Suppose a data set of 4,000 observations (n - 4,000) was analyzed using OLS to examine the factors influencing students' college grade pointaverages. The regression results are as follows, with standard errors in parentheses:colgpa-1.550 +0.0010 sat-0.03 hspere-0.05 hsize +-0.007 haize(0.00) (0.00006) (0.0007)(0.01)(0.000)wherecolgpa- GPA in collegesat score on the SAT examhspere -percentile in high school graduating classhaize size of graduating class, in hundreds11-4,000R-0.254R²-0.253-0.45The predicted college GPA for someone who scored 1,120 on the SAT and graduated in the 27th percentile of his or her 700-person high schoolgraduating class, isSuppose you would like to construct a 95% confidence interval around the expected college GPA for someone with aat 1,120, hapere 27, andhaize 7.colga 1853+0.0010 sat(0,01)(0.0000)wherecolgpa-GPA in collegesat0 sat-1,1200.03 Asperc0-0.05 haize0+ 0.007 haize(0)(0000) The predicted college GPA for someone who scored 1,120 on the SAT and graduated in the 27th percentile of his or her 700-person high schoolgraduating class, isSuppose you would like to construct a 95% confidence interval around the expected college GPA for someone with sat= 1,120, hspere 27, andhsize=7.colgpa 1.853+0.0010 sato- 0.03 hsperc0-0.05 heize0+-0.007 hsized(0.01) (0.006)(7)wherecolgpa- GPA in collegesato-sat-1,120hspered-hapere-27haize0- haize-7-4,000R-0.254-0.253-0.45Hint: The 95% critical value of the t distribution is 1.96.(0.01)(0.001)The confidence interval around this expected value of college GPA would be1.96xNow suppose you would like to construct a 95% confidence interval around the college GPA for someone with sat 1,120, haperc = 27, andhaize 7, and not just the expected value of college GPA for these values of the explanatory variables.This confidence interval around this value of college GPA would be1.96x

In the question we are given the Regression equation for College gpa while independent variables in…

