2.Marks in MATH001(X): 54.46.Marks in STAT101 (Y):34.4.(a)Estimate the simple linear regression model Y= Bo+B1 X+ €¡ usingmatrix method.(b)Test the null hypothesis Ho: Bi=0 against H : Bi# 0 for the data given below usingANOVA table.%3D

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Answered: 2. Marks in MATHO01(X): 4. 4. 6. Marks…

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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17) Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 41 members of the population, where for each member, the value of Y as well as the values of the three explanatory unknowns were observed. The data is entered into a computer using linear regression software and the output summary tells us that R-square is 0.9, the linear model coefficient of the first explanatory unknown is 7 with standard error estimate 2.5, the coefficient for the second explanatory unknown is 11 with standard error 2, and the coefficient for the third explanatory unknown is 15 with standard error 4. The regression intercept is reported as 28. The sum of squares in regression (SSR) is reported as 90000 and the sum of squared errors (SSE) is 10000. From this information, what is the number of degrees of freedom for the t-distribution used to compute critical values for hypothesis tests and confidence intervals for the individual…
A recent Gallup survey of a random sample of Americans (18 and older) found that the average number of alcoholic drinks consumed per week (drinks) by males was 4.2 and by females was 1.4.[¹] Suppose we use the underlying survey data to estimate a least-squares regression of the average number of drinks a person reports consuming per week (Drinks;) on a dummy variable equal to 1 if i is female and 0 otherwise (Female;). (Assume all respondents identify as either male or female.) The estimated regression line equation can be written as: Drinks = a +bFemale Alcohol Consumption by Gender What is the numerical value of b?
The null hypothesis being tested in the least-squares regression output for B is B1 = B1,0=1. True False
The y-interept bo of a least-squares regression line has a useful interpretation only if the x-values are either all positive or all negative. Determine if the statement is true or false. Why? If the statement is false, rewrite as a true statement.
find the least squares regression line equation from the given data.
Consider the following population model for household consumption: cons = a + b1 * inc+ b2 * educ+ b3 * hhsize + u where cons is consumption, inc is income, educ is the education level of household head, hhsize is the size of a household. Suppose a researcher estimates the model and gets the predicted value, cons_hat, and then runs a regression of cons_hat on educ, inc, and hhsize. Which of the following choice is correct and please explain why. A) be certain that R^2 = 1 B) be certain that R^2 = 0 C) be certain that R^2 is less than 1 but greater than 0. D) not be certain
Espan Interpreting technology: The following display from the TI-84 Plus calculator presents the least-squares regression line for predicting the price of a certain stock (y) from the prime interest rate in percent (x). LinReg y=a+bx a = 2.39562641 do b=0.37929688 2=0.4130321711 r=0.64267579 Part: 0 /3 Part 1 of 3 Write the equation of the least-squares regression line. Use the full accuracy shown in the calculator output (do not round your answers). Regression line equation: y = Part 2 of 3 What is the correlation between the interest rate and the yield of the stock? The correlation coefficient is Part: 2 /3 Part 3 of 3 Predict the price when the prime interest rate is 6%. Round the answer to at least four decimal places. When the prime interest rate is 6%, the price is predicted to be
5
)A county real estate appraiser wants to develop a statistical model to predict the appraised value of 3) houses in a section of the county called East Meadow. One of the many variables thought to be an important predictor of appraised value is the number of rooms in the house. Consequently, the appraiser decided to fit the simple linear regression model: E(u) = Bo + Bix, where y = appraised value of the house (in thousands of dollars) and x = number of rooms. Using data collected for a sample of n = 73 houses in Fast Meadow, the following results were obtained: y = 73.80 + 19.72x What are the properties of the least squares line, y = 73.80 + 19.72x? A) Average error of prediction is 0, and SSE is minimum. B) It will always be a statistically useful predictor of y. C) It is normal, mean 0, constant variance, and independent. D) All 73 of the sample y-values fall on the line.

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2. Marks in MATHO01(X): 4. 4. 6. Marks in STAT101 (Y): 3. 4. 4. (a)Estimate the simple linear regression model Y= Bo+Br X+ Ej using matrix method. (b)Test the null hypothesis Ho: Bi 0 against H : Bi 0 for the data given below using ANOVA table. %3D