Homework assingment 3.5 A regression model is built for a data set whose x-values are {5, 6.6, 7.5, 8.1, 9.3, 10.2, 11.4, 12.6, 13.9, 15}. Then confidence intervals for y are constructed at the four x-values 4.6, 5.2, 9.9, and 14.8. Which confidence interval is going to be the narrowest? Which confidence interval was better off not being constructed at all, if any?

Homework assingment 3.5 A regression model is built for a data set whose x-values are {5, 6.6, 7.5, 8.1, 9.3, 10.2, 11.4, 12.6, 13.9, 15}. Then confidence intervals for y are constructed at the four x-values 4.6, 5.2, 9.9, and 14.8. Which confidence interval is going to be the narrowest? Which confidence interval was better off not being constructed at all, if any?

A First Course in Probability (10th Edition)

10th Edition

ISBN:9780134753119

Author:Sheldon Ross

Publisher:Sheldon Ross

Chapter1: Combinatorial Analysis

Section: Chapter Questions

Problem 1.1P: a. How many different 7-place license plates are possible if the first 2 places are for letters and...

See similar textbooks

Similar questions

Brain size Does your IQ depend on the size of yourbrain? A group of female college students took a test thatmeasured their verbal IQs and also underwent an MRI scan to measure the size of their brains (in 1000s of pix-els). The scatterplot and regression analysis are shown, and the assumptions for inference were satisfied.Dependent variable is: IQ_VerbalR-squared = 6.5% s = 21.5291 df = 18Variable Coefficient SE(Coeff)Intercept 24.1835 76.38Size 0.098842 0.0884a) Test an appropriate hypothesis about the associationbetween brain size and IQ.b) State your conclusion about the strength of thisassociation.
A data ninja has created a multiple regression model. There are 3 independent variables in the model. The variables and their P-values can be found in the table below. Independent VariableP-ValueX1: Advertising0.142X2: Social Media0.006X3: Coupons0.071 QUESTION: Study the P-Values. At 90% confidence level, which independent variable(s) can be proven significant in the model? Group of answer choices X3 only X1 only X2 only X2 and X3
The following regression is fitted using variables identified that could be related to student requestedloan amount, LOAN-AMT($) for returning students of a certain University. ????−??? = ? + ? ?????? + ? ???? + ? ????Where ACCEPT = the percentage of applicants that was accepted by the university, PREV = previous loan amount and OUTS =outstanding loan amountThe data was processed using MNITAB and the following is an extract of the output obtained:EXHIBIT 2Predictor Coef StDev T PConstant -26780 6115 * 0.000ACCEPT 116.00 37.17 3.14 0.003PREV -4.21 14.12 -0.30 **OUTS 70.85 15.77 4.49 0.000S = 2685 R-Sq = 69.6% R-Sq(adj) = 67.7%Analysis of VarianceSource DF SS MS F PRegression 3 808139371 269379790 37.37 0.00Residual Error 49 353193051 7208021Total 52 1161332421a)What is dependent and independent variables? b)Fully write out the regression equation c)What is the sample size used in this investigation? d)Fill in the blanks identified by ‘*’ and ‘**’. e)Is β significant, at the 5% level of…
The accompanying table lists systolic blood pressures (mm Hg) and diastolic blood pressures (mm Hg) of adult females. Find the prediction interval for a systolic blood pressure of 121mm Hg using a 99% confidence level. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. Systolic Diastolic 125 69 104 65 129 75 108 65 157 74 95 53 155 89 110 69 120 69 115 73 101 59 127 67 The 99% prediction interval for a systolic blood pressure of 121mm Hg is ____mm Hg<y<___mm Hg.
The fish in my pond have mean length 10 inches and mean weight 4 pounds. The correlation coefficient of length and weight is .9. If the length of a particular randomly selected fish is reported to be 10 inches, then what should we predict for the weight of that fish using simple linear regression? 4 10 2.5 NOT ENOUGH INFORMATION GIVEN NONE OF THE OTHERS
please answer the question and show work!! thank you also please make sure if writing on paper handwriting can be read thank you again :)
2) and a new HPLC instrument. Compare the 2 data sets using graphed regression analysis in Excel to see if the methods can be determined as equal. Discuss your conclusion. You should provide a screengrab of your graph(s). The level of phthalates in 20 water samples was determined by an old HPLC instrument Old HPLC New HPLC 1 10.1 10.5 2 11.3 11.0 3 12.4 11.4 4 9.8 9.3 5 8.6 8.3 9.2 8.9 7 10.2 10.3 8 10.6 10.3 8.9 9.3 10 10.8 10.9 11 11.5 11.0 12 12.4 12.1 13 10.2 10.8 14 9.8 9.9 15 8.4 7.9 16 8.4 8.0 17 10.4 10.9 18 9.8 10.5 19 9.3 10.6 20 11.2 11.1
Could you help me with these 2 please
6
Match to the appropriate answer. You will not use all options and you may not use each option more than once. Dummy variable Coefficient (beta) Correlation coefficient Heteroscedasticity p-value R² + Residual A. Used with categorical variable B. Lower value means statistical significance at a higher confidence level. C. When the distribution of residuals does change as the independent variable changes. D. Higher value means statistical significance at a higher confidence level. E. Difference between the predicted and observed values. F. When the distribution of residuals does not change as the independent variable changes. G. Min=-1, Max=1 H. Min-0, Max=1 1. Used to determine how much dependent variables changes if independent variable changed.
4-15. Data on grade point`average and IQ were obtained for 12 high school students. Grade Point Average IQ Grade Point Averuge 116 2.9 126 2.1 2.2 129 2.7 122 3.1 123 2.1 114 121 í.7 109 2.3 131 3.3 132 3.4 134 3.5 140 2.9 a. Use regression analysis to estimate the effect of IQ on grade point average. Write the equation, 1-statistics, and the coefficient of determination. Is the re- sult consistent with your prior expectations? Explain. b. Forecast the grade point average for a student with an IQ of-120 and for a student with an IQ of 150. Which forecast do•you have more confidence in? Why?
Public health researchers would like to evaluate whether information on age (in years) and weight (in kg) could be used to predict shoe size of students. The output of the linear regression analysis is given below. 95% confidence interval Unstandardized Beta t Lower bound Upper bound (Constant) 6.493 1.121 -5.191 18.177 Age (years) -0.462 -1.617 -1.037 0.114 Weight (kg) 0.155 7.568 0.114 0.197 Dependent variable: shoe size A) Write null hypotheses for the linear regression analysis. B) Interpret the results of the linear regression in not more than 100 words. C) Write a complete equation for the results of the linear regression analysis