4.
a. Since the 90 % confidence interval (1.5 , 9.3 ) does not contain 0.,we would expect that the regression coefficient is not equal to zero. Therefore, we tend to reject Ho and Ho is rejected when P-value is less than level of significance (alpha). In this case Alpha = 1-0.90 = 0.10
Hence, we would expect a p-value < alpha (since this is a two tailed test )
So, P-value < 0.10
b.
c.
d.
The main concern in fitting a simple linear regression model is that the plotted points should be clustered around the regression line so that the regression line is line of best fit.
Here, we can see that the points are clustered close to the regression line , hence , we can easily fit a simple linear regression model in this case.
4. Short answer questions. Unless stated otherwise, each part is unrelated In the linear regression model,...
Problem 8.4: Refer to Muscle Mass Problem 1.27. Second-order regression model (8.2) with independent normal error terms is expected to be appropriate. A. Fit regression model (8.2). Plot the fitted regression function and the data. Does the quadratic regression function appear to be a good fit here? Find R^2. B. Test whether or not there is regression relation; use α= .05. State the alternatives, decision rule and conclusion. C. Estimate the mean muscle mass for women aged 48...
For Questions 4-11, use the swiss dataset, which is built into R. Fit a multiple linear regression model with Fertility as the response and the remaining variables as predictors. You should use ?swiss to learn about the background of this dataset. 9. 1 Run Reset Report the value of the F statistic for the significance of regression test. Enter answer here point 10. 1 Run Reset 0.01. What decision do Carry out the significance of regression test using a you...
R is a little difficult for me, please answer if you can interpret the R code, I want to learn better how to interpret the R code 4. each 2 pts] Below is the R output for a simple linear regression model Coefficients: Estimate Std. Error t value Pr(>t) (Intercept) 77.863 4.199 18.544 3.54e-13 3.485 3.386 0.00329* 11.801 Signif. codes: 0 0.0010.010.05 0.11 Residual standard error: 3.597 on 18 degrees of freedom Multiple R-squared: 0.3891, Adjusted R-squared: 0.3552 F-statistic: 11.47...
psy230 flipped assignment 4- linear regression. thank you PSY 230 Flipped Classroom Assignment: Linear Regression GRADED ASSIGNMENT Prompt: After finding an association between openness to experience and interest in statistics, the statistics instructor examined another personality trait: extraversion. Again, he hypothesizes that there would be a positive association between the two variables, such that greater levels of extraversion will be associated with greater levels of interest in statistics. To test this hypothesis, he used the same participants from his course...