a)
least square regression line: Yhat=7+(-0.1)*x
b)
null hypothesis: Ho: >=0
c)
t ratio =coefficient/std error =-0.1/0.1 =-1
d)critical value =-1.676
e)
No
The USDA collects data on commodity prices and consumption. Use their data to explain per-capita chicken...
Problem 3 The USDA collects data on commodity prices and consumption. Use their data to explain per-capita chicken consumption as a function of the price of chicken. (Note that economic theory suggests that an increase in a commodity price is generally associated with a decrease in demand.) 0per capita chicken consumption PCHICK consumer price index for chicken Collapse Al We estimated a linear regression model using annual data for 1950-2001 (N 52) Dependent variable: coeffidient std. error const PCHICK -0.1...
Problem 3 The USDA collects data on commodity prices and consumption. Use their data to explain per-capita chicken consumption as a function of the price of chicken. (Note that economic theory suggests that an increase in a commodity price is generally associated with a decrease in demand.) 0 per capita chicken consumption PCHICK consumer price index for chicken We estimated a linear regression model using annual data for 1950-2001 (N 52). Dependent variable: coefficlent std. error const РСНСК -0.1 0.15...
Problem 3 The USDA collects data on commodity prices and consumption. Use their data to explain per-capita chicken consumption as a function of the price of chicken. (Note that economic theory suggests that an increase in a commodity price is generally associated with a decrease in demand.) Q capita chicken consumption PCHICKconsumer price index for chicken We estimated a linear regression model usng annual data for 1950-2001 ( N = 52 ). Dependent variable: coefficient d. error const 0.05 R-squared:...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA Students with higher High school GPA are expected to do better in college. colgpa = Grade point average in college (Range 0.85 -3.97) hsgpa = High school GPA (Range 2.29 -4.5) Expand Model 1: OLS, N=427 Dependent variable: colgpa coefficient std. error const 0.8 hsgpa 0.6 0.1 R-squared: 0.854880 a. Write the equation for the least squares...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. Students with higher High school GPA are expected to do better in college. colgpa Grade point average in college (Range 0.85-3.97) hsgpa High school GPA (Range 2.29-4.5) Model 1: OLS, N-427 Dependent variable: colgpa std. error 0.8 0.5 const hsgpa 0.1 R-squared: 0.854880 ,- a. (3%) write the equation for the least-squares regression line: b. (3%) The...
A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. school GPA are expected to do better in college. Students with higher High colgpa-Grade point average in college (Range 0.85 -3.97) hsgpaHigh school GPA (Range 2.29-4.5) Model 1: OLS, N-427 Dependent variable: colgpa coefficient std. error const hsgpa 0.5 0.15 R-squared: 0.854880 a. (3%) Write the equation for the least-squares regression line: y- b. (396) The null hypothesis is:...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. Students with higher High colgpa-: Grade point average in college (Range 085-397 ) hsgpa High school GPA (Range 2.29-4.5) school GPA are expected to do better in college. Model 1: OLS, N 427 Dependent variable: colgpa coefficient 0.9 0.4 std. error const hsgpa 0.15 R-squared: 0.854880 a. (3%) Write the ea ation for the least-squares regression line:...
Problem 7 A study of 427 college students was conducted to test whether high school GPA is a predictor of first-year college GPA. Students with higher High school GPA are expected to do better in college colgpa Grade point average in college (Range 0.85 3.97) hsgpa High school GPA (Range 2.29-4.5) Model 1: OLS, N -427 Dependent variable: colgpa coefficient 0.9 0.4 std. error const hsgpa 0.15 R-squared: 0.854880 a. (3%) Write the equation for the least-squares regression line: y-...
Regression model>BEEF_CONSt - Bl B2INCOMEt+B3BEEF PRICEt B4PORK PRICEt+ et BEEF CONSIconsumption of beef per capita in year t (kg), INCOMEt real income per capita in year t (thousands of dollars), BEEF PRICEt average real price of beef per kilogram in year t ($) PORK PRICEt= average real price of pork per kilogram in year t (S) Bk's regression cocfficients, and et is the random error term, which follows N(0, o2) Gretl Output for Section 2 Sunmary Stati stics Mean Median...
(13 points) Suppose you have a simple linear regression model such that Y; = Bo + B18: +€4 with and N(0,0%) Call: 1m (formula - y - x) Formula: F=MSR/MSE, R2 = SSR/SSTO ANOVA decomposition: SSTOSSE + SSR Residuals: Min 1Q Modian -2.16313 -0.64507 -0.06586 Max 30 0.62479 3.00517 Coefficients: Estimate Std. Error t value Pr(> It) (Intercept) 8.00967 0.36529 21.93 -0.62009 0.04245 -14.61 <2e-16 ... <2e-16 .. Signif. codes: ****' 0.001 '** 0.01 '* 0.05 0.1'' 1 Residual standard...