Learning Objectives :
Sampling: theory and techniques of sampling, sampling distribution (average, proportion and variance). Statistical inference: flats, proportion, and variance. Statistical test: parametric and non-parametric, curve fitting, analysis of variance classification in the same direction. Bivariate Analysis: dependency analysis cross tabulation, correlation and regression analysis, one-way analysis of variance
Competencies :
This course gives students an understanding of the concept of the sampling distribution includes statistical distribution calculated on the basis of the samples. Inference on average, variance and the proportion will give understanding to the students how to make the expansion of the information contained in the sample to the population. With a non-parametric test students can understand how to perform inference when the assumption of normality was not met. By fitting curves student is expected to make predictions for a specific amount that depends on one or more other scale. Students are expected to compare several independent populations by studying the analysis of variance. By understanding the concepts of reliability and life testing students are expected to apply in matters relating to the life of a component
Subject :
Sampling: theory and techniques of sampling, sampling distribution (average, proportion and variance), statistical inference, statistical tests, bivariate analysis
