3. Add the variable abil (a measure of cognitive ability) to the regression frompart (2), and report the results in equation form. Does ability help to explainvariations in education, even after controlling for parents' education? Explain.4. Now estimate the equation where abil appears in quadratic form:educ Bo + Bimotheduc + B₂ fathedu+Bsabil +Babil² + uAssuming all else fixed, find the value of abil where educ is minimized (noticethat abil is measured so that negative values are permissible).5. Argue that only a small fraction of men in the sample have "ability" less thanthe value calculated in part (4). Why is this important?6. Using the estimated equation in (4), test the null hypothesis that educ is linearlyrelated to abil against the alternative that the relationship is quadratic.7. Add the two college tuition variables to the regression from part (4) and deter-mine whether they are jointly statistically significant.8. What is the correlation between tuit 17 and tuit18? Explain why using theaverage of the tuition over the two years might be preferred to adding eachseparately. What happens when you do use the average?9. Do the findings for the average tuition variable in part (8) make sense wheninterpreted causally? What might be going on?Problem 51. Explain what it means for an estimator to be consistent. How is it differentfrom the property of unbiasedness?2. Give an example of an estimator that is consistent but biased.

An estimator is a statistical method or formula used to estimate an unknown parameter or feature of…

Answered: Problem 5 1. Explain what it means for…

MATLAB: An Introduction with Applications

6th Edition

ISBN:9781119256830

Author:Amos Gilat

Publisher:Amos Gilat

Chapter1: Starting With Matlab

Section: Chapter Questions

Problem 1P

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If r = +0.2 for 'Age' versus 'Hours spent on social media', what is your conclusion? There is a weak/no covariance between the two variables There is a moderate covariance between the two variables There is a moderate correlation between the two variables There is a weak/no correlation between the two variables
Would someone familiar with SPSS be able to help with sub-question d please?
B. What is the best predicted temperature for a time when a bug is chirping at the rate of 3000 chirps per minute? The best predicted temperature when a bug is chirping at 3000 chirps per minute is____°F. C. What is wrong with this predicted value? The first variable should have been the dependent variable. It is unrealistically high. The value 3000 is far outside of the range of observed values. It is only an approximation. An unrounded value would be considered accurate. Nothing is wrong with this value. It can be treated as an accurate prediction.
I need help with section t, o and m. Im having difficulties understanding them.
You have the following two data points for a process undergoing learning: Y3 = 50 and Y7 = 35 What is the best equation to use for prediction? Group of answer choices Yx = 84.2 x-0.2324 Yx = 77.6 x-0.5323 Yx = 79.4 x-0.4210 Yx = 80.7 x-0.3365 insufficient information
Suppose a two-way Chi-square test is used to evaluate the relationship between two variables. If both variables have 4 categories or 'items', the number of degrees of freedom (df) for the Chi-square test would be:A. 8B. 7C. 4D. 9 In a two-way Chi-square situation, you are trying to determine if there is a significant relationship between two variables that represent the nominal and/or ordinal scale of measurement.A. TrueB. False What is stated by the null hypothesis in a two-way Chi-square test? A. The two variables have a curvilinear relationshipB. The two variables have very different frequency distributionsC. There is a significant relationship between the two variablesD. There is no significant relationship between the two variables
The results obtained above were primarily due to the mean for the third treatment being noticeably different from the other two sample means. For the following data, the scores are the same as above except that the difference between treatments was reduced by moving the third treatment closer to the other two samples. In particular, 3 points have been subtracted from each score in the third sample. Before you begin the calculation, predict how the changes in the data should influence the outcome of the analysis. That is, how will the F-ratio for these data compare with the F-ratio from above? Treatment A Treatment B Treatment C 1 4 3 2 1 1 3 2 7 2 3 4 F-ratio = p-value = Conclusion: O These data do not provide evidence of a difference between the treatments O There is a significant difference between treatments
What is the answer for the last question regarding the term to describe the data value that doesn't make sense? "Obviously the answer above makes no sense, and this is (in part) because no team in our data had a BA anywhere near .000. This is a textbook illustration of what term from the course? "
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jake suspects that individuals with an optimistic world view live longer than individuals with a pessimistic view. Sate the research hypothesis the null hypothesis cases independent variable dependent variable
On average, a sample of n = 36 scores will provide a better estimate of the population mean than a sample of n = 49 scores from the same population.
The time data is therefore the (Chebyshev's Rule, Empirical Rule) says we should expect of the time data to be minutes.

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Problem 5 1. Explain what it means for an estimator to be consistent. How is it different from the property of unbiasedness? 2. Give an example of an estimator that is consistent but biased.