Suppose we estimated our multiple linear regression, all of the variables have p values below 0.05 (so they are statistically different from zero) but the intercept has p-value equal to p=0.343. What does it mean?

Suppose we estimated our multiple linear regression, all of the variables have p values below 0.05 (so they are statistically different from zero) but the intercept has p-value equal to p=0.343. What does it mean?

ENGR.ECONOMIC ANALYSIS

14th Edition

ISBN:9780190931919

Author:NEWNAN

Publisher:NEWNAN

Chapter1: Making Economics Decisions

Section: Chapter Questions

Problem 1QTC

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10 00 Interest Rate (%) N B Investment Demand 0 $30 60 90 120 150 Investment ($) Price Level Multiple Choice AS Real GDP ($) AD₁ (1=120) AD₂ (1=90) *AD3 (1=60) Refer to the graphs, in which the numbers in parentheses near the AD₁, AD2, and AD3 labels indicate the level of investment spending associated with each curve. All numbers are in billions of dollars. The interest rate and the level of investment spending in the economy are at point D on the investment demand curve. To achieve the long-run goal of a noninflationary, full-employment output Qfin the economy, the Fed should try to decrease aggregate demand by increasing the interest rate from 2 to 4 percent. decrease aggregate demand by increasing the interest rate from 4 to 6 percent. increase aggregate demand by decreasing the interest rate from 4 to 2 percent. increase the level of investment spending from $120 billion to $150 billion.
You are interested in how the number of hours a high school student has to work in an outside job has on their GPA. In your regression you want to control for high school standing and so you run the following regression: GPA = 3.4 0.03 * HrsWrk - 0.7 * Frosh - 0.3 * Soph +0.1 * Junior (1.1) (0.013) (0.23) (0.14) (0.08) where HrsWrk is the number of hours the student works per week, and Frosh, Soph, and Junior are dummy variables for the student's class standing. a) If you include a dummy variable for seniors, that would cause a Hint: type one word in each blank. For the rest of questions, type a number in one decimal place. b) The expected GPA of a Sophomore who works 10 hours per week is c) The expected GPA of a Senior who works 10 hours per week is d) If Dom and Sarah work the same number of hours per week, but Dom is a Junior and Sarah is a Freshman. Dom is expected to have a higher GPA than Sarah. e) Suppose you rewrite the regression as: problem. GPA = ₁HrsWrk + ß2Frosh + B2Soph +…
In an OLS regression, which value represents the "best" R2 in terms of explained variance in the dependent variable? A. 2.53 B. 16.22 C. .001 D. 0.53
5.2 42 The following regression shows the impact of the number of rooms (ROOM) on the monthly rent of an apartment (in thousand Rands) (RENT) from a sample of 42 randomly chosen apartments in Parow: Dependent Variable: RENT Variable C ROOM r-square: 0.7180 Parameter -0.7705 2.1331 SE of regression: 2.3397 Std. Error 0.8233 0.2114 sample size = 42 What will be values of the parameters, variance and standard errors of the parameters, estimated variance of error terms, coefficient of determination and correlation coefficient, if RENT is measured in Rands and there is no change to the unit of measurement of the ROOM variable?
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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…
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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.
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When running a ols regression, if my control variables are insignificant via T-test should I keep them in the regression? Are they significant?
QUESTION 2 Consider the following bivariate linear regression model y = a+3x+u. Suppose that E[u]x] #0 and that z is a valid instrument for r. Knowing that Cov(y, z) = 0.5 and Cov(z, x) = 0.5, the IV estimate of 3 is 1. %3D O True O False