The F-statistic for the overall regression model and t-statistic for the slope coefficient are, respectively, closest to
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An analyst analyses data on 12 flights of comparable distances by regressing Cost (in €'000s) on Load (number of passengers). She obtains the following least squares Analysis of Variance table (omitted output values are denoted?):
The F-statistic for the overall regression model and t-statistic for the slope coefficient are, respectively, closest to
a. 89.080 and 9.438
b. 89.080 and 3.145
c. 9.890 and 3.145
d. 9.890 and 2.985
e. 8.908 and 2.985
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- The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 2020 3030 3535 4242 4949 Number of Bids 33 44 55 66 99 Table Copy Data Step 1 of 6 : Find the estimated slope. Round your answer to three decimal places.Researchers found a positive assodation between the students' performance in STAT 1000 and their first-year cumulative college GPA. Furthermore, STAT 1000 course GPA explained 62% of the variation in students'first-year cumulative college GPA. The summarized data is given below: Mean STAT 1000 GPA = 25 Std. dev. - 0.21 Mean cumulative College GPA = 325 Std. dev, = 03 The slope and intercept of the least squares regression line for predicting first-year college GPA from STAT 1000 scores are, respectively. Oa Slope 055 Intercept 1.88 Ob Slope = 1.12 Intercept 1.88 Oc Slope = 1.12 Intercept 044 Od. Slope 044 Intercept 055 Oe Slope- 1.88 Intercept 055The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Find the estimated y intercept . Round your answer to three decimal places. age 35 41 52 56 66 bone density 358 350 348 332 321
- The following data relate to marks in Advanced Accounts and Business Statistics in B.Com. (H.), Il yr Examination of a particular year in Delhi University : Mean Marks in Advanced Accounts Mean Marks in Business Statistics Standard Deviation of Marks in Advanced Accounts Standard Deviation of Marks in Business Statistics Coefficient of Correlation between the Marks in Advanced Accounts and Business Statistics Find the two regression lines and calculate the expected marks in Advanced Accounts if the marks secured by a student in Business Statistics are 40. = 30 = 35 = 10 = 7 = 0-811. What is the standard error of the slope of the regression line (SEb)? Please put the answer in bold.The table below gives the list price and the number of bids received for five randomly selected items sold through online auctions. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the number of bids an item will receive based on the list price. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Price in Dollars 2323 3434 4040 4646 4747 Number of Bids 11 33 44 55 77 Table Copy Data Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places.
- The following table shows the starting salary and profile of a sample of 10 employees in a certain call center agency. Run a multiple regression analysis with starting salary as the dependent variable (pesos) and GPA, years of experience and civil service ratings as the independent variables. Use .05 level of significance.What is the equation of the resulting multiple linear regression? starting_salary = 3008.61 + 48.65*GPA + 94.79*years_of_experience + 27.36*civil_service_ratings starting_salary = 15000.00 + 48.65*GPA + 94.79*years_of_experience + 27.36*civil_service_ratings starting_salary = 15001.00 + 41.43*GPA + 84.71*years_of_experience + 37.32*civil_service_ratings starting_salary = 2366.77 + 130.25*GPA + 396.39*years_of_experience + 21.67*civil_service_ratingsThe table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1x�^=�0+�1�, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 44 52 54 62 70 Bone Density 346 342 332 325 323 Step 1 of 6: Find the estimated slope. Round your answer to three decimal places. Step 2 of 6: Find the estimated y-intercept. Round your answer to three decimal places. Step 3 of 6: According to the estimated linear model, if the value of the independent variable is increased by one unit, then the change in the dependent variable yˆ is given by? a. b0 b. b1 c. x d. y Step 4 of 6: Find the estimated value of y when x=52. Round your…4. To assess the effect of state right to work laws on union membership (which do not require membership in unions as a precondition for employment), the following regression results were obtained from the data for 50 states in the United States for 1982: PVT, = 19.8066 – 9.3917 RTW, t= (17.0352) (-5.1086) R = 0.3522 where PVT = percentage of private sector employees in unions in 1982 RTW = 1 if right-to-work law exists, 0 otherwise (in 1982, 20 states had right- to-work laws). %3D A priori, what is the expected relationship between PVT and RTW? Do the regression results support this expected relationship? Interpret the regression results. What was the average percent of private sector employees in unions in the states that did not have the right-to-work laws?
- The table below gives the age and bone density for five randomly selected women. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting a woman's bone density based on her age. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Age 3636 5252 5858 6464 6868 Bone Density 336336 335335 318318 317317 314314 Table Copy Data Step 2 of 6 : Find the estimated y-intercept. Round your answer to three decimal places.What are the different measures used for the OLS estimate of the multiple regression line?Listed below are the overhead widths (cm) of seals measured from photographs and weights (kg) of the seals. Find the regression equation, letting the overhead width be the predictor (x) variable. Find the best predicted weight of a seal if the overhead width measured from a photograph is1.8cm, using the regression equation. Can the prediction be correct? If not, what is wrong? Use a significance level of 0.05. Overhead_Width_(cm) Weight_(kg)7.2 1287.4 1679.8 2619.5 2218.7 2118.3 201 The regression equation is y=enter your response here+enter your response herex. (Round the y-intercept to the nearest integer as needed. Round the slope to one decimal place as needed.) Part 2 The best predicted weight for an overhead width of 1.8 cm, based on the regression equation, is enter your response here kg. (Round to one decimal place as needed.) Part 3 Can the prediction be correct? If not, what is wrong? A. The prediction cannot be correct…