Question 2 It would be a one-tailed test. Hypothesis for an one-tail test: Ho:μHo≥μHa Compute Zobt: The Z critical Value = ±1.645 Ho: should be rejected, the researcher should conclude that the new toothpaste prefix better at helping prevent cavities. 95% confident that the population mean is within the range: Question 4 This is a type two error Question 6 Yes, this is a two-tailed Ho:μo≠μ1,μo<μ1 95% Confidence interval: 56.80479 < µ < 61.19521 2.160 No, The researcher
Data Analysis and Interpretation In selecting the appropriate statistical test for your data analysis, you need to identify the question or hypothesis for the study. Are you trying to compare groups, interested in finding out if there is a relationship between two or more variables, or are you trying to predict. Some factors to consider as well are the following: (1) Research design, (2) Level of measurement: nominal, ordinal, interval, ratio, (3) Number of groups, and if they are correlated (dependent)
treated as ordinal or interval data. An underlying reason for analyzing ordinal data as interval data might be the contention that parametric statistical tests are more powerful that nonparametric alternatives. Also, conclusions and interpretations of parametric tests might be considered easier to interpret and provide more information than nonparametric alternatives (Allen and Seaman, 2007,). The data in this study was treated as ordinal data because it was not possible for the participants to know the
difficult by the fact that patients did not speak English and Mendel did not speak Spanish. At that time, it had become a popular new tranquilizer “reserpine”, which provided good results, especially for such patients. Hospital managers decided to test those medicines. Patients were not informed about the fact that some of them received real medicine, and others received sugar pills. Doctors did not know as well, which of the patients received the real medicine and who did not. The experiment lasted
implement the theory and methods of modern statistics to various different fields. Many useful elements were included in this course like descriptive statistics, inferential statistics, hypothesis development and testing, selecting appropriate statistical tests and evaluating the statistical results; which aides me to make better decisions in my personal and professional life. The intention of this paper is to be able to review each of these individual elements that we studied throughout this course. Descriptive
distribution on a graph. The Quantitative method provides the similarities while nonparametric statistics measure the degree of similarities between the two distributions. These nonparametric statistics consist of two tests, the Kolmogorov goodness of fit test (kgf test) and the Wilcoxan signed rank test (Wsr test). The kgf test not only employs the cumulative relative frequency of each distribution, but also calculates a test statistic based on the largest vertical distance between the two crfs. The author
distribution on a graph. The Quantitative method provides the similarities while nonparametric statistics measure the degree of similarities between the two distributions. These nonparametric statistics comprise of two tests, the Kolmogorov goodness of fit test (kgf test) and the Wilcoxan signed rank test (Wsr test). The kgf test not only employs the cumulative relative frequency of each distribution, but also calculates a test statistic based on the largest vertical distance between the two crfs. The
standard deviation certain percentages of the population lie within each portion. Choosing Between Parametric and Nonparametric Statistics Parametric statistics are based on certain assumptions about the nature of the population in question. The most common assumptions are: The data reflect an interval or ratio scale. The data fall in a normal distribution. Nonparametric statistics are not based on such assumptions. Descriptive Statistics This statistics describe a body of data. Three
Test adani wilamar SAMPLE DESIGN Research Design is conceptual structure within which research is conducted. It constitutes the blue print of collection, measurement and analysis of data .Research Design is needed because it facilitates the smooth sailing of various research operations, thereby making research as efficient as possible yielding maximum information with minimum time, effort and money. Research Design stands for advance planning of methods to be used for collecting relevant data
(Zheng & Agresti, 2000). GLM is the underpinning for the t-test, ANOVA, ANCOVA, regression analysis and various multivariate methods. Compare and contrast parametric and nonparametric statistics. Why and in what types of cases would you use one over the other? Parametric test make assumptions concerning estimates from population parameters with a normality of the distribution and commonly use interval or ratio data; nonparametric tests do not involve any population parameters and do not require