Using R for code, no other languages are acceptable Crawl dataset is in the GLMsData package in R
Posted: Sun Jul 10, 2022 10:49 am
Using R for code, no other languages areacceptable
Crawl dataset is in the GLMsData package inR
A study of babies hypothesized that babies would take longer to learn to crawl in colder months because the extra clothing restricts their movement. From 1988-1991, the babies first crawling age and the average monthly temperature six months after birth were recorded. The parents reported the birth month, and age when their baby first crept or crawled a distance of four feet in one minute. Data were collected at the University of Denver Infant Study Center on 208 boys and 206 girls, and summarized by the birth month (data set: crawl).
1. Plot the data. Which assumptions, if any, appear to be violated? 2. Explain why a weighted regression model is appropriate for the data. 3. Fit a weighted linear regression model to the data, and interpret the regression coefficients. 4. Formally test the hypothesis proposed by the researchers. 5. Find a 90% confidence interval for the slope of the fitted line, and interpret. 6. Fit the unweighted regression model, then plot both regression lines on a plot of the data. Comment on the differences. 7. Perform a diagnostic analysis on the fitted un/weighted linear regression models. Suggest if any improvement is needed.
Crawl dataset is in the GLMsData package inR
- Using R For Code No Other Languages Are Acceptable Crawl Dataset Is In The Glmsdata Package In R 1 (42.79 KiB) Viewed 77 times
- Using R For Code No Other Languages Are Acceptable Crawl Dataset Is In The Glmsdata Package In R 2 (51.61 KiB) Viewed 77 times
1. Plot the data. Which assumptions, if any, appear to be violated? 2. Explain why a weighted regression model is appropriate for the data. 3. Fit a weighted linear regression model to the data, and interpret the regression coefficients. 4. Formally test the hypothesis proposed by the researchers. 5. Find a 90% confidence interval for the slope of the fitted line, and interpret. 6. Fit the unweighted regression model, then plot both regression lines on a plot of the data. Comment on the differences. 7. Perform a diagnostic analysis on the fitted un/weighted linear regression models. Suggest if any improvement is needed.