Question

In: Math

The R library faraway contains the pima dataset. We will fit a model with test as...

The R library faraway contains the pima dataset. We will fit a model with test as a response and bmi (only) as a predictor to see the relationship between the odds of a patient showing signs of diabetes and his/her bmi. The odds o and probability p are related by:

o = p/(1-p), p = o(1+o)

Using the GLM function:

a. Please estimate the amount of increase in the log(odds) when the bmi increases by 10.

b. Give a 95% CI for the estimate.

Solutions

Expert Solution

95% CI for bmi = intercept +/- z * std error

where z for 95% conf interval is -/+1.96

0.09353 +/- 1.96 * 0.01205  

So., the 95% CI is 0.069912 to 0.117148

Hope the above answer has helped you in understanding the problem. Please upvote the ans if it has really helped you. Good Luck!!


Related Solutions

Using the Motor Trend Car Road Tests dataset mtcars, in faraway R package, fit a model...
Using the Motor Trend Car Road Tests dataset mtcars, in faraway R package, fit a model with mpg: Miles/(US) gallon as the response and the other variables as predictors. (a) Which variables are statistically significant at the 5% level? For each and every test provide the null and alternative hypotheses, critical region (or rejection region), test statistics and your conclusions. (30) (b) What interpretation should be given to the coefficient for vs: Engine? (3) (c) Compute 90 and 95% confidence...
Work these in R. Using library(resampledata) and the dataset Spruce to conduct a test to see...
Work these in R. Using library(resampledata) and the dataset Spruce to conduct a test to see if the mean difference in how much the seedling grew (in height) over the course of the study under these two treatments are significantly different from each other. Answer the following: a) Set up a hypothesis using appropriate notation. b)Find the value of the observed test statistic using R. c)Compute the P-value of the observed test statistic using a permutation distribution with N= 10^5-1....
1. The dataset prostate (in R package ”faraway”) is from a study on 97 men with...
1. The dataset prostate (in R package ”faraway”) is from a study on 97 men with prostatecancer who were due to receive a radical prostatectomy.Fit a model withlpsa(y) as the response variable andlcavol(x) as the predictor andanswer the following question: •Calculate and plot the 90%confidenceandpredictionbands. Which type ofintervals are wider?
Solve using R. You will need library(resampledata) and the dataset FlightDelays. Conduct a hypothesis test to...
Solve using R. You will need library(resampledata) and the dataset FlightDelays. Conduct a hypothesis test to see whether there is a difference in the variances of flight delay length between the two airlines. 1) Set a hypothesis for this test using appropriate notation 2) Using R, find the value of the observed test statistic 3) Using R, compute the P-value of the observed test statistic using a permutation distribution with N=10^5-1 resamples. If possible use comments so that it is...
Fitting a linear model using R a. Read the Toluca.txt dataset into R (this dataset can...
Fitting a linear model using R a. Read the Toluca.txt dataset into R (this dataset can be found on Canvas). Now fit a simple linear regression model with X = lotSize and Y = workHrs. Summarize the output from the model: the least square estimators, their standard errors, and corresponding p-values. b. Draw the scatterplot of Y versus X and add the least squares line to the scatterplot. c. Obtain the fitted values ˆyi and residuals ei . Print the...
In R: Consider dataset “juul” from library “ISwR”. (juul is a built in data set) Are...
In R: Consider dataset “juul” from library “ISwR”. (juul is a built in data set) Are the means of igf1 equal among tanner groups at 5% level? Please use the six step process to test statistical hypotheses for this research problem. Note: You need to convert tanner from numeric to factor type and ignore all the NAs.
The dataset ’anorexia’ in the MASS package in R-Studio contains data for an anorexia study. In...
The dataset ’anorexia’ in the MASS package in R-Studio contains data for an anorexia study. In the study, three treatments (Treat) were applied to groups of young female anorexia patients, and their weights before (Prewt) and after (Postwt) treatment were recorded. The three treatments adminstered were no treatment (Cont), Cognitive Behavioural treatment (CBT), and family treatment (FT). Determine at the 5% significance level if there is a difference in mean weight gain between those receiving no treatment and those receiving...
The dataset ’anorexia’ in the MASS package in R-Studio contains data for an anorexia study. In...
The dataset ’anorexia’ in the MASS package in R-Studio contains data for an anorexia study. In the study, three treat- ments (Treat) were applied to groups of young female anorexia patients, and their weights before (Prewt) and after (Postwt) treatment were recorded. The three treatments adminstered were no treatment (Cont), Cognitive Behavioural treatment (CBT), and family treatment (FT). Determine at the 5% significance level if there is a difference in mean weight gain between those receiving no treatment and those...
Using the "mammals" dataset from the "MASS" library in R: body brain Arctic fox 3.385 44.50...
Using the "mammals" dataset from the "MASS" library in R: body brain Arctic fox 3.385 44.50 Owl monkey 0.480 15.50 Mountain beaver 1.350 8.10 Cow 465.000 423.00 Grey wolf 36.330 119.50 Goat 27.660 115.00 Roe deer 14.830 98.20 Guinea pig 1.040 5.50 Verbet 4.190 58.00 Chinchilla 0.425 6.40 Ground squirrel 0.101 4.00 Arctic ground squirrel 0.920 5.70 African giant pouched rat 1.000 6.60 Lesser short-tailed shrew 0.005 0.14 Star-nosed mole 0.060 1.00 Nine-banded armadillo 3.500 10.80 Tree hyrax 2.000 12.30...
What are the R codes for these questions below: 1. Load the library {car}, which contains...
What are the R codes for these questions below: 1. Load the library {car}, which contains the Salaries data set. #Then, list the first few records with head(Salaries). The display the summmary() for this dataset, which will shows frequencies. Then, load the library {psych} which contains the describe() function and use this function to list the descriptive statistics for the dataset. 2. Load the coefplot library and display a coefficient plot for lm.fit.2 <- lm(salary~sex+yrs.since.phd, data=Salaries) using the coefplot() function....
ADVERTISEMENT
ADVERTISEMENT
ADVERTISEMENT