q7 part 1 You may need to use the apPpropriate technology to answer this question.Consider the following data for a dependent variable y and two independent variables, x, and x,.x1x2y30129347101082517112511617840945119175747170361211759131427616209The estimated regression equation for these data isý = -17.54 + 2.00x, + 4.70x2.Here, SST = 14,995.6, SSR = 13,855.0,= 0.2482, and s= 0.9526.(a) Test for a significant relationship among x,, x, and y. Use a = 0.05.State the null and alternative hypotheses.O Ho: B1 > B2O Ho: B1 = B2 = 0One or more of the parameters is not equal to zero.%3DO Ho: B, +0 and B2 = 0= 0 and B, * oO Ho: B, +0 and B, +0H: One or more of the parameters is equal to zero.O Họ: B1 < B21.Find the value of the test statistic. (Round your answer to two decimal places.)Find the p-value. (Round your answer to three decimal places.)p-value =State your conclusion.O Reject Hn. There is sufficient evidence to conclude that there is a significant relationship among the variables.Do not reject Ho: There is sufficient evidence to conclude that there is a significant relationship among the variables.Do not reject Hn: There is insufficient evidence to conclude that there is a significant relationship among the variables.O Reject Hn. There is insufficient evidence to conclude that there is a significant relationship among the variables.

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Description: q7 part 1 You may need to use the apPpropriate technology to answer this question.Consider the following data for a dependent variable y and two independent variables, x, and x,.x1x2y30129347101082517112511617840945119175747170361211759131427616209Th

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sider the following data for a dependent variable y and two independent variables, x, and x,. X1 x2 y 30 12 93 47 10 108 25 17 112 51 16 178 40 94 51 19 175 74 7 170 36 12 117 59 13 142 76 16 209 estimated regression equation for these data is ý = -17.54 + 2.00x, + 4.70x2. e, SST = 14,995.6, SSR = 13,855.0, = 0.2482, and s. = 0.9526. Test for a significant relationship among x,, x2, and y. Use a = 0.05. State the null and alternative hypotheses. O Ho: B1 > B2 1 O Ho: B1 = B2 = 0 H: One or more of the parameters is not equal to zero. O Ho: B, +0 and B, = 0 = 0 and B, *0 OH O Ho: B1 *0 and B2 + 0 H: One or more of the parameters is equal to zero. O Hoi Bq < Bz Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. O Reject Hn. There is sufficient evidence to conclude that there is a significant relationship among the variables. O Do not reject Hp: There is sufficient evidence to conclude that there is a significant relationship among the variables.

sider the following data for a dependent variable y and two independent variables, x, and x,. X1 x2 y 30 12 93 47 10 108 25 17 112 51 16 178 40 94 51 19 175 74 7 170 36 12 117 59 13 142 76 16 209 estimated regression equation for these data is ý = -17.54 + 2.00x, + 4.70x2. e, SST = 14,995.6, SSR = 13,855.0, = 0.2482, and s. = 0.9526. Test for a significant relationship among x,, x2, and y. Use a = 0.05. State the null and alternative hypotheses. O Ho: B1 > B2 1 O Ho: B1 = B2 = 0 H: One or more of the parameters is not equal to zero. O Ho: B, +0 and B, = 0 = 0 and B, 0 OH O Ho: B1 0 and B2 + 0 H: One or more of the parameters is equal to zero. O Hoi Bq < Bz Find the value of the test statistic. (Round your answer to two decimal places.) Find the p-value. (Round your answer to three decimal places.) p-value = State your conclusion. O Reject Hn. There is sufficient evidence to conclude that there is a significant relationship among the variables. O Do not reject Hp: There is sufficient evidence to conclude that there is a significant relationship among the variables.

College Algebra

7th Edition

ISBN:9781305115545

Author:James Stewart, Lothar Redlin, Saleem Watson

Publisher:James Stewart, Lothar Redlin, Saleem Watson

Chapter1: Equations And Graphs

Section: Chapter Questions

Problem 10T: Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s...

