Question 2The 5 summary statistics for the homework and final scores of 100 randomly selected students from a large Physics class of 2000 students are given in the table on the right.Avg SDHomework78 8r = 0.5Final6515a. Find the slope and y-intercept of the regression equation for predicting Finals from Homework. Round your final answers to 2 decimal places. Don't round in the intermediate steps. Besure to enter y-intercept in the first blank and the slope in the second.Final =X Homework提交答案| Tries 0/99b. A 95% Confidence Interval for the population slope is closest toO(0.78 to 1.10)O(-0.94 to 2.81)O(0.31 to 0.63)O(0.62 to 1.26)提交答案Tries 0/99c. To test the null hypothesis Ho: slopepon = 0 against the alternative HA: slopepoD + 0 we'd compute a Z statistic. Select the closest Z statistic.O0.05O0.577O5.77O57.7提交答案| Tries 0/99d. If you square the Z stat, you get the Chi Square v statistic.提交答案Answer Submitted: Your final submission will be graded when the time limit is reached. Tries 1/99 LBIitAnd if you square the t, you get the Fv statistic.提交答案Answer Submitted: Your final submission will be graded when the time limit is reached. Tries 1/99 UMASe. Suppose the p-value for the hypothesis test in part (c) based on the Z stat yielded a p-value = 6.93%, then the X2 stat would yield a p-valuev 6.93%.提交答案| Tries 0/99And the t-test would yield a p-valuev 6.93%.坦本签安Tries 0/00

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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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I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here before
The mall has a set of data with employee age (X) and the corresponding number of annual on-the-job-accidents(Y). Analysis on the set finds that the regression equation is Y=100-3X. What is the likely number of accidents for someone aged 30? 97 100 10 none of the above
r = -0.579 Calculate the coefficient of determination. (Round to three decimal places as needed.) What does this tell you about the explained variation of the data about the regression line? % of the variation can be explained by the regression line. (Round to one decimal place as needed.) About the unexplained variation? % of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)
Listed below are systolic blood pressure measurements (in mm Hg) obtained from the same woman. Find the regression equation, letting the right 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 85 mm Hg. Use a significance level of 0.05 Right Arm 101 100 94 75 Left Arm 174 167 146 144 Click the icon to view the critical values of the Pearson correlation coefficient r The regression equation is (Round to one decimal place as needed.) 76 144 CRITS
Different patients are randomly selected and measured for pulse rate and body temperature. Using technology with x representing the pulse rates and y representing temperatures, we find that the regression equation has a slope of -0.007 and a y-intercept of 95.6. a. What is the equation of the regression line? b. What does the symbol y represent? a. Choose the correct answer below and fill in the answer boxes to complete your choice. (Type integers or decimals. Do not round.) OA. The equation is Inx=+(y. OB. The equation is Iny=( OC. The equation is y=+x OD. The equation is x=+y b. The symbol y represents value of the actual the mean a predicted ▼ C
How does R treat those observations with missing values? In other words, what role do the observations with missing values play in the regression? No command is needed for this question. You must need to provide an answer or take a guess.
Listed below are foot lengths (mm) and heights (mm) of males. Find the regression equation, letting foot length be the predictor (x) variable. Find the best predicted height of a male with a foot length of 272.7 mm. How does the result compare to the actual height of 1776 mm? Foot Length 282.3 277.8 252.8 258.7 279.0 258.4 274.1 261.7 Height 1785.0 1771.0 1675.7 1645.7 1859.3 1710.2 1789.2 1737.0 The regression equation is ŷ = + (x. (Round the y-intercept to the nearest integer as needed. Round the slope to two decimal places as needed.) The best predicted height of a male with a foot length of 272.7 mm is (Round to the nearest integer as needed.) How does the result compare to the actual height of 1776 mm? O A. The result is close to the actual height of 1776 mm. O B. The result is exactly the same as the actual height of 1776 mm. O C. The result is very different from the actual height of 1776 mm. O D. The result does not make sense given the context of the data. C mm.
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported asy=98.15x+95.04y=98.15x+95.04and the r=0.73r=0.73.What proportion of the variation in y can be explained by the variation in the values of x?r² = %Report answer as a percentage accurate to one decimal place.
Please answer letters b-e.
Make a sample data about the factors that could be rated to the number of text/chat messages a senior high student/s sends in a day. Make a scenario out of it using the data needed in correlation and regression analysis. Use samples that are less than 30. Lastly, test your claim if it is true and form the regression equation for this scenario.
Is it reasonable to interpret the y-intercept of the regression line? Why or why not? One home that is 1,750 square feet is listed at $590,000. Is this home's price above or below average for a home of this size? Use residuals and show your work to answer. footaget) ## (Intercept) 21.14411 136.31449 0.155 0.87954 ## footage 0.28388 0.08516 3.333 0.00667 ** ## Residual standard error: 71.64 on 11 degrees of freedom ## Multiple R-squared: 0.5025, Adjusted R-squared: 0.4573 ## F-statistic: 11.11 on 1 and 11 DF, p-value: 0.006671
The accompanying table shows results from regressions performed on data from a random sample of 21 cars. The response (y) variable is CITY (fuel consumption in mi/gal). The predictor (x) variables are WT (weight in pounds), DISP (engine displacement in liters), and HWY (highway fuel consumption in mi/gal). Which regression equation is best for predicting city fuel consumption? Why? E Click the icon to view the table of regression equations. Choose the correct answer below. O A. The equation CITY = 6.65 - 0.00161WT + 0.675HWY is best because it has a low P-value and the highest adjusted value of R2. O B. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it has a low P-value and the highest value of R?. OC. The equation CITY = 6.83 - 0.00132WT - 0.253DISP + 0.654HWY is best because it uses all of the available predictor variables. O D. The equation CITY = - 3.14 + 0.823HWY is best because it has a low P-value and its R2 and adjusted R? values are comparable to…

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