9. Regression analysis was applied between sales data (y in$1000s) and advertising data (x in $100s) and the followinginformation was obtained.y hat (Estimated y)= 12 + 1.8xn == 20SSR = 225%3DSSE = 75sb1 = .2683%DThe critical t value for testing the significance of the slope, ata 0.05 level of significance, is2.1311.7532.1011.746

Answered: Image /qna-images/answer/6ad7c2f4-c51f-4bf2-9fa9-3874a0480237.jpg

Answered: 9. Regression analysis was applied…

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 researcher records age in years (x) and systolic blood pressure (y) for volunteers. They perform a regression analysis was performed, and a portion of the computer output is as follows: ŷ = 4.3 14.9x Coefficients (Intercept) X Estimate St 4.3 Ho: B₁ = 0 Ha: B₁ > 0 B1 O Ho: B₁ Ha: B₁ <0 = 0 14.9 B1 O Ho: B₁ = 0 0 Ha: B1 Std. Error Test statistic P-value 2.9 5.1 1.48 Specify the null and the alternative hypotheses that you would use in order to test whether a negative linear relationship exists between x and y. 2.92 0.08 0.01
A linear regression model is designed to predict service charges by a bank (in dollars per month) based on sales revenues of 26 companies who use the services (in millions of dollars). Partial output from Excel gives ŷ = -5428 + 32.756x,, with SSE = 117600, and the p-value = 0.04022 for the estimated slope. Interpret the standard error of the estimate. A B E Approximately 95% of the observed service charges fall within $117600 of the least squares line None of the suggested answers are correct For every $1 million increase in sales revenue, we expect a service charge to increase by $117600 Approximately 96% of the observed service charges fall within $140 of the least squares line. Approximately 95% of the observed service charges equal their corresponding predicted values
A regression was run to determine if there is a relationship between hours of TV watched per day (x) and number of situps a person can do (y). The results of the regression were: y-ax+b a=-1.108 b-34.092 r2-0.736164 r=-0.858 Assume the correlation is significant, and use this to predict the number of situps a person who watches 4 区 hours q TV can do (to one decimal place) 72222392 口 D
The Toyota Camry is one of the best-selling cars in North America. The cost of a previously owned Camry depends upon many factors, including the model year, mileage, and condition. To investigate the relationship between the car's mileage and the sales price for a 2007 model year Camry, the following data show the mileage and sale price for 19 sales (PriceHub website). Click on the datafile logo to reference the data. DATA file 1. 18 Price ($1000s) 16 14 12 10 18 6 If your answer is zero, enter "0". a. Select a scatter diagram with the car mileage on the horizontal axis and the price on the vertical axis. 20 40 6,0 8,0 100 Miles (1000s) 120 223 +♡ ♡ Miles(1000s) 29 36 47 63 77 73 87 92 101 110 28 59 68 68 91 42 65 110 Price ($1000s) 16.2 16.0 13.8 11.5 12.5 12.9 11.2 13.0 11.8 10.8 8.3 12.5 11.1 15.0 12.2 13.0 15.6 12.7 8.3
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The following data gives the number of employees at the bookstore and the number of minutes students wait in line to buy books at the beginning of the term. The independent variable is the number of employees and the dependent variable is the number of minutes. What is the y intercept? SSxx = 56.857; SS=2095.714; SSxy=-322.571 SUMMARY OUTPUT Regression Statistics Multiple R R Square Adjusted R Square XA356899 0.96 0.93 0.91 y 67 54 47 33 31 25 12
The price X (dollars per pound) and consumption y (in pounds per capita) of beef were samples for 10 randomly selected years. The following data should be used to answer the question that follows. n = 10 Ex = 36.19 Ex2 = 134.17 2.9 < x s 6.2 Ey = 774.7 Iy² = 60739.23 Exy = 2832.21 Before the slope of the line of best fit can be estimated, it is necessary to calculate SS Enter the value for SSx accurate to the nearest tenth.
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? Click the icon to view the table of regression equations. Choose the correct answer below. A. The equation CITY=6.86 -0.00131WT -0.258DISP+0.659HWY is best because it has a low P-value and the highest value of R². B. The equation CITY=6.73 -0.00157WT +0.668HWY is best because it has a low P-value and the highest adjusted value of R². C. The equation CITY= -3.15+0.823HWY is best because it has a low P-value and its R² and adjusted R² values are comparable to the R² and adjusted R² values of equations with more predictor variables. O D. The equation CITY=6.86 -0.00131WT-0.258DISP + 0.659HWY is best because it…
Interpret the intercept and the coefficients of D1 and D2 in the regression above.
I need help with the test statistic only. Thanks.
A set of n=15 pairs of x and y scores has sx=10, ssy=40, and sp=30. What is the slope for the regression for predicting y from x?
The arm span and foot length were both measured (in centimeters) for each of 20 students in a biology class. The computer output displays the regression analysis. Predictor Coef SE Coef t-ratio Constant Arm span -7.611 2.567 2.965 0.046 0.186 0.035 5.377 0.000 S = 1.61 R-Sq = 63.0% R-Sq (Adj) = 64.9% Which of the following is the best interpretation of the standard deviation of the residuals? The typical arm span is 161 centimeters. The typical foot length is 16.1 centimeters. The typical distance between the observed and predicted arm spans is 1.61 centimeters. The typical distance between the observed and predicted foot lengths is 1.61 centimeters. Op

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