Suppose you are testing the null hypothesis that the slope of the regression line is zero versus thealternative hypothesis that the slope is different than zero. Would a very small P-value (i.e., lessthan 0.0001) indicate a strong relationship between the explanatory variable and the responsevariable?Yes, because the P-value will give the strength of the association between the explanatory variable and theresponse variable.Yes, because if the P-value is small, then r2 is large.No, because there could be a lot of scatter about the regression line, indicating a weak relationship betweenthe explanatory variable and the response variable.No, because a large P-value would indicate a strong relationship between the explanatory variable and theresponse variable.

State the hypotheses as follows:Null Hypothesis: there is no significant relationship between the…

Answered: Suppose you are testing the null…

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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a. Compute the least-square regression line for predicting sales from temperature. b. Test for a significant linear relationship between the two variables coffee sales (?) and temperature (?) by doing a hypothesis test regarding the population slope ?1. Include the null and alternative hypothesis and the conclusion of the test. Use the critical value approach, with a 0.05 level of significance. Use your table of statistical values for the relevant information. Write your conclusion and interpret it.
4. Suppose you are interested in estimating the ceteris paribus relationship between final exam scores (y) and class attendance (x1). You can also collect data on two control variables, x2 and x3, which could be GPA and SAT scores. Let B1 be the bivariate regression estimate on x1 only and B1 represent the estimate from the full regression with all controls. (a) If class attendance is highly correlated with both controls, and both controls have a large effect on y, would you expect the two estimates to be similar or very different? Why? (b) If class attendance is uncorrelated with the two controls, but the two controls are highly correlated with each other, will the two estimates tend to be similar or different? Why? (c) If class attendance is highly correlated with the two controls, and the two controls have very small effects on y, do we expect the two estimates to be similar or different on average? Why?
The defending attorney Mr. Justin Case was interested in how a lengthy trial could affect howlong a jury would deliberate on a case (and see if he should just cut to the chase). He observed asample of courtroom trials and noticed the following:Days inTrial (X) 5 2 6 4 5 6 2 4 2 1 HoursDeliberation (Y) 4 4 1 3 1 3 9 2 3 7 3) What is the full regression equation?
5) The following results are from data where the dependent variable is SALARY, the independent variables are AGE, EDUCATION, and FEMALE which is a dummy variable = 1 for females and = 0 for males. e) What is the null and alternative hypothesis that Excel is testing in reporting the t-stats above? f) Is the regression equation as a whole significant at the 1% level of significance? How do you know? g) What is the effect on the income of a person being a female versus male? h) Is there a significant amount of evidence (say at the 10% level of significance) that an additional year of education increases income by more than $2000? Be sure to state your null and alternative hypotheses, calculate your test statistic, and find the critical value.
can someone produce SPSS results by including the the intercept (a.k.a. the constant), standard error of estimate, unstandardized regression coefficient, the p-value for the unstandardized coefficient, R-square, Beta (i.e. standardized regression coefficient) using Excel with this given data? Overall: 3.4, 2.9, 2.6, 3.8, 3.0, 2.5, 3.9, 4.3, 3.8, 3.4, 2.8, 2.9, 4.1, 2.7, 3.9, 4.1, 4.2, 3.1, 4.1, 3.6, 4.3, 4.0, 2.1, 3.8, 2.7, 4.4, 3.1, 3.6, 3.9, 2.9, 3.7, 2.8, 3.3, 3.7, 4.2, 2.9, 3.9, 3.5, 3.8, 4.0, 3.1, 4.2, 2.9, 3.9, 3.5, 3.8, 4.0, 3.1, 4.2, 3.0, 4.8, 3.0, 4.4, 4.4, 3.4, 4.0, 3.5 Teach: 3.8, 2.8, 2.2, 3.5, 3.2, 2.7, 4.1, 4.2, 3.7, 3.7, 3.3, 3.3, 4.1, 3.1, 2.9, 4.5, 4.3, 3.7, 4.2, 4.0, 3.7, 4.0 2.9, 4.0, 3.3, 4.4, 3.4, 3.8, 3.7, 3.1, 3.8, 3.2, 3.5, 3.8, 4.4, 3.7, 4.00, 3.4, 3.2, 3.8, 3.7, 4.3, 3.4, 4.0 3.1, 4.5, 4.8, 3.4, 4.2, 3.4 Exam: 3.8 3.2, 1.9, 3.5, 2.8, 3.8, 3.8, 4.1, 3.6, 3.6, 3.5, 3.3, 3.6, 3.8, 3.8, 4.2, 4.1, 4.0, 4.3, 4.2, 4.0, 4.1, 2.7, 4.4, 4.4, 4.3, 3.6, 4.1, 4.2, 3.6,…
3. In some data sets, there are values that are far from the linear regression line. What are the data points called?
If the slope of the taru regrets is zero, then the slope of the sample regrets is two different from zero.
The conditioned and unconditioned samples represent measurements taken from the same group of people at Conduct a paired t-test. different times or under different circumstances (e.g., before and after some conditioning program). The conditioned and unconditioned samples are made up of different v Choose... groups of people (e.g., athletes and non- Conduct an unpaired t-test. athletes). Conduct a regression analysis. See if the standard errors overlap. Conduct a paired t-test.
You estimated a single regression relating the annual bonus given to employees of big corporations to the age of those employees. Your simple regression shows a positive value for the slope coefficient. You are absolutely convinced that the true effect of age on bonus amount is negative, so you think you have an omitted variable bias. If you are correct, which of the following is true about the omitted variable? There is some (omitted) variable that positively affects bonus amount and that is positively correlated with age. There is some (omitted) variable that negatively affects bonus amount and that is completely uncorrelated with age. There is some (omitted) variable that positively affects bonus amount and that is negatively correlated with age. There is some (omitted) variable that positively affects bonus amount and that is completely uncorrelated with age.
7. True or False? The r2 value and a least squares regression line can be an excellent way to demonstrate the degree of correlation between two variables, and the type of association between the two variables.
10) The following results are from a regression where the dependent variable is COST OF COLLEGE and the independent variables are TYPE OF SCHOOL which is a dummy variable = 0 for public schools and = 1 for private schools, FIRST QUARTILE SAT which is the average score of students in the top quartile of SAT’s, THIRD QUARTILE SAT which is the average score of students in the 3rd quartile, and ROOM AND BOARD which is the cost of room and board at the school. The first set of results includes all the independent variables whereas the second set of results excludes the THIRD QUARTILE SAT variable. a) Based on these two sets of data, does there appear that multicollinearity is a problem (specifically, does it appear that THIRD QUARTILE SAT is highly collinear with the other independent variables? Explain. b) Calculate the VIF for THIRD QUARTILE SAT. c) Based on the VIF, do you think that multicollinearity is a problem? Explain.
tion of about $1 million in payroll. (E) One extra win corresponds to an addi- tion of about $17 million in payroll. 6. The residual for the least-squares regression line y = 3.422 +5.551x at the data point (9.227, y) is equal to 1.811. Which of the following is the value of y? (A) 56.452 (B) 54.641 (C) 52.830 (D) 11.038 (E) 7.416 ere to search

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