The local utility company surveys 12 randomly selected customers. For each survey participant, the companycollects the following: annual electric bill (in dollars) and home size (in square feet). Output from a regressionanalysis appears below:Bill = 15.9 + 4.45*SizeCoefficients Estimate Std. Error(Intercept)15.90.3Size4.450.57We are 98% confident that the mean annual electric bill increases by betweendollars anddollars for every additional square foot in home size.

Given Bill = 15.9 + 4.45 * size

Answered: ollects the following: annual electric…

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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6.6 You estimated a regression with the following output. Source | SS df MS Number of obs = 209 -------------+---------------------------------- F(1, 207) = 92470.91 Model | 622579639 1 622579639 Prob > F = 0.0000 Residual | 1393670.56 207 6732.70804 R-squared = 0.9978 -------------+---------------------------------- Adj R-squared = 0.9978 Total | 623973310 208 2999871.68 Root MSE = 82.053 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 34.2537 .1126432 304.09 0.000 34.03163 34.47578 _cons | 8.806569 10.31802 0.85 0.394 -11.5353 29.14844…
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3. You estimated a regression with the following output. Source | SS df MS Number of obs = 400 -------------+---------------------------------- F(1, 398) = 716.92 Model | 22118655.1 1 22118655.1 Prob > F = 0.0000 Residual | 12279166.2 398 30852.1764 R-squared = 0.6430 -------------+---------------------------------- Adj R-squared = 0.6421 Total | 34397821.3 399 86210.0784 Root MSE = 175.65 ------------------------------------------------------------------------------ Y | Coef. Std. Err. t P>|t| [95% Conf. Interval] -------------+---------------------------------------------------------------- X | 5.299576 .1979268 26.78 0.000 4.910464 5.688689 _cons | 67.86719 18.3827 3.69 0.000 31.72786 104.0065…
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Hi Just question 10 and 11 Thank you.
Calculate the estimated R2 and state why we should consider using adjusted R2 instead of R2 in the setting of multiple linear regression?
A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls is
Can you please help with 4.30 sulfur, the ocean and the sun? Only part A which is make a scatter plot that shows how DMS responds to SRD.
You may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per month
The attached images show linear regression analysis to evaluate the ability of independent variables full and part-time FTEs, number of Medicare certified beds and urban vs. rural setting to predict dependent variable, occupancy rate. How do you interpret these results, what are the basic assumptions for regression analysis?
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

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