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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?
The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
For the following exercises, consider the data in Table 5, which shows the percent of unemployed in a city ofpeople25 years or older who are college graduates is given below, by year. 41. Based on the set of data given in Table 7, calculatethe regression line using a calculator or othertechnology tool, and determine the correlationcoefficient to three decimal places.
Which of the multivariate regression parameters listed below would be best interpreted as: the predicted value on the dependent variable when all of the independent variables in the model are equal to zero. a b1 X1 R2
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Given below are results from the regression analysis where the dependent variable is the number of weeks a worker is unemployed due to a layoff (Unemploy) and the independent variables are the age of the worker (Age), the number of years of education received (Edu), the number of years at the previous job (Job Yr), a dummy variable for marital status (Married: 1=married, 0=otherwise), a dummy variable for head of household (Head: 1=yes, 0=no) and a dummy variable for management position (Manager: 1=yes, 0=no). We shall call this Model 1. The coefficient of partial determination (R2Yj.(All variables except j)) of each of the six predictors are, respectively, 0.2807, 0.0386, 0.0317, 0.0141, 0.0958, and 0.1201. Model 2 is the regression analysis where the dependent variable is Unemploy and the independent variables are Age and Manager. The results of the regression analysis are given. Refer to model 1. Which of the following is the correct null hypothesis to test…
Which of the multivariate regression parameters listed below would be best interpreted as: the predicted change in the dependent variable for a one-unit change in an independent variable, holding constant all the other independent variables in the model. a b1 X1 R2
If a sample of 25 pairs of data yields a correlation coefficient, r, of 0.390 and the scatterplot displays a linear trend, can you use the regression equation to make predictions, assuming your x-values are within the domain of the data set? Choose your answer from the multiple choice answers below A.) Yes, because rcrit = 0.396 and the regression coefficient, r, is less than this value. B.) Yes, because rcrit = 0.381 and the regression coefficient, r, is greater than this value. C.) No, because rcrit = 0.381 and the regression coefficient, r, is greater than this value. D.) No, because rcrit = 0.396 and the regression coefficient, r, is less than this value.
The following multiple regression printout can be used to predict a person's height (in inches) given his or her shoe size and gender, where gender = 1 for males and 0 for females. Regression Analysis: Height Versus Shoe Size, Gender Coefficients Term Coef SE Coef T-Value P-Value Constant 55.26 1.08 51.17 0.000 Shoe Size 1.167 0.17 0.000 Gender 2.578 0.482 5.35 0.000 (a) Find the value of the test statistic for shoe size. (Round your answer to two decimal places.) t = ?? (b) Is the regression coefficient of shoe size statistically significant? (Use ? = 0.05.) The regression coefficient of shoe size is/is not statistically significant. (c) Does the variable shoe size belong in the model? The variable shoe size may/may not belong in the model. (d) Interpret the regression coefficient of Gender. Males are (??) inches taller than females, on average, controlling for shoe size.
Consider the following scenario for Questions 6 through 9: The City of Bellmore’s police chief believes that maintenance costs on high-mileage police vehicles are much higher than those costs for low-mileage vehicles. If high-mileage vehicles are costing too much, it may be more economical to purchase more vehicles. An analyst in the department regresses yearly maintenance costs (Y) for a sample of 200 police vehicles on each vehicle’s total mileage for the year (X). The regression equation finds: Y = $50 + .030X with a r2 of .90 What is the IV? What is the DV? If the mileage increases by one mile, what is the predicted increase in maintenance costs? If a vehicle’s mileage for the year is 50,000, what is its predicted maintenance costs? What does an r2 of .90 tell us? Is this a strong or weak correlation? How can you tell?
The personnel director of a large hospital is interested in determining the relationship (if any) between an employee’s age and the number of sick days the employee takes per year. The director randomly selects ten employees and records their age and the number of sick days which they took in the previous year. Employee 1 2 3 4 5 6 7 8 9 10Age 30 50 40 55 30 28 60 25 30 45Sick Days 7 4 3 2 9 10 0 8 5 2 The estimated regression equation and the standard error are given. Sick Days=14.310162−0.236900(Age) Se=1.682207 Find the 95% prediction interval for the average number of sick days an employee will take per year, given the employee is 34 . Round your answer to two decimal places.