[8] A simple regression was performed using data on 50 states in
USA. The variables were Y = current value of capital building
(millions of dollars) and X = total debt held by each state bank
(millions of dollars). Use α=0.01
Variables |
Coefficients |
Standard error |
Intercept Debt |
b0 b1 |
Sb0 Sb1 |
a) Use the degree of freedom (df=n-2 =48) for the t-test and find the two-tailed critical value for t by using the table
b) Construct the 99 % confidence interval for the slope
(β1).
NOTE : CI for true slope è [ b1-
tα/2
Sb1≤
β1≤
b1+
tα/2
Sb1
]
c) Can you tell the result of Hypothesis test if the slope = 0 with 99 % level of significance? Explain why?
[8] A simple regression was performed using data on 50 states in USA. The variables were...
5. Summary of regression between a dependent variable y and two independent variables X, and x2 is as follows. Please complete the table: SUMMARY OUTPUT Regression Statistics Multiple R 0.9620 R Square R2E? Adjusted R Square 0.9043 Standard Error 12.7096 Observations 10 ANOVA F Significance F F=? Overall p-value=? Regression Residual Total 2 df of SSE MS MSR=? MSE? 14052.1550 1130.7450 SSTE? MSE? 9 Coefficients -18.3683 Standard Error 17.9715 t Stat -1.0221 Intercept ty=? 2.0102 4.7378 0.2471 0.9484 P-value 0.3408...
A regression analysis is performed using data for 36
single-family homes to predict appraised value (in thousands of
dollars) based on land area of the property (in acres), X1i, and
age (in years), X2i, in month i. Use the results below to
complete parts (a) and (b) below.
Variable
Coefficient
Standard Error
t Statistic
p-value
Intercept
392.60372
51.68272
7.60
0.0000
Area, X1
451.43475
100.48497
4.49
0.0001
Age,X2
−2.17162
0.79077
−2.75
0.0097
a. Construct a 95% confidence interval estimate...
For a sample of USA industries the following variables are recorded: YOUTPUT is the total production of each industry in millions of dollars in constant prices. WAGES corresponds to the total wages of the each industry in millions of dollars in constant prices. KCAPITAL is the fixed capital of each industry in millions of dollars in constant prices. Labor is the total number of employees in each industry in thousands. D1 is a dummy variable which takes the value of...
An aircraft company wanted to predict the number of worker-hours necessary to finish the design of a new plane. Relevant explanatory variables were thought to be the plane’s top speed, its weight, and the number of parts it had in common with other models built by the company. A sample of 27 of the company’s planes was taken, and the following model was estimated: y = b0 + b1x1 + b2x2 + b3x3 + e where y = design effort,...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
8. A regression of wage (log(wage) is run on a set of following variables: female (-1 if female), educ (years of education), exper (years of experience) and tenure (years with current employer). The regression results are listed as follows. Coefficients: Estimate Std. Error tvalue Pr(Itl) (Intercept) -1.56794 0.72455 -2.164 0.0309 female -1.81085 0.26483 -6.838 2.26e-11*** educ 0.57150 0.04934 11.584 <2e-16*** 0.02540 0.01157 2.195 0.0286 exper 0.14101 0.02116 6.663 6.83e-11*** tenure Signif. codes:0.0010.010.050.1'"1 Residual standard error: 2.958 on 521 degrees of...
Data were gathered from a simple random sample of cities. The variables are Violent Crime (crimes per 100,000 population), Police Officer Wage (mean $/hr), and Graduation Rate (%). Use the accompanying regression table to answer the following questions consider the coefficient of Graduation Rate. Complete parts a through e Dependent variable is: Violent Crime R squared=36.1 R squared (adjusted)equals=38.2% s=129.6 with 37 degrees of freedom Variable Coeff SE (Coeff) t-ratio P-value Intercept 1391.86 187 .88 7.41 < 0.0001 Police Officer...
Hi! Can someone help me to calculate the last question, the CI?
Thanks!
1 point) College Graduation Rates. Data from the College Results Online website compared the 2011 graduation rate and school sze for 92 similar-sized public universities and colleges in the United States. Statistical sottware was used to places. create the linear regression model using size as the explanatory variable and graduation rate as the response variable. Summary output from the software and the scatter plot are shown below....