If I want to estimate the regression of a model by using OLS on Eveiws , and I chose the "keep it as general as possible" approach, what tests can I apply through the estimation and inference process to validate the model and the variables?
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If I want to estimate the regression of a model by using OLS on Eveiws , and I chose the "keep it as general as possible" approach, what tests can I apply through the estimation and inference process to validate the model and the variables?
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- Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?It is required to use the data given in the table to estimate the parameters of the simple linear regression equation by any of the estimation methods:You spilled water on your calculations from (a) and can't remember what your estimated regression parameters are. But you do have two possible estimated errors for each of your initial four observations:
- The estimated regression equation for a model involving two independent variables and 10 observations follows.Find the multiple regression equation with weight as the response variable and the dummy variable of sex and the variable of age as the explanatory variables.According to World Health Organization (WHO), the recommended limit for a noise level inside a classroom is 35 dBA. However, nine out of ten schools fail to meet this recommendation. A researcher wishes to conduct a study relevant to the prior information, but as a gap, he decides to include the area (in square meters) of every classroom and how it could possibly affect the resulting noise level. He selects 17 classrooms at random, and the noise levels are recorded in the next slide. a. Find the regression equation and construct the scatter plot diagram. b. Predict the noise level if a classroom has an area of 85.97 m2 . c. Calculate the coefficient of determination and interpret the findings. d. Calculate the coefficient of alienation and interpret the findings. Use Excel
- I've had to ask this question multiple times, to which a few had either been copied from the previous answer or from the internet so please, if you can produce a direct and concise answer it would be much appreciated. I have recently produced a linear regression model in R, to which everything seemed fine with the code and model, but my standard residual plots appear to be non-linear. Why is it that my qq plot and initial plot seem linear but the residual contradicts? Furthermore what does this mean in context to the topic? The topic is on the dependency of strength on body weight. Also, why is it that my r-squared value is high and my Shapiro test fails to reject the null hypothesis but my residual plot is non-linear The data I used was of a linear trend as well, as shown by the initial scatter plot. Context in the answer as well as why this has happened is vital in my understanding so sincerely please do your best I can’t attach more than 2 images so I will attach my dataset and…Would I use the regression line to predict Y from X ? And what is the pattern of the scatterplot?IS the following statment true or false, please explain why For each x term in the multiple regression equation, the corresponding β is referred to as a partial regression coefficient.
- What is the difference between fitting longitudinal body weight with a non-linear model such as Gompertz and fitting this longitudinal body weight with random regression models? Please state their assumptions.What type of standard errors would you use to estimate regression? Does your inference about the parameters change if you use different standard errors?** had to resubmit this question because the first time the data was duplicated and reflected incorrectly. The table below gives the number of hours spent unsupervised each day as well as the overall grade averages for seven randomly selected middle school students. Using this data, consider the equation of the regression line, yˆ=b0+b1xy^=b0+b1x, for predicting the overall grade average for a middle school student based on the number of hours spent unsupervised each day. Keep in mind, the correlation coefficient may or may not be statistically significant for the data given. Remember, in practice, it would not be appropriate to use the regression line to make a prediction if the correlation coefficient is not statistically significant. Hours Unsupervised 1 2 3 4 5 5.5 6 Overall Grades 96 89 87 77 76 68 64 **Please circle the answer for each step so I don't get confused. Thanks in advance for helping me with the breakdown and notes** Step 1 of 6 : Find the…
