Run the Multiple Linear Regression using Fatalities as the dependent variable and Licensed drivers, registered vehicles, and GDP per capita as independent variables.  Submit Excel File Calculate the following: a- Interpret the R-square b-Find the equation of the fitted line c- Which variable has the strongest relationship with number of fatalities? d- Identify which variables are significant/non-significant using alpha = 0.05?   Fatalities  Licensed Drivers Registered Vehicles GDP per Capita (measured) 953 3999057 5300199 40598 80 536033 803684 71996 1010 5284970 5806313 43464 516 2145334 2817145 38919 3563 27039400 31022328 68970 632 4244713 5356018 59885 294 2605612 2879802 68555 111 786504 1008468  64895 31 527731 351933 176498 3133 15368695  17496002 43423 1504 7168733 8512550 50288 117 948417 1267385 58185 231 1252535 1879670 40189 1031 8714788 10588910 60419 858 4589405 6190736 49209 318 2260271 3691892 54520 404 2149430 2684010 53094 724 3032530 4368285 41898 768 3425435 3885119 50938 137 1040582 1125588 42925 501 4407973 4204846 61042 360 4944666 5061499 73321 974 7153645 8380387  47072 381 3391057 5404277 59510 664 2058036 2067498 34434 921 4272960 5498675 46507 182 806204 1845338 43509 230 1420317 1961309 59178 330 1983453 2514338 49361 147 1161665 1346318 55905 564 6342876 6055389 62385 391 1458433 1824217 44671 943 12194360 11482229 73463 1437 7509231 8210213 47896 105 561333 899953 69563 1068 8032665 10913773 51790 655 2504253 3699022 49840 506 2930702 3942875 51677 1190 8991370 10727715 55580 59 756966 872344 50718 1037 3846069 4457519 40755 130 638428 1269415 52696 1041 5422429 5770874 47810 3642 17370383 22186241 59674 260 2030644  2372800 50236 68 564892 619694 47502 820 5929031 7604646  56002 546 5909967 7152413 67901 294 1136774.765 1693719 39584 588 4288171 5683061 51883 111 419256 837024 65843

QUESTION 10 Answer questions 10 to 16 based on the regression outputs given in Table 1& 2. Table 1 DATA4-1: Data on single family homes in University City community of San Diego, in 1990. price - sale price in thousands of dollars (Range 199. 9 505) sqft - square feet of living area (Range 1065 - 3000) Table 2 Model 1: OLS, using observations 1-14 Dependent variable: price coefficient std. error t-ratio p-value 52. 3509 0.138750 37. 2855 0.0187329 0. 1857 8. 20e-06 *** const sqft 7. 407 Me dependent var Sun squared resid R-squared F(1, 12) Log-likelihood Schwarz criterion 317. 4929 18273. 57 0. 820522 54. 86051 -70. 08421 145. 4465 Hannan-Quinn S.D. dependent var S.E. of regression Adjusted R-squared P-value (F) Akaike criterion 88. 49816 39. 02304 0. 805565 8. 20e-06 144. 1684 144. 0501 There are observations included in this dataset. It is a. data. O 12; cross-sectional 13; time-series data 14; cross-sectional In this regression model, sale price of a single-family house is the. the…

The data for this question is given in the file 1.Q1.xlsx(see image)  and it refers to data for some cities X1 = total overall reported crime rate per 1 million residents X3 = annual police funding in $/resident X7 = % of people 25 years+ with at least 4 years of college   (a) Estimate a regression with X1 as the dependent variable and X3 and X7 as the independent variables. (b) Will additional education help to reduce total overall crime (lead to a statistically significant reduction in crime)? Please explain. (c) Will an increase in funding for the police departments help reduce total overall crime (lead to a statistically significant reduction in total overall crime)? Please explain. (d) If you were asked to recommend a policy to reduce crime, then, based only on the above regression results, would you choose to invest in education (local schools) or in additional funding for the police? Please explain.

6 9 13 Yi 7 18 9. 26 23 a. Which of the following scatter diagrams accurately represents the data? A. 24+ 24+ 20- 20- 16+ 16+ 12+ 12+ 8+ 8+ 4- -4 4 12 16 24 28 x -4 4 8. 12 16 20 24 28 x -4t -4t С. D. 24 -----24+ 20- 20- 16- 16- 20 B. 20 00 00 O.

Define coefficients of the Linear Regression Model?

E3

please answer in text form and in proper format answer with must explanation , calculation for each part and steps clearly

You are given information on the use of public transportation in the data set within the image. Number of weekly riders                                     y -variable Price per week (of using public transportation) x-variable Population of city                                                 x-variable Monthly income of riders                                     x variable         Average parking rates per month x-variable   (a) Run a regression of the Number of weekly riders on Price per week, Population of city, Monthly income of riders, Average parking rates per month. Please copy and paste your regression results and do not submit your EXCEL worksheet.   (b) If the average parking rates increase by $1, what will be the change in the number of weekly riders? Will this change be a statistically significant change? Please explain.   (c) If the price per week (of using public transportation) increases by $1, what will be the change in the number of weekly riders? Will this change be…

In the model Y = Bo +B 1X 1 + B 2X 2 + 8, which of these parameters represents a coefficient of an independent variable? the Y the X1 the B1 the e

Explain the OLS Estimator in Multiple Regression in detail?