### Regression Analysis OutputIn this section, we will analyze the outputs of a regression analysis to better understand the relationship between variables. The equation for the regression is:\[ Y = 797 - 4.73X \]#### Regression CoefficientsThe table below presents key statistics for the regression coefficients:| Predictor | Coef | SE Coef | T | P ||-----------|----------|---------|------|------|| Constant | 797.101 | 86.64 | 9.20 | 0.000|| X | -4.731 | 1.551 | -3.05 | 0.006|**Key Terms:**- **Intercept (β₀):** The constant coefficient, representing the expected mean of the dependent variable when all predictors are zero.- **Slope (β₁):** Indicates the change in the dependent variable for a one-unit change in the predictor variable X.- **SE Coef:** Standard Error of the coefficient, measuring the accuracy of the coefficient's estimation.- **T:** T-statistic used to determine the significance of the predictor.- **P:** P-value indicating the probability that the coefficient is different from zero purely by chance. Values below 0.05 typically suggest statistical significance.### Interactive Section**Inputs:**- **Intercept (β₀):** [Enter value]- **Slope (β₁):** [Enter value]This section allows users to input and explore their own calculations for regression analysis. Additional resources and references are accessible through the eTextbook and Media link. Users can save their progress for future access.

The population regression equation can be written as Y = β0 + β1X, where X is the independent…

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MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

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The data provided give the number of standby hours based on total staff present, X₁, and remote hours, X₂. Perform a multiple regression analysis using the data provided and determine the VIF for each independent variable in the model. Is there reason to suspect the existence of collinearity? Click the icon to view the data. Determine the VIF for each independent variable in the model. VIF 1 VIF₂ = (Round to three decimal places as needed.) Is there reason to suspect the existence of collinearity? = A. No. The VIF for each independent variable is greater than 5. B. Yes. The VIF for each independent variable is less than 5. C. No. The VIF for each independent variable is less than 5. D. Yes. The VIF for each independent variable is greater than 5. Table of Data Standby Total Staff Remote Hours Present Hours 244 338 413 272 357 658 198 339 523 ITI 197 333 382 114 326 347 156 303 351 115 282 381 276 320 159 203 353 276 238 327 473 X
t Predictions: On A startup team is interested in creating a productivity app. In estimating the cost of creating the app, they collected sample data and developed the following regression equation: where number of developers b 2number of hours spent creating the app Is cost of software and testing ($'000) y-total cost of creating app ($'000) a. What is the value of b2 in the equation? y = -8.006-19.271x1 +0.8462 +1.786x3 b. Does it make sense to interpret bo in this equation? Yes No Accessibility: Investigate Search O W 5 I
Use the computer output to estimate the intercept B, and the slope , The regression equation is Y = 25.9 + 4.39X. Predictor Coef SE Coef T Constant 25.871 5.588 4.63 0.000 4.388 0.6608 6.64 0.000 Intercept Po: Slope ß, :i eTextbook and Media Hint Attempts: Save for Later
10:56 AA www-awn.aleks.com O REGRESSION AND CORRELATION Predictions from the least-squares regressio. OC OO D The managers of an electric utility wish to examine the relationship between temperature and electricity use in the utility's service region during the summer months. In particular, the managers wish to be able to predict total electricity use for a day from the maximum temperature that day. The bivariate data below give the maximum temperature (in degrees Fahrenheit) and the electricity use (in thousands of kilowatt hours) of electricity generated and sold for a random sample of sixteen summer days. A best-fitting line for the data, obtained from least- squares regression, is given by =53.28 + 3.07.x, in which x denotes the maximum temperature and y denotes the electricity use. This line is shown in the Figure 1 scatter plot. Temperature, x Electricity use, y (in degrees Fahrenheit) (in thousands of kilowatt hours) 78.4 295.4 72.5 232.1 00- 72.7 276.8 94.5 363.4 375 94.8 335.6…
= In a statistics course, a linear regression equation was computed to predict the final-exam score from the score on the first test. The equation as y 10+ 0.75x where y is the final-exam score and x is the score on the first test. Parvez scored 80 on the first test. What is the predicted value of the Parvez's score on the final exam? Parvez scored 60 on the final exam. What is the value of the residual?
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day (xx) and number of sit ups a person can do (yy). The results of the regression were: y=ax+b a=-1.152 b=30.418 r2=0.703921 r=-0.839 1. Use this to predict the number of sit ups a person who watches 2.5 hours of TV can do, and please round your answer to a whole number
What is the slope and intercept for the regression equation given this data?X = -5, 34, 56, 0, 61Y = 13, 63, 46, -23, 50
Input 'X' Family Median Percentiles OutPut 'Y' Percent Of Children Attending College 100 94 1 25 Find The Linear equation using the above data.
Save The data show the bug chirps per minute at different temperatures. Find the regression equation, letting the first variable be the independent (x) variable. Find the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute. Use a significance level of 0.05. What is wrong with this predicted value? Chirps in 1 min 776 1173 1034 951 975 1214 D Temperature (°F) 70.1 89 77.4 76.2 77 92.6 What is the regression equation? (Round the x-coefficient to four decimal places as needed. Round the constant to two decimal places as needed.) Incorrec ary ns
The correlation for the data is r=1 and a=0.05 should regression analysis be done Find the equation of the regression line y = a+bx A= B= Find y when x=40
A regression analysis was performed to determine if there is a relationship between hours of TV watched per day () and number of sit ups a person can do (y). The results of the regression were: y=ax+b a3D-0.838 b=28.783 es 72-0.784996 r=-0.886 rences borations Use this to predict the number of sit ups a person who watches 1 hours of TV can do, and please round your answer to a whole number. gle Drive ce 365 Submit Question adent Course aluations ere to search Chp ins prt sc delete f12 f9 f10 fa f5 f6 +1 f4 backspa & $4 8. 6 3 O E R T II 5
Use the computer output to estimate the intercept β0 and the slope β1.The regression equation is Y⁢=824-3.11X. Predictor Coef SE Coef T P Constant 824.309 89.60 9.20 0.000 X -3.108 1.019 -3.05 0.006 Intercept β0: Enter your answer; InterceptSlope β1: Enter your answer; Slope

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Use the computer output to estimate the intercept Bo and the slope ß- The regression equation is Y = 797 - 4.73X. %3D Predictor Coef SE Coef T P Constant 797.101 86.64 9.20 0.000 -4.731 1.551 -3.05 0.006 Intercept Bo: i Slope B: i