For variables x1, x2, x3, and y satisfying the assumptions for multiple linear regression inferences, the population regression equation is y = 27 – 4.7x1 + 2.3x2 + 5.8x3. For samples of size 20 and given values of the predictor variables, the distribution of the estimates of ß1 for all possible sample regression planes is a _________ distribution with mean a_________ and standard deviation _______.

For variables x1, x2, x3, and y satisfying the assumptions for multiple linear regression inferences, the population regression equation is y = 27 – 4.7x1 + 2.3x2 + 5.8x3. For samples of size 20 and given values of the predictor variables, the distribution of the estimates of ß1 for all possible sample regression planes is a _____ distribution with mean a_ and standard deviation ___.

Linear Algebra: A Modern Introduction

4th Edition

ISBN:9781285463247

Author:David Poole

Publisher:David Poole

Chapter7: Distance And Approximation

Section7.3: Least Squares Approximation

Problem 31EQ

See similar textbooks

Similar questions

Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
For a sample of 74 monthly observations the regression of the percentage return on gold (y) against the percentage change in the consumer price index (x) was estimated. The sample regression line, obtained through least squares, was as follows: y = -0.003 + 1.11x The estimated standard deviation of the slope of the population regression line was 2.31. Test the null hypothesis that the slope of the population regression line is 0 against the alternative that the slope is positive.
Which of the following is/are true if the coefficient of determination between the response and the predictor variables is 81 percent based on a random sample of size n i. the pearson's sample correlation coefficient is +0.9 ii. this means that 19 percent of the total variation in the response variable remains unexplained by tge simple linear regression model that uses the given predictor variable iii both i and ii iv neither i and ii
At any given combination of values , the assumptions for the multiple regression model require that the population of potential error term values has a. a constant variance. b. a normal distribution. c. a mean equal to zero. d. All three of the assumptions are required.
In the above plot, a rejection of the null hypothesis that the population regression slope is equal to 0 implies: a) the correlation coefficient in the population is likely equal to 0 b) the correlation coefficient in the population is likely unequal to 0 c) the standard deviation of height is likely very large d) the standard deviation of weightis likely very small e) the correlation coefficient must be close to 1.0 f) a and c g) b and e h) b, d and e
9)Suppose that Y is normal and we have three explanatory unknowns which are also normal, and we have an independent random sample of 11 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 0.79, 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 79000 and the sum of squared errors (SSE) is 21000. From this information, what is the adjusted R-square? .8 .7 NONE OF THE OTHERS .6 .5
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 researcher would like to predict the dependent variable YY from the two independent variables X1X1 and X2X2 for a sample of N=10N=10 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level α=0.01α=0.01. X1X1 X2X2 YY 58.8 29.9 63.1 64.1 57.3 40.1 51.4 35.3 46.2 77.1 88.5 30 60.6 67.5 16.2 68.3 63.4 62 44.8 6.6 77.6 49 29.3 65.5 55.5 25.8 62.5 57.5 30.2 62 R2=R2= F=F= P-value for overall model = t1=t1= for b1b1, P-value = t2=t2= for b2b2, P-value = What is your conclusion for the overall regression model (also called the omnibus test)? The overall regression model is statistically significant at α=0.01α=0.01. The overall regression model is not statistically significant at α=0.01α=0.01. Which of the regression coefficients are statistically different from zero? neither regression coefficient is…
For each of the following, explain what is wrong and why. a)In simple linear regression, the null hypothesis of the ANOVA F test is H0: β0 = 0. b)In an ANOVA table, the mean squares add. In other words, MST = MSM + MSE. c)The smaller the P-value for the ANOVA F test, the greater the explanatory power of the model. d)The total degrees of freedom in an ANOVA table are equal to the number of observations n.
Consider the following sample regression equation yˆ = 150 − 20x, where y is the demand for Product A (in 1,000s) and x is the price of the product (in $). The slope coefficient indicates that if _____
A researcher would like to predict the dependent variable Y from the two independent variables X1 and X2 for a sample of N=13 subjects. Use multiple linear regression to calculate the coefficient of multiple determination and test statistics to assess the significance of the regression model and partial slopes. Use a significance level α=0.01. X1 X2 Y 49 45.3 42.4 46.3 48.1 47.6 46.2 57 35.8 39.1 50.5 66.5 31.3 47.1 62.1 50 48.4 61.8 43.6 47.4 47.8 39.1 42.2 77.2 45.2 54.7 44.7 53.2 56.8 63.9 38.4 34 70.7 28.3 43.3 49.1 38.9 51.9 49.8 R2= F=P-value for overall model = t1= for b1, P-value = t2= for b2, P-value = What is your conclusion for the overall regression model (also called the omnibus test)? The overall regression model is statistically significant at α=0.01. The overall regression model is not statistically significant at α=0.01. Which of the regression coefficients are statistically different from zero? neither regression coefficient…