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?

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?

Elementary Linear Algebra (MindTap Course List)

8th Edition

ISBN:9781305658004

Author:Ron Larson

Publisher:Ron Larson

Chapter5: Inner Product Spaces

Section5.CR: Review Exercises

Problem 62CR

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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?
Find the equation of the regression line for the following data set. x 1 2 3 y 0 3 4
when a regression is used as a method of predicting dependent variables from one or more independent variables. How are the independent variables different from each other yet related to the dependent variable?
Assume that you want to predict Company Performance based on the linear effects of Trust and Commitment that exist among a company’s employees c) If you want to test whether there is a linear effect of Commitment and a curvilinear effect of Trust on Company Performance—such that Company Performance increases up to a certain level of Trust, after which Company Performance diminishes with increasing Trust levels—how would your regression equation look like? Further, explain your answer in your own words. Provide at least two sentences of explanation, in addition to giving the regression equation.
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please use this situation: A small theater company has a linear regression model to estimate y = the concession stand sales in dollars, based on knowing x = the number of people in attendance. The regression equation is: = 6.72x + 11.50 and the correlation coefficient was r = 0.781. The data set saw the number of people in attendance ranging from a minimum of 18 people to a maximum of 170 people. 1) How reliable would it be to make a prediction for the concession sales amount if there were 500 people in attendance? Explain.
The average number of passing yards per attempt for the New York Jets during the 2011 season was 6.5, and the team’s number of interceptions thrown per attempt was 0.033. Use the estimated regression equation developed in part (c) to predict the percentage of games won by the New York Jets during the 2011 season. (Note: For the 2011 the 2011 season, the New York Jets' record was 7 wins and 9 loses.
Let's consider a situation where a company wants to understand the relationship between its advertising expenditure and its sales revenue. This is a typical scenario where simple linear regression can be applied. The company hypothesizes that as advertising expenditure increases, sales revenue also increases. To test this hypothesis, the company can collect data on advertising expenditure and sales revenue and perform a simple linear regression analysis. Match the explanatory and response variables with the appropriate information from this description.
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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.