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Fit a linear model predicting the number of views (views), from the length of a video (length) and its average user rating

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HW week 12 w203: Statistics for Data Science w203 teaching team OLS Inference The file videos.txt contains data scraped from Youtube.com. 1. Fit a linear model predicting the number of views (views), from the length of a video (length) and its average user rating (rate). 2. Using diagnostic plots, background knowledge, and statistical tests, assess all 6 assumptions of the CLM. When an assumption is violated, state what response you will take. 3. Generate a printout of your model coefficients, complete with standard errors that are valid given your diagnostics. Comment on both the pr...

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Question Preview:

HW week 12 w203: Statistics for Data Science w203 teaching team OLS Inference The file videos.txt contains data scraped from Youtube.com. 1. Fit a linear model predicting the number of views (views), from the length of a video (length) and its average user rating (rate). 2. Using diagnostic plots, background knowledge, and statistical tests, assess all 6 assumptions of the CLM. When an assumption is violated, state what response you will take. 3. Generate a printout of your model coefficients, complete with standard errors that are valid given your diagnostics. Comment on both the practical and statistical significance of your coefficients.

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OLS Inference
The file videos.txt contains data scraped from Youtube.com.
1. Fit a linear model predicting the number of views (views), from the length of a video (length) and its
average user rating (rate).
mydata = read.table("G:/videos.txt", header = TRUE, sep = "\t")
fit <- lm(views ~ length + rate, data=mydata)

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