After studying the milk yields of a randomly selected sample of 900 cows, researchers felt that the presence of whitespots (WS;) on a cow's body led to a different level of milk production (MP;). The researchers estimate theOLS regression:where WS; is a binary variable of the form:WS;=MP;=5.84 +0.12×WS₁,(2.23) (0.06)1, if the th cow has white spotsO, if the th cow doesn't have white spotsThe 95% confidence interval for the coefficient B₁ is (,).(Round your answers to four decimal places.)

The provided information is as follows:The researchers estimated this regression using a randomly…

Answered: After studying the milk yields of a…

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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A researcher conducted a number of descriptive statistics for two variables X and Y. They were as follows: SP = 15; SSx = 3; My = 7; Mx = 3 What is b equal to (Please include the sign: e.g., -20)? What is a equal to (please include the sign: e.g., +4.0)? Using b and a construct a regression equation, and then using the regression equation, calculate the value of predicted Y when X = 2?
When testing for bl in a regression model, the null hypothesis (2-tailed) is b1=0. Why? O Then the alternative hypothesis can be b0=1 This indicates that bl=b2 This indicates that bl=b0 A change in x does not result in a change in y
Consider the following correlations -0.9 , -0.5 , -0.2 , 0 , 0.2 , 0.5 and 0.9. For each give the fraction of the variation in y that is explained by the least-squares regression of y on x.
Accountants at the GIll Co and Charted Brothers Accounting believed that several traveling executives were submitting unusually high travel vouchers upon returning from business trips. First, they took a sample of 200 vouchers submitted from the past year. Then they developed the following multiple-regression equation relating expected travel cost (Y) to a number of days on the road (X1) and distance traveled (X2) in miles: Y = 90.00 + 48.50X1 + 0.40X2 Here is additional information concerning the regression model: Sb1 = 0.038 Sb2 =0.019 R2 = 0.68 Se = 1.63 F-Statistic = 32.123 Durbin-Watson (d) statistic = 0.5436 a. Which of the independent variables appear to be statistically significant (at the 0.05 significant level) in explaining the expected travel cost for accountants
q12
The following is the result of the multiple linear regression analysis in STATISTICA, where the response Y = lung capacity of a person, xage = age of the person in years, xheight = height of the person in inches, = a categorical variable with 2 levels (0 = non- X smoke smoker, 1 = smoker), and xCaesarean = a categorical variable with 2 levels (0 = normal delivery, 1 = %3D %3D Caesarean-section delivery). b* Std.Err. Std.Err. t(720) p-value N=725 Intercept Age Height Smoke Caesarean of b 0.467772 0.017626 of b* -11.8001 0.1372 0.2790 -0.6407 -25.2263 7.7846 28.6552 -5.0142 0.000000 0.000000 0.000000 0.206427 0.026517 0.026340 0.754765 -0.074205 -0.033054 0.009735 0. 127774 0.092146 0.000001 0.022851 0.014799 0.014492 -0.2102 -2.2808 What is the predicted lung capacity of an 14-year old non-smoker whose height is 71 inches born by normal delivery? (final answer to 4 decimal places)
The amount of gas used by a typical household in a week (Q, in litres) is found to have a strongcorrelation with the price of gas (P, in $). For 6 observations, the following data was collected:Q P11.4 4.0014 3.5016 3.0018 2.5020 2.00(a) Create a regression relationship for this data(b) Perform a hypothesis test to determine whether the slope coefficient obtained in part (a) above is statistically significant(c) Use the regression equation in part (a) above to predict the quantity of gas used by a household when the price is $3.75 per litre(d) Calculate the price elasticity of demand for gas at a price of $3.75 (e) Using a further calculation, discuss how well the regression equation in part (a) above fits the data
A student used multiple regression analysis to study how family spending (y) is influenced by income (x1), family size (x2), and additions to savings(x3). The variables y, x1, and x3 are measured in thousands of dollars. The following results were obtained. ANOVA df SS Regression 3 45.9634 Residual 11 2.6218 Total Coefficients Standard Error Intercept 0.0136 x1 0.7992 0.074 x2 0.2280 0.190 x3 -0.5796 0.920 Write out the estimated regression equation for the relationship between the variables. Compute coefficient of determination. What can you say about the strength of this relationship? Carry out a test to determine whether y is…
The following table gives the sales of CD_Rom drives and compact disk players for selected years and can be found in Glassman. Sales are in millions. Year 1988 1990 1992 1993 Sales 0.075 0.24 1.5 4.8 A. On the basis of this data, find the best-fitting exponential regression. Let x = 0 correspond to 1988. State the square of the correlation coefficient. (Use 4 decimal places in you answers.)S(x) = r2 = B. Use this model the estimate the sales in 1997. (Round to the nearest player.)In 1997, there were about million CD_ROM and compact disk players sold.
The following results were obtained from a simple linear regression analysis. Total sum of square = 5.7640. Unexplained sum of square = 0,2225. The coefficient of determination is: OA 0.0386 O B, 0.0402 OC0.9614 O D.0.9805
Ten observations were provided for a dependent variable y and two independent variables x1 and x2; for these data, SST = 15,177.6 and SSR = 14,059.3. (a) Compute R2. (Round your answer to three decimal places.) R2 = (b) Compute Ra2. (Round your answer to three decimal places.) Ra2 = (c) Does the estimated regression equation explain a large amount of the variability in the data? Explain. (For purposes of this exercise, consider an amount large if it is at least 55%. Round your answer to one decimal place.) , after adjusting for the number of independent variables in the model, we see that % of the variability in y has been accounted for.

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