The t value obtained from the table which is used to test an individual parameter at the 1% level is a. 2.977. b. 2.921. c. 3.012. d. 2.650.
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| Coefficients | Standard Error | |||
| Constant | 12.924 | 4.425 | ||
| x1 | -3.682 | 2.630 | ||
| x2 |
45.216 | 12.560 | ||
| Analysis of Variance | ||||
| Source of Variation |
Degrees of Freedom |
Sum of Squares |
Mean Square |
F |
| Regression | 4853 | 2426.5 | ||
| Error | 485.3 |
The t value obtained from the table which is used to test an individual parameter at the 1% level is
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- I need help with these questions 1. do the covariates and factors interact? 2. can you conclude a homogeneity of regression slopes? 3. can you conclude homogeneity of variance?In a multiple regression, which of the following tests can be used to determine if the relationship between the dependent variable and the set of independent variables is significant? The Coefficient Of Determination R-Squared for each coefficient of the X variables & an F- Statistic Test for the overall regression F-Statistic tests for each coefficient & a t-test for the overall regression The Coefficient Of Determination R-Squared and/or an F-Statistic Test for the overall regression & t-tests for each coefficient of the X variables t-tests for each coefficient and for the overall regressionThe summary output obtained from fitting the multiple regression are given below. Model Unstandardized Coefficients Standardized Sig. Coefficients B Std. Error Beta -3.512 (Constant) Education (years) -3019.226 859.789 .000 658.518 45.852 .581 14.362 .000 Gender -1615.440 253.239 -.249 -6.379 .000 Age (years) 45.008 10.469 .163 4.299 .000 Dependent Variable: Beginning Salary, Male=0 & Female=1. (a) Write down the estimated multiple regression model of the beginning salary on education, gender and age of employees of a company. (b) Interpret estimated regression coefficient values. (c) Find the predicted beginning salary for an employee who is 24 years old male and has 17 years of education.
- A sales manager for an advertising agency believes there is a relationship between the number of contacts that a salesperson makes and the amount of sales dollars earned. A regression analysis shows the following results. Coefficients Standard Error t-Stat p-value Intercept -12.201 6.560 -1.860 0.100 Number of contacts 2.195 0.176 12.505 0.000 ANOVA df SS MS F Significance F Regression 1.00 |13,555.42 |13,555.42 156.38 0.00 Residual 8.00 693.48 86.68 Total 9.00 14,248.90 Assume that X = 33.4 and E(X – X) 2814.4. Rounding to one decimal place, the 95% confidence interval for 30 calls isYou may need to use the appropriate technology to answer this question. Following is a portion of the computer output for a regression analysis relating y = maintenance expense (dollars per month) to x = usage (hours per week) of a particular brand of computer terminal. Analysis of Variance SOURCE DF Adj SS Adj MS Regression 1 1575.76 1575.76 Error 8 349.14 43.64 Total 9 1924.90 Predictor Coef SE Coef Constant 6.1092 0.9361 X 0.8951 0.1490 Regression Equation Y = 6.1092 + 0.8951 X #1) Write the estimated regression equation. ŷ = #2) 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.) #3)Use the estimated regression equation to predict monthly maintenance expense (in dollars per month) for any terminal that is used 15 hoursper week. (Round your answer to the nearest cent.) $ _____per monthGive proper explanation
- Sam had 3 caffeinated drinks on april 24 Use linear regression to predict the number non-caffeinated drink he had on April 24 and calculate the error of estimate for your prediction.The data are the ages of criminals and their victims. The regression output is shown in a separate tab from the data. Do the data support that a prediction of victim age can be obtained given the data provided in the file? Cite the elements of the output you used to draw your conclusion.The regional manager of a franchise business is interested in understanding how income in a region affects sales. Below is a regression output for sales ($’000) regressed on the average household income of an area ($’000) Linear Fit Sales = 14.5774 + 2.9048*Income Summary of Fit RSquare 0.9683 RSquare Adj 0.9630 Root Mean Square Error 3.1083 Mean of Response 43.6250 Analysis of Variance Source DF Sum of Squares Mean Square F Ratio Model 1 1771.9048 1771.90 183.3946 Error 6 57.9702 9.66 Pro>F C. Total 7 1829.8750 ItI Intercept 14.5774 2.4101 6.05 0.0009* Income 2.9098 0.2145 13.54 < 0.0001* Answer the following questions: (i) What is the average sales across all regions? (ii) Interpret the slope of regression (iii) What is the prediction of the value of sales in a region with an average…
- The commercial division of a real estate firm is conducting a regression analysis of the relationship between x, annual gross rents (in thousands of dollars), and y, selling price (in thousands of dollars) for apartment buildings. Data were collected on several properties recently sold and the following computer output was obtained. Analysis of Variance SOURCE DF Adj SS Regression 1 41587.3 Error 7 Total 8 51984.1 Predictor Coef SE Coef T-Value Constant 20.000 3.2213 6.21 X 7.210 1.3626 5.29 Regression Equation Y = 20.0 + 7.21 X (a) How many apartment buildings were in the sample? (b) Write the estimated regression equation. ŷ = (c) What is the value of sb1? 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 =An automobile rental company wants to predict the yearly maintenance expense (Y) for an automobile using the number of miles driven during the year () and the age of the car (, in years) at the beginning of the year. The company has gathered the data on 10 automobiles and run a regression analysis with the results shown below:. Summary measures Multiple R 0.9689 R-Square 0.9387 Adj R-Square 0.9212 StErr of Estimate 72.218 Regression coefficients Coefficient Std Err t-value p-value Constant 33.796 48.181 0.7014 0.5057 Miles Driven 0.0549 0.0191 2.8666 0.0241 Age of car 21.467 20.573 1.0434 0.3314 Use the information above to estimate the annual maintenance expense for a 10 years old car with 60,000 miles.What does the slope and the intercept mean in this setting?
