Exercise 2. Consider the iris data set. (a) Fit a linear regression model for Sepal.Width using S...
Exercise 1. For this exercise use the bdims data set from the openintro package. Type ?bdims to read about this data set in the help menu. Of interest are the variables hgt (height in centimeters), wgt (weight in kilograms), and sex (dummy variable with 1-male, 0-female). Since ggplotO requires that a categorical variable be coded as a factor type in R, run the following code: library (openintro) bdíms$sex2 <-factor (bdins$sex, levels-c (0,1), labels=c('F', 'M')) (a) Use ggplot2 to make a...
in R For the iris dataset, store the 50 sepal lengths for the 50 versicolor rises in a vector x For the iris dataset, store the 50 sepal lengths for the 50 virginica irises in a vectory What are the means and the variances of x and y? The variances "seem" different. Perform Welch's t-test that is appropriate in such cases to check if the mean sepal lengths of Versicolor and Virginica irises are significantly different. What is the p-value...
For Questions 4-11, use the swiss dataset, which is built into R. Fit a multiple linear regression model with Fertility as the response and the remaining variables as predictors. You should use ?swiss to learn about the background of this dataset. 9. 1 Run Reset Report the value of the F statistic for the significance of regression test. Enter answer here point 10. 1 Run Reset 0.01. What decision do Carry out the significance of regression test using a you...
HELP ASAP Suppose you are wishing to fit a multiple linear regression model using one categorical variable that can take on 17 different values. For example, if you wished to use the months of the year in your model, the categorical variable "month" would have 12 different values: January, February, March, etc. In general, how many dummy variables would you need to incorporate into your model to completely capture the effect of all 17 conditions of a categorical variable on...
its 8.17 the one that is highlighted and I have also attached the models. Xi2: 0 1 0 a. Explain how each regression coefficient in model (8.33) is interpreted hene. b. Fit the regression model and state the estimated regression function. c. Test whether the X2 variable can be dropped from the regression model; use α 01 St ate the alternatives, decision rule, and conclusion. d. Obtain the residuals for regression model (8.33) and plot them against XiXz. Is there...
2. Suppose we observe the pairs (X, Y), i-1, , n and fit the simple linear regression (SLR) model Consider the test H0 : β,-0 vs. Ha : Aメ0. (a) What is the full model? Write the appropriate matrices Y and X. (b) What is the full model SSE? (c) What is the reduced model? Write the appropriate matrix XR. (d) What is the reduced model SSE? (e) Simplify the F statistics of the ANOVA test of Ho B10 vs....
2) Suppose the regression model y = B0 + B1x1 + B2x2 + B3x3 + B4x1x2 + B5x1x3 + B6x2x3 was fit to n = 27 data points with SSE = 2000.0. a) Set up the null and alternative hypotheses for testing whether the interaction terms are significant. b) Give the reduced model necessary to test the significance of the interaction terms. c) The reduced model resulted in SSE = 2800. Calculate the value of the test statistic appropriate for...
In this exercise use the Peruvian blood pressure data set, provided in the file peruvian.txt. This dataset consists of variables possibly relating to blood pressures of n = 39 Peruvians who have moved from rural high altitude areas to urban lower altitude areas. The variables in this dataset are: Age, Years, Weight, Height, Calf, Pulse, Systol and Diastol. Before reading the data intoMATLAB, it can be viewed in a text editor. This question involves the use of multiple linear regression...
Decide (with short explanations) whether the following statements are true or false. e) In a simple linear regression model with explanatory variable x and outcome variable y, we have these summary statisties z-10, s/-3 sy-5 and у-20. For a new data point with x = 13, it is possible that the predicted value is y = 26. f A standard multiple regression model with continuous predictors and r2, a categorical predictor T with four values, an interaction between a and...
3. The table below shows the regression output of a multiple regression model relating the beginning salaries of employees in a given company to the following independent variables: Sex : an indicator variable (1=man and 0-woman) ducation years of schooling at the time of hire Experience number of months of previous work experience Source Regression Residual Total Df 4 8822,387,82 254,407 92 MS F-value 23.763,297 5,940,82423.35 46,151,118 Coefficient table Variable Constant Sex Education Experience Months t-value 10.94 6.02 3.22 2.16...