5:41 PM | 21.0KB/s O ODocScanner 17-Feb...For certain data the following regression equations were obtained:4x – 5y + 33 = 020x + 9y - 107 = 0Estimate y when x = 20 and x when y = 20

Given: Regression equations: 4x-5y+33=0 ..........(1)20x+9y-107=0…

Answered: For certain data the following…

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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Use the following linear regression equation to answer the questions. X3=-17.3+3.7x1+9.6x4-2.0x7 a) which number is the constant term? List the coefficient explanatory variables. constant= x1 coefficient = x4 coefficient =x7 coefficient =b) if x1=1, x4=-3, x7=5, what is the predicted value for x3?(round you answer to one decimal place.) c) suppose x1 and x7 were held at fixed but arbitrary values. If x4 increased by 1 unit what would we expect the corresponding change in x3 to be? if x4 increased by 3 units what would be the corresponding expected change in x3?if x4 decreased by 2 units what would we expect for the corresponding change in x3?
Data from 147 colleges from 1995 to 2005 (Lee,2008) were tested to predict the endowments (in billions) to a college from the average SAT score of students attending the college. The resulting regression equation was Y = -20.46 + 4.06 (X). This regression indicates that: a. for every one-point increase in SAT scores, a college can expect 4.06 billion more in endowments. b. most colleges have very high endowments. c. for every one-point increase in SAT scores, a college can expect 20.46 billion fewer in endowments. d. for every one-dollar increase in endowments, the college can expect a half-point increase in SAT scores.
A researcher is interested in finding out the factors which determined the yearly spending on family outings last year (Y, measured in dollars). She compiles data on the number of members in a family (X1), the annual income of the family (X2), and the number of times the family went out on an outing in the last year (X3). She collects data from 196 families and estimates the following regression: Y=120.45+1.54X1+2.12X2+2.12X3. Suppose β1, β2, β3, denote the population slope coefficients of X1, X2, and X3, respectively. The researcher wants to check if neither X1 nor X2 have a significant effect on Y or at least one of them has a significant effect, keeping X3 constant. She calculates the value of the F-statistic for the test with the two restrictions (H0: β1=0, β2=0 vs. H1: β1≠0 and/or β2≠0) to be 3.00. The p-value for the test will be enter your response here?
The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x,) and newspaper advertising (x,). The estimated regression equation was ý = 82.3 + 2.29x, + 1.90x2. The computer solution, based on a sample of eight weeks, provided SST = 25.1 and SSR = 23.415. (a) Compute and interpret R? and R 2. (Round your answers to three decimal places.) The proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is 653 x . Adjusting for the number of independent variables in the model, the proportion of the variability in the dependent variable that can be explained by the estimated multiple regression equation is (b) When television advertising was the only independent variable, R2 = 0.653 and R,2 = 0.595. Do you prefer the multiple regression results? Explain. Multiple regression analysis (is preferred since both R2 and R.2 show an increased v v…
The following estimated regression equation has been proposed to predict daily sales at a furniture store. ŷ = 12 − 5x1 + 8x2 + 17x3 where ŷ = estimated sales (in $1,000s) x1 = competitor's previous day's sales (in $1,000s) x2 = population within 1 mile (in 1,000s) x3 = 1 if any form of advertising was used; 0 otherwise (a) Fully interpret the meaning of the b3 coefficient (Give the answer in dollars.) Predict sales (in dollars) for the store with competitor's previous day's sale of $4,000, a population of 11,000 within 1 mile, and ... (b) no radio advertisements. $ (c) one radio advertisement. $ (d) eight radio advertisements. $
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). The equation CITY - 3.17 +0.823HWY was previously determined to be the best for predicting city fuel consumption. A car weighs 2700 lb, it has an engine displacement of 1.6 L, and its highway fuel consumption is 35 mi/gal. What is the best predicted value of the city fuel consumption? Is that predicted value likely to be a good estimate? Is that predicted value likely to be very accurate? Click the icon to view the table of regression equations. The best predicted value of the city fuel consumption is (Type an integer or a decimal. Do not round.). Regression Table I R² Adjusted R2 WT/DISP WT/HWY Predictor (x) Variables P-Value WT/DISP/HWY 0.000 0.942 0.000 0.748 0.000 0.942 0.000…
You are the Filipino analyst hired by the multinational company to study the sales data of its more than 75 stores worldwide. You used regression analysis to predict $ sales (y) by using $ advertising (x1) and $ salary of sales representatives (x2) across all the branches. You obtained the following regression function: y = 7800 + 8.5x1-1.6x2 If the advertising budgets of one of the branches of the corporation is the same as before and the salary of sales representatives is now 20% less than before, then the predicted sales in that branch will A. increase B. no sufficient information to determine C. decrease D. remain the same
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If you know that the equation of the simple linear regression between the final exam result and the mid-year examination result for students in engineering statistics is as follows: Final exam = 50 + 0.5 x midterm according to the above equation, then the regression coefficient is:
Define both x and y in all problems. x is the cause, and y is the effect. This is the most important step when doing linear regression, otherwise, all the remanding parts will be wrong.

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For certain data the following regression equations were obtained: 4x – 5y + 33 = 0 20x + 9y - 107 = 0 Estimate y when x = 20 and xr when y = 20