7.5.3 The relationship between type I and II errors.7.3.1 What do we do when we “analyze the data?”.7.2 Research Hypothesis and Null Hypothesis.7.1 Independent and Dependent Variables–Recap.6.5.2 Provide alternative explanations for the following correlations.6.5.1 Critique the following research studies from the perspectives of design and sample.6.3 Coming up with alternative explanations.6.2 Correlational/Observational Studies.5.9.6 Play with the dataset called “wvs” (world value survey).5.9.5 Interpret the meaning of each of the following correlation coefficients (you can do so after the examples, or feel free to use your own language if you could try to stay close to the interpretations provided here):.5.9.4 Given r (correlation coefficient)= 0.50 between X and Y, it follows that:.5.9.3 If the coefficient of correlation between X and Y is found to be -0.98, which of the following would be indicated?.5.9.2 Most of the examinees who score below the mean on Test 1 also scored below average in Test 2 the correlation between the two tests appears to be:.5.9.1 The lowest magnitude of correlation shown below is:.5.8.3 r could be unduely influenced by outliers.5.8.2 r only represents linear relationship.5.8 What the correlation coefficient does and does not mean.5.7 The Correlation Coefficient, aka Pearson’s r.5.4 Independent and Dependent Variables.5.3 What kind of data are suitable for correlation?.5.1 Plotting the relationship between 2 Variables.4.3 The z-score: How the normal distribution helps us making judgement.4.1 Why is the Normal Distribution so important?.3.4.2 What does the standard deviation mean?.3.4.1 How to Calculate Standard Deviation.2.4 Something Else about Categorical Variables–Risk and Change of Risk.2.3.3 Comparing a Histogram with a Bar Plot.2.3 Visualizing a Quantitative Variable.1.5.1 Identify main variables in research summaries.1.4.5 Categorical Variables and Quantitative Variables.1.2.3 Type, Run, and Save a line of code.1.2.1 Type and Save your code in the source editor.
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