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Lesson 1  Introduction to Statistical Research Methods 
Lesson 2  Visualizing Data 
Lesson 3  Central Tendency 
Lesson 4  Variability 
Lesson 5  Standardizing 
Lesson 6  Normal Distribution 
Lesson 7  Sampling Distributions 
Lesson 8  Estimation 
Lesson 9  Hypothesis Testing 
Lesson 10  tTests for Dependent Samples 
Lesson 11  tTests for Independent Samples 
Lesson 12  Intro to OneWay ANOVA 
Lesson 13  OneWay ANOVA: Test significance of differences 
Lesson 14  Correlation 
Lesson 15  Linear Regression 
Lesson 16  ChiSquared Tests 
Afterward  
Index 
All the tests we’ve done so far have involved sets of data for which we could find the mean and standard deviation. However, sometimes we only have frequencies or proportions. For example, let’s say that marketing researchers post an article about a new product on Facebook, Twitter, LinkedIn, and Instagram. They want to determine if followers are more likely to read the article from a particular social network.
The company has the following numbers of followers on Twitter, Facebook, and LinkedIn (in thousands):
Facebook: 1991
Twitter: 821
LinkedIn: 1733
After releasing the article, the marketing researchers found the following numbers of people clicked the link:
ztests and ttests are parametric tests since they’re based on means and standard deviations. In this case, we need to do a nonparametric test to
determine if the number of people who clicked the link to the article is what we would have expected based on the number of followers on each social
network. This is the null hypothesis; the alternative hypothesis is that the number of people who clicked the link is different than what was expected.
In this case, we’ll do a chisquared goodnessoffit test to test the “fit” between observed and expected values. This involves computing a statistic:
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