26. The results from a Pearson correlation used to examine the relationship between two variables will be the exact same as the results if computed as a simple regression.O TrueO False27. There is no advantage to using a simple regression analysis over a correlation analysis to examine the relationship between two variables.O TrueO False28. What is the best practice to use when conducting a multiple regression analyses?OA. Enter in all the independent variables (drivers) to the regression analysis you think might help explain your dependent variable (key outcome).OB. Based on previous research, only enter in the independent variables (drivers) you think will be related to the dependent variable (key outcome).OC. Examine all the pairwise (1 independent variable related to the dependent variable) relationships you predict are related to dependent variable first and then only enter those that are significant into the multiple regression.OD. There isn't an advantage to doing a multiple regression over numerous pairwise (1 independent variable related to the dependent variable) comparisons.

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Answered: 26. The results from a Pearson…

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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Q2. Adjusted R2 is the modified version of R after adjusting for the number of predictor variables in a multiple regression model. a. TrueB. FalseC. None of the above
Q1 Collect sufficient data about any two quantitative variables (logically correlated) using reasonable sets of values (At least 20 values). Use Microsoft Excel and answer the following: First: Determine the independent variable and the dependent variable. Second: Make the correlation analysis, determine the correlation coefficient, and interpret it. Third: Conduct the regression analysis and answer the following questions: What is the type of regression in your example? Write the sample size. Find the degrees of freedom. Find the value of the coefficient of determination, and interpret it. Is your model valid? Why? Is the independent variable having a significant effect on the dependent variable? Why? Write down the regression line equation. Estimate the value of ŷ at any value of X.
If the linear correlation between two variables is negative, what can be said about the slope of the regression line? ..... Choose the correct answer below. A. More information is needed B. Negative C. Positive
3. Instead of using R-squared, what is the alternative method of assessing the reliability of the results in the regression model?
10
1. Why is the error term included in the regression model? 2. Why we don't prefer to apply the Spearman correlation on the quantitative variables? 3. Compare between the roles of both the regression coefficient and the correlation coefficient. Can the values of regression coefficient and the correlation coefficient have different signs? Explain. 4. Explain the meaning of independent and dependent variables for a regression model. Explain the difference between a simple and a multiple regression model. 5. What is difference between the normal and the standard normal distributions?
21. Which of the following statements is true regarding the sources of variation present in an analysis of regression? SSy is partitioned into variation explained by the regression model and residual variation. If most of the variability in Y is associated with residual variation, then X predicts Y. There are three sources of variation in an analysis of regression: regression variance, residual variance, and error variance. Regression variation measures variability in X, whereas residual variation measures variability in Y.
5. Explain what the correlation coefficient tells you about the relationship between your variables. 7. Interpret the slope of the regression equation in terms of the variables in your data set.
A researcher is interested in examining the relationship between spousal abuse and child abuse. Specifically, they are interested in determining whether there is a predictive relationship between spousal abuse and child abuse in 5 county social services offices. Calculate the linear regression line for the following data. Note you have already calculated the first step to this analysis (Pearson's Correlation)
1. If we choose the significance level α=0.05, is Gender a significant factor in explaining Salary? Explain. 2. Use Excel’s Data Analysis tool to conduct a multiple regression, using Salary as the dependent variable, and all the rest of variables as independent variables.(Choose confidence level at 95 %.) 3. Based on the multiple regression analysis output, write the estimated regression equation. (Keep 2 decimal places) Salary Gender Years Of Service Years Of Education 52,100 0 5 13 55,015 0 20 13 55,520 0 23 12 43,430 0 7 12 43,813 0 5 12 51,903 0 6 12 54,580 0 24 14 54,913 0 18 12 52,100 0 29 12 50,693 0 14 12 47,878 0 10 12 46,555 0 6 14 47,878 0 7 16 47,878 0 9 12 54,913 0 16 13 50,123 0 4 13 50,693 0 20 12 48,933 0 29 12 58,270 0 12 12 50,538 0 5 13 48,933 0 12 12 45,078 0 5 12 47,878 0 5 14 53,508 0 24 12 42,250 0 1 16 53,508 1 8 15 42,250 1 3 15 47,808 1 1 13 50,180 1 11 13 44,163 1 9 10 41,718 1 3 10 47,878…
write the regression code for the following data based on the below instruction, in STATA. hint: occupation=a categorical variable equal to ”1” if a person’s occupation is ”management”; ”2” if it is ”sales”; ”3” if it is ”clerical”, ”4” if it is ”service”, ”5” if it is ”professional” and ”6” if it is ”other”. Please make sure to use ”other” as the base category;
What is the purpose of testing whether B, = 0? O The purpose of testing whether 8, = 0 is to determine whether or not the mean of the x values is equal to the mean of the y values. O The purpose of testing whether 8, = 0 is to determine whether or not the regression line provides a good fit for the data. O The purpose of testing whether 8, = 0 is to determine whether or not there is a cause-and-effect relationship between x and y. O The purpose of testing whether 8, = 0 is to determine whether or not there is a significant relationship between x and y. If we reject 8, = 0, does it imply a good fit? O Rejecting 8, = 0 does not necessarily imply a good fit. For example, if 8, = 0 is rejected and r2 is high, there is a statistically significant relationship between x and y but the fit is not very good. O Rejecting 8, = 0 always implies a good fit. If 8, = 0 is rejected, there is a statistically significant relationship between x and y which always implies a good fit. O Rejecting 8, = 0…

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