Can you explain how I can find the variance for the data using the formula provided? I don’t understand what the numbers for E and X are. ✓2✓37.50 75.37 12.00 28.3713.88 26.69 14.75 46.1210.87 40.25 60.50 28.0028.25 6.94 45.12Send data to Excel Example(n-1)s²Xrightd. f. = n-1<25(n-1)s²2XleftFind the variance for the data. Use the formula shown below or your calculator.n(EX²)-(EX)²n(n-1)

7.50,75.37,12.00,28.37,13.88,26.69,14.75,46.12,10.87,40.25,60.50,28.00,28.25,6.94,45.12sample…

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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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I’m taking a statistics and probability class. Please get this correct because I want to learn. I have gotten wrong answers on here before
You may need to use the appropriate technology to answer this question. A regression analysis involving 45 observations relating a dependent variable and two independent variables resulted in the following information. ý = 0.406 + 1.3385X + 2X2 The SSE for the above model is 43. When two other independent variables were added to the model, the following information was provided. ŷ = 1.9 - 3x₁ + 12x₂ + 4x3 + 8x4 This model's SSE is 36. At a 0.05 level of significance, test to determine if the two added independent variables contribute significantly to the model. State the relevant null and alternative hypotheses. O Ho: One or more of the parameters is not equal to zero. H₂: B₁ = B₂= B3 = P4 = 0 OH: One or more of the parameters is not equal to zero. H₂: B3 =B₁ = 0 O Ho: B3 =B4 = 0 H₂: None of the parameters are equal to zero. H₁: B3 =B4 = 0 H₂: One or more of the parameters is not equal to zero. O Ho: B₁ = B₂= B3 =B4 = 0 H₂: One or more of the parameters is not equal to zero. ✔ Find the…
Use the value of the linear correlation coefficient to calculate the coefficient of determination. What does this tell you about the explained variation of the data about the regression line? About the unexplained variation? r = -0.338 Calculate the coefficient of determination. (Round to three decimal places as needed.) % of the variation can be explained by the regression line (Round to one decimal place as needed.) % of the variation is unexplained and is due to other factors or to sampling error. (Round to one decimal place as needed.)
For variables X and Y we have that the correlation between them is 0.9. Also the standard deviation of Y is 8, while the standard deviation of X is 2. What is the slope of the regression equation that attempts to predict Y from X? Use two decimal places in your answer. Your Answer:
Using your favorite statistics software package, you generate a scatter plot with a regression equation and correlation coefficient. The regression equation is reported asy=−71.07x+16.92y=-71.07x+16.92and the r=−0.52r=-0.52.What percentage of the variation in y can be explained by the variation in the values of x?r² = % (Report exact answer, and do not enter the % sign)
For a MR model with 4 predictors, we have: SSE 288 and SST = 957 What percentage of the variation in Y is accounted for by its assumed relationship with the predictors?
Why is the coefficient of variation important? What do we mean when we say that the coefficient of variation has no units? What advantage can there be in having no units? Why is relative size important?
5. For the following set of data: Y 1 10 5 4 4 13 a. Find the regression equation for predicting Y from X. b. Does the regression equation account for a significant portion of the variance in the Y scores? Use a = .05 to evaluate the F-ratio %3D X27 33
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.

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