Chi Squared Test using R programming

This video tutorial covers the chi-square test, focusing on two types: goodness of fit and test of independence. It explains hypothesis testing, null and alternative hypotheses, and statistical significance. The video demonstrates using R and the Tidyverse package to perform chi-square tests on flower data, converting numeric variables to categorical ones. It concludes with a discussion on expected values and introduces the next topic, simple linear regression.

What are the two types of chi-square tests mentioned in the video?

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Explain the concept of the null hypothesis in the context of the chi-square test.

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What is the significance level commonly used in hypothesis testing?

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What is the difference between the chi-squared test of independence and the goodness of fit test?

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What are the three species of flowers mentioned in the video?

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Explain the importance of using the Tidyverse package in R as mentioned in the video.

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Describe the process of converting a numeric variable into a categorical variable as discussed in the video.

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What is the chi-squared goodness of fit test used for?

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What does a p-value indicate in the context of the chi-square test?

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How do you interpret a statistically significant result in hypothesis testing?

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