, market analysis tends to be…

2.4. Data Analysis For the analysis of the quantitative data, one-way ANOVA, Chi-Square test of independence and descriptive statistics were applied. For ANOVA, in order to determine which groups caused the significant difference, Tamhane T2 test, one of the most common post hoc (multiple comparison) tests, was used. For the normal distribution of data, the skewness and kurtosis coefficients were examined. For the normality test skewness coefficient of a distribution taken in the range of -1.5…

MEDSCI 725: EXPERIMENTAL DESIGN Ellynn Sy ANCOVA and More Complex Designs (Parametric Tests) Introduction Analysis of covariance (ANCOVA) is most frequently used to refer to the statistical technique that combines regression and ANOVA [1]. It offers a way to obtain a more precise assessment of the effect of the experimental manipulations on the dependent variable. An ANCOVA design requires the measurement of one or more other variables. These variables are covariates, predictor, variables,…

Results Pearson-R Correlation The data underwent correlational analysis using the Pearson-R coefficient and obtained the following results: Table 1. N E O A C SPS Pearson Correlation .562** -.273** .022 -.294** -.218** Sig. (2-tailed) .000 .000 .676 .000 .000 N 372 372 372 372 372 ** Correlation is significant at the 0.01level (2-tailed) Based on the table above, the independent variables N (.562), E (-.273), A (-.294), and C (-.218) are significantly correlated to the dependent variable,…

the regression, correlation test between variables should be done in the model to eliminate the multicollinearity effect. Based on the results of F and Hausman test, a fixed effects model is selected from three basic estimation techniques for panel data, including simple cross-section, fixed and random effects approaches. Finally, t-statistic and F-statistic show the significance level of individual variable and overall…

By considering the recorded Blood pressure data, among the synthesized derivatives, 9c has shown significant anti-hypertensive activity and prolonged duration of action when compared to the standard drug Propranolol but onset of action is slightly delayed than standard drug, which may be due to…

and complicated projects.. This is more emphasis placed on risk analysis. We called as Spirals because each phases are iteration. Principle of Spiral Methodology The spiral model is describe 4 phases. 1. Planning phase In this stage we do requirement study and gathering them. Therefore identify System requirement, Subsystem requirement, unit requirement and finalize list of all requirement are done in this step. 2. Risk Analysis phase All the requirements will study and identify the risk…

Aside from the general issue regarding survey data and their inability to accurately represent reality, there are no overtly obvious issues regarding the measurement or operationalization of this variable. The central independent variable I chose was number of group memberships. The variable was measured by simply asking respondents for the number of groups that they belonged to. The unit of analysis in individuals, and the variable is continuous. The mean is 0.833, indicating that the number…

Title: Application of Self-Organization Neural Network Technique (SOM) to Optimize Finite- Element Partial Differential Equation (PDEs) Results in Square-Shaped Structures Analysis. The finite-element method (FEM) is a computationally method for solving partial differential equations (PDEs) with specific boundary conditions over a domain. When we applying the FEM to a domain, it has to divide to a finite number of elements and nodes. The collections of the elements and nodes form the…