An F-test is a type of statistical test that is very flexible. You can use them in a wide variety of settings. F-tests can evaluate multiple model terms simultaneously, which allows them to compare the fits of different linear models. The overall F-test compares the model that you specify to the model with no independent variables.
F-test has two significance hypothesis:-
a- Null hypothesis:- Model with no independent variable.
b- Alternate hypothesis:- Model fits the data better than the intercept-only model
p-value of 0.0000 |
Interpreting the F-test:-
Compare the p-value for the F-test to your significance level. If the p-value is less than the significance level, your sample data provide sufficient evidence to conclude that your regression model fits the data better than the model with no independent variables. It means that the independent variables in your model improve the fit.
Suppose you run a regression and the Serial Correlation LM Test, has a p-value of 0.0000. How would you interpret the results using an F-test?
Suppose that you run a correlation and find the correlation coefficient is 0.206 and the regression equation is ˆ y = − 33.96 + 7.6 x . The mean for the x data values was 6.6, and the mean for the y data values was 16. A T Test for the slope of the regression line is performed, and the p-value is greater than the level of significance of 0.05. Use the appropriate method to predict the y value when...
Q3. [10 points [Serial Correlation Consider a simple linear regression model with time series data: Suppose the error ut is strictly exogenous. That is Moreover, the error term follows an AR(1) serial correlation model. That where et are uncorrelated, and have a zero mean and constant variance a. 2 points Will the OLS estimator of P be unbiased? Why or why not? b. [3 points Will the conventional estimator of the variance of the OLS estimator be unbiased? Why or...
3) (Total: 12 points) The results of the regression model is shown below. Using the p-value in the table, determine whether there is significant relationship between durability and the two independent variables at the 0.05 level of significance. Variable Intercept FOREIMP (x) MIDSOLD (x2) Coefficients Standard Error t-Stat -0.03501 0.05905 -0.39 0.84774 0.06295 (1) 0.58993 0.07174 8.43 p-Value 0.7034 < 0.0000 < 0.0000 (1) (2 points) Calculate the t-Stat of the FOREIMP (x2). (2) (2 points) Write hypothesese to test...
Run an excel data analysis for regression A) What is the F test and interpret it B) Run a T Test and interpret it C) Use the hypothesis equation to see if you if you accept or reject the hypothesis Sample Data for Supermarket Profits. Supermarket Food Sales Nonfood Sales Store Size Profit Number (tens of thousands (tens of thousands(thousands of (thousands of dollars) square feet) X. 35 of dollars) of dollars) 35 98 83 76 93 20 15 17...
Serial correlation, its implications on the OLS model. What is classical Assumption 4 Error term has constant variance Error term is normally distributed All explanatory variables are un correlated with error term Different observations of error term are uncorrelated each other Relationship between serial correlation and classical assumption 4. Serial violates classical assumption 4 always hold Serial violates support Serial violates is not related Serial violates is used to help when 4 violated Assuming we are using an appropriate test...
5- Interpret the coefficient of determination (R-squared) and the F test. SUMMARY OUTPUT Regression Statistics Multiple R 0.8811 R Square 0.7764 Adjusted R Square 0.7205 Standard Error 14.7724 Observations 16 ANOVA df SS MS F Regression 3 9091.7392 3030.5797 13.8874 Residual 12 2618.7008 218.2251 Total 15 11710.44 Coefficients Standard Error t Stat P-value Intercept 29.1385 174.7427 0.1668 0.8703 PFH -2.1236 0.3405 -6.2361 0.0000 PR 1.0345 0.4667 2.2164 0.0467 M 3.0871 0.9993 3.0892 0.0094
if your p-value for a correlation is p=0.049, and you are running a two tailed test your correlation is significant. t or f
How would you interpret correlation coefficient of: -0.95? A. Not a good correlation measure B. Week negative correlation C. Strong negative correlation D. Not enough information to determine correlation
Looking at the sample provided, how would you interpret the results of the two-way ANOVA? What does the p value tell you? The results mention df. What does that term represent? How is it calculated? Write a plainly stated sentence that explains what these results tell you about your groups. ANOVA Sum of Squares df Mean Square F Sig. SCORES Between Groups 351.520 4 87.880 9.085 .000 Within Groups 435.300 45 9.673 Total 786.820 49
Suppose you conducted a simple linear regression t-test with a null hypothesis that the slope was positive. You obtained a p-value of 0.023. If you were to conduct an F-test that tested for a linear relationship between the one predictor and response what p-value would you expect to obtain?