Problem 1Consider a regression (a really bad one, since the number of observations is very low!) with n=2, K = 2,2(13)1. Write the model in matrix notation and denoting each element of each matrix, e.g.3/19211Unwhere X =2. Find an expression for our OLS estimator, B.V

Given a regression with n=2, K=2, where X=1213

Answered: Consider a regression (a really bad…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

See similar textbooks

Similar questions

19.
estion 7 of 15 Suppose the manager of a gas station monitors how many bags of ice he sells daily along with recording the highest temperature each day during the summer. The data are plotted with temperature, in degrees Fahrenheit (°F), as the explanatory variable and the number of ice bags sold on as the response variable. The least squares regression (LSR) line for the data is y = -114.05 +2.17x. On one of the observed days, the temperature was 82 °F and 66 bags of ice were sold. Determine the number of bags of ice predicted to be sold by the LSR line, ŷ, when the temperature is 82 °F. Enter your answer as a whole number, rounding if necessary. ice bags Using the predicted value you just found, compute the residual at this temperature. residual = ice bags DOLL
Might we be able to predict life expectancies from birthrates? Below are bivariate data giving birthrate and life expectancy information for each of twelve countries. For each of the countries, both x, the number of births per one thousand people in the population, and y, the female life expectancy (in years), are given. Also shown are the scatter plot for the data and the least-squares regression line. The equation for this line is y = 82.17 -0.47x. Birthrate, x (number of births per 1000 people) 14.3 27.4 51.1 46.8 24.9 29.9 18.2 41.6 49.4 14.1 33.9 49.3 Send data to calculator Female life expectancy, y (in years) 75.6 70.5 58.2 59.0 73.3 62.7 73.6 65.2 62.4 74.3 67.0 53.9 Send data to Excel Female life expectancy (In years) Based on the sample data and the regression line, answer the following. 85+ 80+ 75+ 70+ 65 60 55+ 50 (a) From the regression equation, what is the predicted female life expectancy (in years) when the birthrate is 29.9 births per 1000 people? Round your answer to…
A catalog company builds a logistic regression (LR) model to predict the probability that a customer will buy from the catalog during a particular campaign (mailing). The LR model contains 2 independent variables: X1 spend per year in 1000's of dollars (so $2000 will be coded as X1 2) and X2= does customer possess a loyalty card (X2 1 means customer has loyalty card, X2 = 0 means customer does not have the card). Once the model is fitted, the LR coefficients are provided below: = Constant term (BETAO): - 3.5 (negative 3.5) X1 coeff. (BETA1): 0.6 X2 coeff.(BETA2): 1.5 What is the probability that a customer who spends $6000/year and who does NOT have a loyalty card will respond to the campaign? = O About 53% O About 15% About 33% O About 45%
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?
5
Which of the following best describes the least-squares line fit to the data shown in the plot? (i) bo = 0, bị =-1 (ii) bo = -3, b₁ = 1 (iii) bo-5, b₁ = 2 (iv) bo = −3, bị =-1 (v) bo = 0, b₁ = -3 2 X
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. $
For a linear regression problem, The actual observed y-value when x = 2 is y = 6. The least squares line is y = 2x+1. The predicted value when x=2 is y = 5. Determine the value of the residual (prediction error) oooo 1 6 5 Cannot be determined from the information given
Suppose we have a multiple regression model with 2 predictors and an intercept. (Without any interaction or higher order terms, we have only the 2 predictors in the model and the intercept.) We have only n= 6 observations (so it would be rather silly to fit this model to this data, but let's pretend it is reasonable). We find the values of the first 5 residuals are: 2.6, 2.3, 2.5, -1.5, -1.4 What is the value of MSRes for this multiple regression model?
Find the least-squares regression line ŷ bo+ b₁z through the points For what value of is y = 0? H= (-2,0), (1, 6), (4, 14), (8, 19), (10, 27).
2

SEE MORE QUESTIONS

Recommended textbooks for you

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

MATLAB: An Introduction with Applications

Statistics

ISBN:

9781119256830

Author:

Amos Gilat

Publisher:

John Wiley & Sons Inc

Probability and Statistics for Engineering and th…

Statistics

ISBN:

9781305251809

Author:

Jay L. Devore

Publisher:

Cengage Learning

Statistics for The Behavioral Sciences (MindTap C…

Statistics

ISBN:

9781305504912

Author:

Frederick J Gravetter, Larry B. Wallnau

Publisher:

Cengage Learning

Elementary Statistics: Picturing the World (7th E…

Statistics

ISBN:

9780134683416

Author:

Ron Larson, Betsy Farber

Publisher:

PEARSON

The Basic Practice of Statistics

Statistics

ISBN:

9781319042578

Author:

David S. Moore, William I. Notz, Michael A. Fligner

Publisher:

W. H. Freeman

Introduction to the Practice of Statistics

Statistics

ISBN:

9781319013387

Author:

David S. Moore, George P. McCabe, Bruce A. Craig

Publisher:

W. H. Freeman

SEE MORE TEXTBOOKS

Consider a regression (a really bad one, since the number of observations is very low!) with n=2, K = 2, - G where X 1. Write the model in matrix notation and denoting each element of each matrix, e.g. 4 2. Find an expression for our OLS estimator, B. 7/1 9/2 1/1 -8 Un