Refer to the regression output in the previous question.What is the regression model being estimated here?Oy=Po + B1x + €Oŷbo+b₁x + €Oy= Bo + B₁xO â=bo+bí A company organizes its sales into territories, each assigned to one sales representative. An analysttheorizes that the on-the-job experience level of the sales rep may be related to the amount of salesin each territory. The analyst decided to use the time the sales reps had been employed at thecompany to predict sales.After taking a random sample of sales territories, the analyst measured the last quarter's Sales indollars and the Experience the sales rep had at the company in months.Here are the three tables from the regression results:Regression StatisticsMultiple RR SquareAdjusted R SquareStandard ErrorObservationsANOVARegressionResidualTotalInterceptExperience(months)0.59660.35590.339497.905840df13839Coefficients6.6108SS5205388.04 5205388.049420585.51StandardError4250.9742 180.233214625973.551.4427How many territories are in the sample?247910.15t StatMS23.5864.5822P-value2.71E-244.85E-05F20.9971Lower 95% Upper 95%3886.1113Significance F4.85E-053.69024615.83729.5313

This question is from Regression Analysis. From the given output it can be seen that it is Simple…

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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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Consider the following data for two variables, x and y. x 22 24 26 30 35 40 y 11 21 34 36 39 36 (a) Develop an estimated regression equation for the data of the form ŷ = b0 + b1x. (Round b0 to one decimal place and b1 to three decimal places.) ŷ = (b) Use the results from part (a) to test for a significant relationship between x and y. Use ? = 0.05. Find the value of the test statistic. (Round your answer to two decimal places.) F = Find the p-value. (Round your answer to three decimal places.) p-value = Is the relationship between x and y significant? Yes, the relationship is significant.No, the relationship is not significant. (c) Develop a scatter diagram for the data. A scatter diagram has 6 points. The horizontal axis ranges from 20 to 45 and is labeled: x. The vertical axis ranges from 0 to 45 and is labeled: y. Moving from left to right, the leftmost point is at (22, 7), with the next 4 points extending upward, the first two rising more…
The following table lists the birth weights (in pounds), x, and the lengths (in inches), y, for a set of newborn babies at a local hospital. Birth Weight (in Pounds), x Answer Length (in Inches), y Birth Weights and Lengths 10 7 21 18 7 4 12 7 10 3 1 17 12 12 16 21 19 22 15 21 20 Copy Data Step 2 of 2: Predict the length of an 8-pound baby. Assume the regression equation is appropriate for prediction. Round your answer to two decimal places necessary. Tables Keyboard S
A group of scientists and engineers aim to create fuel-efficient and fuel-efficient cars. In order to study the problem, they randomly selected a sample of 20 cars and took information from X: weight (hundreds of pounds) and Y: vehicle performance (miles per gallon). Once the information was collected and analyzed, using a scatterplot, they determined that a linear model can fit the data. Using R the following information is obtained from the linear regression model. Y = 40.15−0.65X Which of the following statements is correct when interpreting the slope ?: Select one: a. Vehicle performance increases by 0.65 miles per gallon when car weight increases by 100 pounds. b. Vehicle performance is reduced by 0.65 miles per gallon when car weight increases by 1lb c. Vehicle performance is reduced by 0.65 miles per gallon when the car's weight increases by 100 pounds. d. Vehicle performance is reduced by 65 miles per gallon when the car's weight increases by 100 pounds.
The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 4 Suppose we have an SLR model, where the dependent variable (Y) represents ‘how satisfied someone is with his/her life, from 0 to 100’ (the higher the value, the higher the satisfaction with life), and the explanatory variable (X1) represents ‘personal annual income in £1,000’. The estimated OLS regression line is: Yhat = 33.2 + 0.74*X1. According to this model, what is the predicted life satisfaction, for someone with…
A statistical program is recommended. Consider the following data for two variables, x and y. X 9 32 18 15 26 Y 9 19 20 16 22 (a) Develop an estimated regression equation for the data of the form ŷ = b0 + b1x. (Round b0 to two decimal places and b1 to three decimal places.) (b) Develop an estimated regression equation for the data of the form ŷ = b0 + b1x + b2x2. (Round b0 to two decimal places and b1 to three decimal places and b2 to four decimal places.) (c) Use the model from part (b) to predict the value of y when x = 20. (Round your answer to two decimal places.)
