Applied Statistics and Software
Applied Statistics and Software, available at Free, has an average rating of 4.83, with 17 lectures, based on 3 reviews, and has 700 subscribers.
You will learn about Knowing test runs Understanding non-parametric tests Processing of assigning ranks Understanding statistical errors Developing cluster analysis This course is ideal for individuals who are All levels It is particularly useful for All levels.
Enroll now: Applied Statistics and Software
Summary
Title: Applied Statistics and Software
Price: Free
Average Rating: 4.83
Number of Lectures: 17
Number of Published Lectures: 17
Number of Curriculum Items: 17
Number of Published Curriculum Objects: 17
Original Price: Free
Quality Status: approved
Status: Live
What You Will Learn
- Knowing test runs
- Understanding non-parametric tests
- Processing of assigning ranks
- Understanding statistical errors
- Developing cluster analysis
Who Should Attend
- All levels
Target Audiences
- All levels
Statistics is the discipline that deals with the synthesis, presentation, analysis and evaluation of numerical data in order to highlight proposed or assumed statistical meanings. So:
– statistics is a tool of the scientific method in science, which has several components (observation, hypothesis development, empirical data analysis, conclusion);
– variables are characteristics of reality that can take different values and are directly measurable;
– latent variables express science constructs measured indirectly by means of indicators (observed variables);
– descriptive statistics deals with the organization, synthesis, description and presentation of data;
– inferential statistics deals with the generalization of the results at the level of the population from which the sample was drawn and supports conclusions regarding the research hypotheses;
– the dependent variables are those that are the object of the researcher’s direct interest, being measured in order to draw a conclusion;
– the independent variables represent the condition or context from which the variation in the values of the dependent variable results and are those variables that the researcher manipulates;
– the population represents the totality of information unitsthat constitute the objective of interest of a research. As a rule, in social and human sciences the units of information represent persons;
– the sample represents a selection, based on a certain rule, of the units of information, with the aim of drawing conclusions about the population . A sample is representative when its characteristics fairly reproduce the characteristics of the population from which it was drawn. Sample representativeness is always imperfect (contains some error);
– measurement means assigning numbers or symbols to some characteristics of reality (objective or subjective) according to certain aspects (quantitative or qualitative);
– measurement levels are: nominal, ordinal, interval, ratio (they are ordered hierarchically which means that the higher scales include the properties of the lower scales). Levels are also called measurement scales. In statistical programs Measurecolumns contain three coding options (nominal, ordinal, scales). For the first two options (nominal and ordinal) the measurements are equivalent.
Course Curriculum
Chapter 1: Non-parametric tests
Lecture 1: Overview 1
Lecture 2: Non-parametric tests
Chapter 2: Chi-Square and binomial test
Lecture 1: Chi-Square and binomial test
Lecture 2: Runs and Kolmogorov-Smirnov tests
Chapter 3: Runs and Kolmogorov-Smirnov tests
Lecture 1: Overview 2
Chapter 4: Mann-Whitney test and ranks
Lecture 1: Mann-Whitney test and ranks
Chapter 5: McNemar tests
Lecture 1: McNemar tests
Chapter 6: Friedman's, Kendall's W and Cochran 's Q tests
Lecture 1: Friedman's, Kendall's W and Cochran 's Q tests
Chapter 7: Tests for k independent samples
Lecture 1: Overview 3
Lecture 2: Tests for k independent samples
Chapter 8: Statistical errors
Lecture 1: Statistical errors
Chapter 9: Cluster analysis
Lecture 1: Cluster analysis
Chapter 10: Cronbach's Alpha coefficient
Lecture 1: Cronbach's Alpha coefficient
Chapter 11: Primary tools and analyses
Lecture 1: Overview 4
Lecture 2: Primary tools and analyses
Chapter 12: Distributions and assumptions
Lecture 1: Distributions and assumptions
Chapter 13: Statistical decision
Lecture 1: Statistical decision
Instructors
-
Brindusa Covaci
Professor at the CBM International University -
Mihai Covaci
Professor at the CBM International University
Rating Distribution
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- 2 stars: 0 votes
- 3 stars: 0 votes
- 4 stars: 1 votes
- 5 stars: 2 votes
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