Answered: ppose a data set of 4,000 observations…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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You were given a dataset that includes 100 datapoints for expenses in Canadian dollars and waste in kilo grams. Based on the data you were able to calculate the following: The variance of the expenses = 78515151.52 The variance of the waste amount 14602.38, The R squared = 0.372460641, Intercept = cS 34046.3765, Total waste amount = 54420 kg ion Then the slope of the regression line is (approximately) O a 44,75124 O b. 54.420 Oc 5840 Od. 34.04637
. A professor at the University of Alabama was interested in evaluating the relationship between family support and delinquency. Using data collected on 4545 families, the researcher used regression to analyze the relationship. The results are presented below. Variables Entered/Removeda Model Variables Entered Variables Removed Method 1 Family supportb . Enter a. Dependent Variable: Delinquency b. All requested variables entered. Model Summary Model R R Square Adjusted R Square Std. Error of the Estimate 1 .249a .062 .062 1.59168 a. Predictors: (Constant), Family support ANOVAa Model Sum of Squares df Mean Square F Sig. 1 Regression 759.204 1 759.204 299.671 <.001b Residual 11479.107 4531 2.533 Total 12238.311 4532 a. Dependent Variable: Delinquency b. Predictors: (Constant), Family support…
A researcher is curious about the association between the association between the number of hours that a student slept the night before the exam and exam score. The appropriate statistical test would be an: a. regression b. independent-samples t-test c. Pearson Correlaation Coefficient d. z test
Consider the following data: x¯ = 20, sx = 2, y¯ = −5, sy = 4, and b1 = 0.40. Which of the following is the sample regression equation?
I need help with these questions. I did part A with the regressional analysis and got 92.5% for the coefficient determination.
Listed below are systolic blood pressure measurements (in mm H) obtained from the same woman. Find the regression equation, letting the fight arm blood pressure be the predictor (x) variable, Find the best predicted systolic blood pressure in the left arm given that the systolic blood pressure in the right arm is 90 mm Hg. Use a significance level of 0.05. Right Arm 100 99 93 76 77 9 Left Arm 176 169 118 145 146 EB Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is 9 = 7 + 7x (Round to one decimal place as needed.) Given that the systolic blood pressure in the right arm is 90 mm Hq, the best predicted systolic blood pressure in the left arm is mm Hg. (Round to one decimal place as needed.)
Fifty matched pairs of magnitude/depth measurements randomly selected from 10,594 earthquakes recorded in one year from a location in southern California. Find the best predicted depth of an earthquake with a magnitude of 2.2. Use a significance level of 0.05. : Click the icon to view the earthquake measurements. Click here to view the critical values of the Pearson correlation coefficient, r. The regression equation is y = ? + ?x. 回-ト i Critical Values of the Pearson Correlation Coefficient r (Round to one decimal place as needed.) Critical Values of the Pearson Correlation Coefficient r α= 0.05 0.950 0.878 0.811 0.754 0.707 0.666 0.632 0.602 0.576 a = 0.01 0.990 0.959 0.917 0.875 0.834 0.798 0.765 0.735 0.708 NOTE: To test Ho: p=0 Jagainst H,: p#0, reject H, if the absolute value of r is greater than the critical value in the table. 5 10 11 12
Listed below are altitudes (thousands of feet) and outside air temperatures (°F) recorded during a flight. Find the (a) explained variation, (b) unexplained variation, and (c) indicated prediction interval. There is sufficient evidence to support a claim of a linear correlation, so it is reasonable to use the regression equation when making predictions. For the prediction interval, use a 95% confidence level with the altitude of 6327 ft (or 6.327 thousand feet). Altitude 8 15 22 28 31 33 Temperature 56 39 24 - 28 - 41 - 60 a. Find the explained variation. (Round to two decimal places as needed.)
Consider the following computer output from a multiple regression analysis relating the cost of car insurance to the variables: number of car accidents, driver's credit score, and safety rating of the car. Answer Coefficients Coefficients Standard Error 680 Intercept Car Accidents (In last 3 years) Credit Score -74.80 Safety Rating - 104.25 122.40 Does the sign of the coefficient for the variable number of car accidents make sense? 79.71 14.75 8.89 11.46 t Stat P-value 8.531 0.0000 0.0000 8.298 -8.414 -9.097 0.0000 0.0000 O Yes, because it is expected that as the number of car accidents increases then the cost should also increase. O No, because it is expected that as the number of car accidents increases then the cost should also increase. O Yes, because it is expected that as the number of car accidents increases then the cost should decrease. O No, because it is expected that as the number of car accidents increases then the cost should decrease. Keyp- Keyboard Short Tables
Consider the following computer output from a multiple regression analysis relating the price of a used car to the variables: age of car, mileage, and safety rating. Intercept Age (Year) Mileage (in Thousands) Safety Rating Does the sign of the coefficient for the variable age make sense? Answer Coefficients 23287.65 7671.28 -551.27 Coefficients 894.16 Standard Error 3327.39 8976.75 45.10 96.36 t Stat 6.999 0.855 - 12.223 9.280 P-value 0.0000 0.3965 0.0000 0.0000 O No, because it is expected that as age increases then the price should decrease. O Yes, because it is expected that as age increases then the price should also increase. O No, because it is expected that as age increases then the price should also increase. O Yes, because it is expected that as age increases then the price should decrease. Tables Key Keyboard Shor
QUESTION 5 A hospital analyzed, for several randomly selected employees, the relationship between the employee's annual and the distance the employee must travel between home and work (in 10s of miles). The regression analysis sh -50.40. What is the sample correlation? a. -0.9073 b. None of the answers is correct C. -0.8256 d. -0.9525 e. -0.8871 QUESTION 6 Dep.=Annual Sales Indep.=Salespersons SUMMARY OUTPUT Regression Statistics Multiple R Click Save and Submit to save and submit. Click Save All Answers to save all answers. MacBook 000 D00 F4
Answer the questions based on the following data table. What is the value of the correlation coefficient (r) between the variables? (round to 2 decimals) How much of the variation between the variables is explained by the regression model (R-sqr)? (round to 2 decimals) How far off (std error) will the regression model typically be in predicting y? At what level is the regression statistically significant (i.e.,Sig.F)? decimals) What is the slope (coefficient of x) of the linear regression equation? 2 decimals) What is the y-intercept of the linear regression equation? (round to 2 If given an x value of 3.6, what value of y does the regression equation predict? (round to 2 decimals) (round to (round to 2 decimals)

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