Consider the following responses and the associated values of the explanatory vari- ables: Response YX₁ X₂ Xs 1 5 -1 0 1 -1 -3 1 0 -4 0 1 -3 2 0 -1 3 5 1 4 1 -3 0 (X'X)-¹ to do 0 1 2 3 3 -2 Based on the conventional notations for the multiple regression model we have X'Y = [10, 14, 10,-3]', and 000 0 0 0 0 0 0 0 00 (g) Find a 95% prediction interval for a single response Y when z₁ = 1, ₂ = −3, 73 = -1, that is, a[1, 1, -3, -1]'. (h) State the differences between a 95% confidence interval and a 95% prediction interval.
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- For 10 observations on price (p) and supply (S) the following data were obtained (in appropriate units): Σρ- 130, ES -220, Σp-2,288 Σ-5,506, ΣpS-3,467, Ν= 10 Obtain the line of regression of S on p and estimate the supply when the price is 16 units, and find out the standard error of the estimate.Let x be the size of a house (sq ft) and y be the amount of natural gas used (therms) during a specified period. Suppose that for a particular community, x and y are related according to the simple linear regression model with the following values. ? = slope of population regression line = 0.014? = y intercept of population regression line = -4 (a) What is the equation of the population regression line?y = (b) What is the mean value of gas usage for houses with 2100 sq ft of space?(c) What is the average change in usage associated with a 1 square foot increase in size?(d) What is the average change in usage associated with a 100 square feet increase in size?The owner of a movie theater company used multiple regression analysis to predict gross revenue (y) as a function of television advertising (x1) and newspaper advertising (x2). The estimated regression equation was ŷ = 82.4 + 2.23x1 + 1.40x2. The computer solution, based on a sample of eight weeks, provided SST = 25.5 and SSR = 23.495.(Note: SST = SS yy ) (a)Compute and interpret R2 and Ra2. (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 (??) . 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 Ra2 = 0.595. Is the multiple regression model more preferable over the simple regression model? Explain. Multiple…
- Consider the following responses and the associated values of the explanatory vari- ables: Response Y X₁ X₂ X3 1-3 5 -1 0 -2 0 1 0 -1 -3 1 1 0 -4 0 1 -1 3 2 -1 3 3 1 2 3 4 5 6 7 2 (X'X)-¹ t -3 05 Based on the conventional notations for the multiple regression model we have X'Y = [10, 14, 10, -3]', and 000 0 0 0 0 00 0 (d) Find an estimate of the error variance o² from the SSE. (Note SSE = Y'Y - B'X'Y with 7-4=3 degrees of freedom.) (e) Setting up appropriate hypotheses, test the significance of X3 in the regression model at the 5% level of significance. (f) Find a 95% confidence interval for the mean response μy when I₁ = 1, x₂ = −3, F3 = -1, that is, a= [1, 1, -3, -1]'.The table shows the total square footage (in billions) of retailing space at shopping centers and their sales (in billions of dollars) for 10 years. The equation of the regression line is ModifyingAbove y=589.637x−2143.147. Complete parts a and b. Total Square Footage, x 5.1 5.2 5.3 5.4 5.6 5.7 5.9 5.9 6.1 6.1 Sales, y 858.5 940.1 992.7 1064.6 1122.8 1203.5 1275.1 1339.3 1435.2 1533.3 (a) Find the coefficient of determination and interpret the result. nothing (Round to three decimal places as needed.)and test score (y) is V= 67.3+ 1.07x The same data yield r=0.224 and V=75.2. What is the best predicted test 8) Based on the data from six students, the regression equation relating number of hours of preparation (x) Score for a student who spent 2 hours preparing for the test? Use the given data to find the best predicted value of the response variable.
- Suppose you run a regression y=alpha + beta*x + u. From your Stata output you found that the estimated coefficient for constant is 5.03, the estimated coefficeint for slope is -2.83, the F-test is 7.27, and Root MSE is 20.308. What is the estimated form of your regression?A researcher is hired to investigate the relationship between the number of unauthorized days that employees are absent per year and the distance in miles between home and work for the employees. A sample of 10 employees was chosen and the results are listed in the chart below. Use the proper Data Analysis function in Excel to computethe regression equation needed to predict the number of missed days that could be expected foremployees who live certain distances away from the workplace. Distance to Work (miles): 1, 3, 4, 6, 9, 10, 12, 14, 14, 18 Number of Days Absent: 9, 6, 7, 7, 6, 4, 5, 3, 4, 2The cotton aphid poses a threat to cotton crops in Iraq. The accompanying data on y = infestation rate (aphids/100 leaves) X1 = mean temperature (°C) x, = mean relative humidity appeared in the article “Estimation of the Economic Threshold of Infestation for Cotton Aphid" (Mesopotamia Journal of Agriculture [1982]: 71–75). Use the data to find the estimated regression equation and assess the utility of the multiple regression model y = a + Bjx1 + Bx2 + e y X1 X2 y X1 X2 61 21.0 57.0 77 24.8 48.0 87 28.3 41.5 93 26.0 56.0 98 27.5 58.0 100 27.1 31.0 104 26.8 36.5 118 29.0 41.0 102 28.3 40.0 74 34.0 25.0 63 30.5 34.0 43 28.3 13.0 27 30.8 37.0 19 31.0 19.0 14 33.6 20.0 23 31.8 17.0 30 31.3 21.0 25 33.5 18.5 67 33.0 24.5 40 34.5 16.0 34.3 6.0 21 34.3 26.0 18 33.0 21.0 23 26.5 26.0 42 32.0 28.0 56 27.3 24.5 60 27.8 39.0 59 25.8 29.0 82 25.0 41.0 89 18.5 53.5 77 26.0 51.0 102 19.0 48.0 108 18.0 70.0 97 16.3 79.5 Given: significance level = 0.05 Required: 1. Regression Equation 2. F and…
- Consider the multiple regression model Y₁ = Bo + B₁x1₁j + B₂x2j+B3 x 3j+ €j under the usual assumptions labelled A1, A2, A3, A4, A5, A6. Briefly explain which type of graphs are performed in the analysis of residuals.Suppose that n = 50, i.i.d observations for (Y₁, X₁) yield the following regressions results: Ỹ= 49.2 + 73.9 X, SER = 13.4, R²=0.78 (23.5) (16.4) Another researcher is interested in the same regression, but makes an error when entering the data into a regression program: The research enters each observation twice, ending up with 100 observations (with observation 1 entered twice, observation 2 entered twice and so forth). Using these 100 observations, what results will be produced by the regression program? Complete the spaces in the equation below. Report the intercept and slope to one decimal place, but report the R2 to two decimal places. Ŷ = + * X, R² =Data is collected on the distance of several hikes (in miles), along with the amount of time (in minutes) the hike is expected to take. All hikes in the data set are between 0.5 miles and 10 miles long, and the relationship between distance and time is linear and strong. The regression equation to predict time based on distance is as follows: Predicted time = –266 + 31.48 (distance). Suppose we want to use the regression equation to predict the time it takes to complete a particular hike. For which one of the following distances would using the regression equation result in extrapolation? 1 mile 4.5 miles 6.8 miles 9 miles None of the above distances would result in extrapolation.