The number of initial public offerings of stock issued in a 10-year period and the total proceeds of these offerings (in millions) are shown in the table. The equation of the regression line is y = 47.018x+ 18,440.06. Complete parts a and b. 150 183 56 69 397 478 500 686 Issues, x Proceeds, 19,915 29,368 43,272 30,252 65,200 66,431 20,344 10,189 32,069 473 415 27,552 (a) Find the coefficient of determination and interpret the result. (Round to three decimal places as needed.) How can the coefficient of determination be interpreted? The coefficient of determination is the fraction of the variation in proceeds that can be explained by the variation in issues. The remaining fraction of the variation is unexplained and is due to other factors or to sampling error. Lion O The coefficient of determination is the fraction of the variation in proceeds that is unexplained and is due to other factors or sampling error. The remaining fraction of the variation is explained by the variation in…
You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ŷ = 0.406 + 1.3385x₁ + 2x₂ The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ŷ = 1.9 - 3x₁ + 12x2 + 4x3 + 8x4 This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = P₂ = P3 =B4 = 0 B1 O Ho: One or more of the parameters is not equal to zero. H₂: B3 =B₁ = 0 O Ho: B3 = P4 = 0 H₂: None of the parameters are equal to zero. O Ho: B3 B4= = = H₂: One or more of the parameters is not equal to zero. O Ho: P₁ = P₂ = P3= P4= H: One or more of the parameters is not equal to zero. Find…
The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 28 Suppose your estimated MLR model is: Y_hat = -30 + 2*X1 + 10*X2 Suppose the standard error for the estimated coefficient associated with X2 is equal to 5. Now, suppose that for some reason we multiply X2 by 5 and we re-estimate the model using the rescaled explanatory variable. What will be the value of the estimated coefficient of X2 and its standard error? The estimated coefficient of X2 will be equal to 50 and its standard error will be…
gr and Lin Reg Inf x 9 Practice Exam 2 and ar x 6 Practice Exam 2 and ar x 4 Extra.pdf - Google Drive x G the test statist om/file/d/1r12gnvovqpwfgv8Fua7nZAGRFInXvJbL/view ance + Calendar In questions 6 & 7, use the following printout of the linear regression relating the moving times (in minutes) and weights (in pounds) of 20 randomly selected moving jobs performed by three-man crews. The regression equation is Moving Times = 21.84 + 0.036538(Weight) Predictor Coefficient SE(Coeff) t-ratio Constant 21.84 25.54 0.86 0.404 Weight 0.036538 0.002977 12.27 0.000 S= 30.32 R-Sq = 89.3% R-Sq (ad) = 88.7% 6) The value of S, for this regression is: A. D.002977 B. D.036538 C. 0,000 D. 25.54 E. 21.84 7) The test statistic for a test of significance for a non-zero slope is: A. D.002977 B. 25.54 C. 12.27 D. 0.86 E. None of these. (hp 50000000044171
The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 16 In a t-test, suppose a researcher sets the significance level at 0.5%. What does this mean? The probability that the null hypothesis is true is 0.5% The researcher would be rejecting the null hypothesis, only if the p-value is less than 0.5% The researcher would be rejecting the null hypothesis, if the t-statistic is higher than 0.5 It does not mean anything, because the significance level can only be set at 5% QUESTION 17 In an MLR…
A study examined the eating habits of 20 children at a nursery school. The variables measured for each child included: calories (the number of calories eaten at lunch), time (the time in minutes spent eating lunch), and sex (male=1, female=0). A multiple linear regression model using Y = calories, X1 = time, and X2 = sex led to the following model: y=547.65-2.85x1+10.67x2 For two children who spend the same amount of time eating, one male and one female, which child is predicted to consume more calories and by how much?
The Simple Linear Regression model is Y = b0 + b1*X1 + u and the Multiple Linear Regression model with k variables is: Y = b0 + b1*X1 + b2*X2 + ... + bk*Xk + u Y is the dependent variable, the X1, X2, ..., Xk are the explanatory variables, b0 is the intercept, b1, b2, ..., bk are the slope coefficients, and u is the error term, Yhat represents the OLS fitted values, uhat represent the OLS residuals, b0_hat represents the OLS estimated intercept, and b1_hat, b2_hat,..., bk_hat, represent the OLS estimated slope coefficients. QUESTION 7 In the MLR model, the assumption of ‘linearity in parameters’ is violated if: one of the slope coefficients appears as a power (e.g. Y = b0 + b1*(X1^b2) + b3*X2 + u) the model includes the reciprocal of a variable (e.g. 1/X1) the model includes a variable squared (e.g. X1^2) the model includes a variable in its logarithmic form (i.e. log(X1) ) QUESTION 8 In the MLR model, the assumption of 'no perfect collinearity'…

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Refer to the regression output in the previous question. What is the regression model being estimated here? Oy= Bo + B₁x + € O ý=bo+b2+c Oy= Bo + B₁x O ý=bo+bí