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Hypothesis Testing, Regression, ANOVA, Central Limit Theorem, Probability, SPSS, Inferential statistic, Distribution
Statistics provides tools and methods to find structure and to give deeper data insights. Both Statistics and Mathematics love facts and hate guesses. Knowing the fundamentals of these two important subjects will allow you to think critically, and be creative when using the data to solve business problems and make data-driven decisions.
This course covers the basic principles of statistical analysis beginning with the data types, descriptive statistics regarding whether their frequency distribution is skewed or follows a normal curve with most values around the mean. This course also covers the statistical inference and gives procedures for making inferences about populations parameters (mean μ and standard deviation σ), such as calculating the standard Z score, the mean of the means (and standard deviations) of different samples of the same size drawn from the same population, or by finding in the sample data a range of values (confidence interval) within which it can be assumed, with a certain confidence level, that the population parameter is located.
This course covers key statistical tests like student t-test, ANOVA (Analysis of variance), Regression, Correlation and many other statistical procedures including mean, standard deviation, confidence interval, hypothesis testing and interpretation of SPSS outcome is also part of this course.