SPSS Normality Test | Model Assumptions Tests (Amharic) በአማርኛ

This video demonstrates how to assess the normality assumption of data in SPSS by utilizing the p-values from the Kolmogorov-Smirnov and Shapiro-Wilk tests, along with examining skewness and kurtosis statistics and visually interpreting histograms and QQ plots.
It also covers how to conduct various assumption tests, including those for sample size, identification, missing data, normality, linearity, multicollinearity, and independence of errors (such as autocorrelation and homoscedasticity).
Furthermore, the video will walk you through the process of conducting normality tests, data reflection to addressing negatively skewed data, transforming non-normal datasets, interpreting the results, and understanding their relevance to your research.

ይህ ቪዲዮ Kolmogorov-Smirnov and Shapiro-Wilk tests የ p-values በ Skewness እና kurtosis ስታቲስቲክስ በመመርመር እና ሂስቶግራምን እና የQQ ግራፍ በእይታ በመተርጎም በ SPSS ውስጥ ያለውን መረጃ መደበኛነት እንዴት መገምገም እንደሚቻል ያሳያል።

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→Lesson one course for missed data interpolation techniques using SPSS and introduction for AMOS Software Course:    • የአሞስ ሶፍትዌር መሰረታዊ ፅንሰ ሀሳብ  AMOS Software Le...  

Related keywords

Normality Test, SPSS, AMOS, Research HUB, Normal Distribution, Shapiro-Wilk, QQ-plot, Kolmogorov-Smirnov, linearity, multicollinearity, independence of errors, autocorrelation, homoscedasticity, reflection, transforming data, SPSS missing values, SPSS missing values analysis, Missing Data SPSS tutorial, SPSS normality test, Kruskal-Wallis test SPSS, Kolmogorov-Smirnov test SPSS