Exercise 15.40 Algo (Residual Analysis)Data for two variables, and y, follow.NoX2224-225a. Develop the estimated regression equation for these data (to 2 decimals). Enter negative value as negative number.y = -42.33b. Compute the standardized residuals for these data (to 2 decimals). Do not round your intermediate calculations. Enter negative value as negative number.2841+2.73Yi1223313668xStandardized ResidualStandardizedResidual-1.42-0.041.26 X0.47Can any of these observations be classified as an outlier? Explain.-0.39XBecause nonec. Develop a standardized residual plot for ŷ.AXiYiCittad Val2212of the standardized residuals are less than -2 or greater than 2, none24 25 28 412331 36 68of the observations can be classified as an outlier.Question 1 of 1Hint(s) Check My Work

The question related to regression analysis..The data on x and y is given…

Answered: Exercise 15.40 Algo (Residual Analysis)…

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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a snow plower recorded inches of snow fall and the yield by measure of square acreage over the previous 5 seasons. The regression equation is determined to be y=38.4+6.3x where x represents the inches of snowfall and y the measure of yield. 1. use the best fitting line to predict yield for 6 inches of snow 2. one of the records is (6,75) what is teh residual if x is 6
13/
6, 5. Kathy painted a picture and posted it on a social media site. The table shows the number of people who responded that they liked the picture since Kathy posted it. Time Since Picture Was Posted 5. 7. (days) Number of Likes 11 21 38 Write the regression equation that best models the data. a. y = 0.96(1. 55)* b. y = 2.07(1. 38)* c. y = 0.536x – 0.857x + 1.81 d. y = 1.38x´ + 0.30x + 0. 43
Develop a scatterplot and explore the correlation between customer age and net sales by each type of customer (regular/promotion). Use the horizontal axis for the customer age to graph. Find the linear regression line that models the data by each type of customer. Round the rate of changes (slopes) to two decimal places and interpret them in terms of the relation between the change in age and the change in net sales. What can you conclude? Hint: Rate of Change = Vertical Change / Horizontal Change = Change in y / Change in x
The money raised and spent (both in millions of dollars) by all congressional campaigns for 8 recent 2-year periods are shown in the table. The equation of the regression line is y = 0.942x +27.609. Find the standard error of estimate s, and interpret the result. 793.9 1042.3 957.7 1203.3 450.7 673.7 745.1 778.6 Money raised, x Money spent, y 734.8 1024.2 929.1 1160.6 448.6 697.9 735.7 751.2 Find the standard error of estimate s, and interpret the result. (Round to three decimal places as needed.) How can the standard error of estimate be interpreted? O A. The standard error of estimate of the money raised for a specific amount of money spent is about s, million dollars. O B. The standard error of estimate of the money spent for a specific amount of money raised is about s, million dollars.
please assist with this non graded problem.
H. Find the slope and y-intercept of each regression line. Then, determine if the correlation between the variables is positive or negative. (12 points) Regression Line Equation 1. Y = 1.751X – 21.722 Slope y-intercept Kind of Correlation 2. Y = -221.943X+65.434 3. Y = -82.455X –314.986 4. Y = 0.997X + 0.168
In a fisheries researchers experiment the correlation between the number of eggs in tge nest and the number of viable (surviving ) eggs for a sample of nests is r=0.67 the equation of the regression line for number of viable eggs y versus number of eggs in the nest x is y =0.72x + 17.07 for a nest with 140 eggs what is the predicted number of viable eggs ?
Write the linear model to test the hypothesis that there is no treatment effect. Clearly describe each term in the model, and the range of the subscripts. Write the null hypothesis that you are testing. Call: lm(formula = score ~ list, data = hearing) Residuals: Min 1Q Median 3Q Max -14.7500 -5.5833 -0.2083 6.3333 16.4167 Coefficients: Estimate Std. Error t value Pr(>|t|) (Intercept) 32.750 1.612 20.315 < 2e-16 *** listList2 -3.083 2.280 -1.352 0.17955 listList3 -7.500 2.280 -3.290 0.00142 ** listList4 -7.167 2.280 -3.144 0.00225 ** --- Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1 Residual standard error: 7.898 on 92 degrees of freedom Multiple R-squared: 0.1382, Adjusted R-squared: 0.1101 F-statistic: 4.919 on 3 and 92 DF, p-value: 0.00325
Compute the least-squares regression equation for the given data set. Round the slope and yFintercept to at least four decimal places. 4 y 7. 3. Send data to Excel Regression line equation: y =|

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