Question

You estimate a simple linear regression model using a sample of 25 observations and obtain the...

You estimate a simple linear regression model using a sample of 25 observations and obtain the following results (estimated standard errors in parentheses below coefficient estimates): y = 97.25 + 19.74 *x

(3.86) (3.42)

You want to test the following hypothesis: H0: beta2 = 1, H1: beta2 >12. If you choose to reject the null hypothesis based on these results, what is the probability you have committed a Type I error?

a.)between .01 and .02 b.)between .02 and .05 c.)less than .005 d.) It is impossible to determine without knowing the true value of beta2

Homework Answers

Answer #1

ANSWER:

The test statistic can be written as

which under H0 follows a t distribution with df n - 2

P(Type I error) =

Since in this case critical value or rejection region is not given, it not possible to compute Type I error.

ans-> d.) It is impossible to determine without knowing the true value of 2

Option:: (d) is correct............

NOTE:: I HOPE YOUR HAPPY WITH MY ANSWER....***PLEASE SUPPORT ME WITH YOUR RATING...

***PLEASE GIVE ME "LIKE"...ITS VERY IMPORTANT FOR ME NOW....PLEASE SUPPORT ME ....THANK YOU

Know the answer?
Your Answer:

Post as a guest

Your Name:

What's your source?

Earn Coins

Coins can be redeemed for fabulous gifts.

Not the answer you're looking for?
Ask your own homework help question
Similar Questions
Use the following results obtained from a simple linear regression analysis with 12 observations. = 37.2895...
Use the following results obtained from a simple linear regression analysis with 12 observations. = 37.2895 − (1.2024)X r2 = .6744sb = .2934 Test to determine if there is a significant negative relationship between the independent and dependent variables at α = .05. Give the test statistic and the resulting conclusion.  
The following table is the output of simple linear regression analysis. Note that in the lower...
The following table is the output of simple linear regression analysis. Note that in the lower right hand corner of the output we give (in parentheses) the number of observations, n, used to perform the regression analysis and the t statistic for testing H0: β1 = 0 versus Ha: β1 ≠ 0.   ANOVA df SS MS F Significance F   Regression 1     61,091.6455 61,091.6455 .69        .4259   Residual 10     886,599.2711 88,659.9271      Total 11     947,690.9167 (n = 12;...
Explain the limitations of the linear regression model. [5 marks] Using a sample of 1801 employees,...
Explain the limitations of the linear regression model. [5 marks] Using a sample of 1801 employees, the following earning equation has been estimated:                                (0.135)   (0.008)        (0.007)      (0.036) Where: Y is earnings, x1 is education level,x2 is experience and x3 is female The standard errors are the values in brackets. R2=0.179 Required: Interpret each of the coefficient estimates. [5 marks] At 5% significance level, test the hypothesis that there is no difference in expected earnings between male and female...
3.) Now, you are going to run the multivariable linear regression model you just created. For...
3.) Now, you are going to run the multivariable linear regression model you just created. For credit: Provide your model command and summary command below along with all the output for your model summary. Model1 <- lm(LifeExpect2017~HouseholdIncome + Diabetic + FoodInsecure + Uninsured + DrugOverdoseMortalityRate ) > summary(Model1) Call: lm(formula = LifeExpect2017 ~ HouseholdIncome + Diabetic + FoodInsecure + Uninsured + DrugOverdoseMortalityRate) Residuals: Min 1Q Median 3Q Max -5.4550 -0.8559 0.0309 0.8038 7.1801 Coefficients: Estimate Std. Error t value Pr(>|t|)...
Following is a simple linear regression model: The following results were obtained from some statistical software....
Following is a simple linear regression model: The following results were obtained from some statistical software. R2 = 0.523 syx (regression standard error) = 3.028 n (total observations) = 41 Significance level = 0.05 = 5% Variable Parameter Estimate    Std. Error of Parameter Est. Intercept 0.519    0.132 Slope of X    -0.707 0.239 Questions: the correlation coefficient r between the x and y is? What is the meaning of R2? Show your work.
Two simple (i.e, one x variable) linear regression models are fit. Model A, using variable x1...
Two simple (i.e, one x variable) linear regression models are fit. Model A, using variable x1 only, has an R2 of 0.23. Model B, using variable x2 only, has an R2 of 0.57. What will the R2 value be for Model C, which uses both x1 and x2? Cannot be determined without knowing the correlation between x1 and x2. It will be some value less than 0. It will equal 0.34 It will equal 0.80. We cannot say without knowing...
3.) Now, you are going to run the multivariable linear regression model you just created. For...
3.) Now, you are going to run the multivariable linear regression model you just created. For credit: Provide your model command and summary command below along with all the output for your model summary. Model1 <- lm(LifeExpect2017~HouseholdIncome + Diabetic + FoodInsecure + Uninsured + DrugOverdoseMortalityRate ) > summary(Model1) Call: lm(formula = LifeExpect2017 ~ HouseholdIncome + Diabetic + FoodInsecure + Uninsured + DrugOverdoseMortalityRate) Residuals: Min 1Q Median 3Q Max -5.4550 -0.8559 0.0309 0.8038 7.1801 Coefficients: Estimate Std. Error t value Pr(>|t|)...
You wish to estimate as precisely as possible the slope β1 in the simple linear regression...
You wish to estimate as precisely as possible the slope β1 in the simple linear regression model yi = β0 + β1xi + ei , i = 1, . . . , 4. Each pair of observations (xi , yi) costs $1.00 and your budget is $4.00. A data analyst proposes that you consider one of the following two options: (a) Make two y-observations at x = 1 and a further two at x = 4; (b) Make one y-observation...
6.    Consider the following sample regression results:             Y hat = 15.4 +    2.20 X1   +...
6.    Consider the following sample regression results:             Y hat = 15.4 +    2.20 X1   + 48.14 X2                 R2 = .355                      (6.14)     (.42)          (5.21)            n = 27 The numbers in the parentheses are the estimated standard errors of the sample regression coefficients. 6. (continued) a.    Construct a 95% confidence interval for b1. b.    Is there evidence of a linear relationship between X2   and Y at the 5% level of significance? c.    If you were to use a global test...
1. You are given the following data to fit a simple linear regression x 1 2...
1. You are given the following data to fit a simple linear regression x 1 2 3 4 5 y -2 4 2 -1 0 Using linear least squares, determine the t-value for testing the hypothesis that no linear relationship exist between y and x. (a) 0.01, (b) 0.03, (c) 0.09, (d) 0.11, (e) 0.13
ADVERTISEMENT
Need Online Homework Help?

Get Answers For Free
Most questions answered within 1 hours.

Ask a Question
ADVERTISEMENT