### Using the Following Information#### Coefficients Table- **Intercept:** -12.8094 - **Independent Variable:** 2.1794 #### ANOVA Table| | df | SS | MS | F ||----------------|----|----------|----------|---------|| **Regression** | 1 | 12,323.90 | 12,323.90 | 90.0481 || **Residual** | 8 | 1,604.502 | 136.8550 | || **Total** | 9 | 13,928.4 | | |#### QuestionWhat is the correlation coefficient? ### Educational Multiple Choice Question**Please select the correct answer from the options below:**- **Option A:** 0.9406- **Option B:** -0.9406- **Option C:** 0.8827- **Option D:** 0.8848*Note: This is a standard multiple-choice question designed to test your understanding of numerical values. Please choose the option that best answers the question provided in your study material.*

The correlation Coefficient is denoted by r. Formula : r = √SS_Regression/SST

Answered: Independent variable 2.1794 ANOVA df S…

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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show how to do it on a TI-84 please and thank you From the data, find the least-squares regression line. What type of correlation is there? Study Hours Test Score 0 20 1 35 2 50 4 47.5 4 65 5 72.5 5 75 6 67.5 6 92.5 7 85 7 90 8 95 5 70 y=9.051+24.765x.y=9.051+24.765x. There is positive linear correlation. y=24.765−9.051x.y=24.765-9.051x. There is negative linear correlation. y=24.765+9.051x.y=24.765+9.051x. There is positive linear correlation. y=−24.765+9.051x.y=-24.765+9.051x. There is negative linear correlation y=9.051−24.765x.y=9.051-24.765x. There is negative linear correlation.
**Please complete table**
ed weight (in kg) and total serum cholesterol (TSC in mg/dl) data for five men (n = 5). They tested for association between weight and TSC at 5% level of significance. weight, X TSC, Y X*X Y*Y X*Y 37 2.9 1369 8.41 107.3 50 0.7 2500 0.49 35.0 67 3.3 4489 10.89 221.1 70 4.2 4900 17.64 294.0 57 3.8 3249 14.44 216.6 SUM: 281 14.9 16507 51.87 874.0 What are the Null and Alternate hypotheses for this test? Clearly label your answers as HỌ or H1. (Do not use a numerical format. Both of your statements must be in sentence form, and say something about the weight
Test the significance at 0.05 level of the computed values of correlation r= 0.65 with a sample size of 10
Dex Research Limited conducted a research to investigate consumer characteristics that can be used to predict the amount charged by credit card users. The following multiple regression output is based on a data collected by this research company on annual income, household size and annual credit card charges for a sample if 50 consumers. Regression Statistics Multiple R R Square Adjusted R Square Standard Error 0.9086 A 0.8181 398.0910 Observations ANOVA df SS MS Significance F Regression 2 1.50876E-18 Residual C 7448393.148 F Total 49 42699148.82 Coefficients Standard Error t Stat P-value Intercept 1304.9048 197.6548 6.6019 3.28664E-08 Income ($1000s) 33.1330 3.9679 H 7.68206E-11 Household Size 356.2959 33.2009 10.7315 3.12342E-14 a. Complete the missing entries from A to H in this output b. Estimate the annual credit card charges for a three-person household with an annual income of $40,000. C. Did the estimated regression equation provide a good fit to the data? Explain
linear refression
Analysis of Variance Source DF SS MS Regression 1 Residual Error 13 0.2364 Total 14 11.3240 What is the value for MSR (Mean Square for Regression)?
Refer to the ANOVA table for this regression. Source d.f. SS MS Regression 2 578,850 289,425 Error 21 378,420 18,020 Total 23 957,270 Using Appendix F on the pictures, calculate F.05, a=0.05, F statistics, R2, and R2 adj.
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 2020 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 1 of 2: How many independent variables are included in the regression model
Ques = Homework: Ch 8 - Sampling Distributions Critical values of the correlation coefficient ars. Com - X Int. Critical Values for Correlation Coefficient Data table 3 0,997 4 0.950 5 0.878 6 0.811 7 0.754 Year Rate of Return of Company Rate of Return of Company ble. Chod 6 2 2007 0.203 0.402 8 0.707 9 0.666 10 0.632 2008 0.310 0.510 2009 0.267 0.410 11 0.602 12 0.576 13 0.553 14 0.532 15 0.514 16 0.497 2010 0.195 0.426 2011 -0.101 -0.060 2012 - 0.130 -0.151 2013 -0.234 -0.357 2014 0.264 0.328 17 0.482 18 0.468 19 0.456 20 0.444 21 0.433 2015 0.090 0.207 2016 0.030 -0.014 2017 0.128 0.113 2018 - 0.035 0.027 22 0.423 23 0.413 24 0.404 25 0.396 Print Done 26 0.388 27 0.381 Tne correlauon coeicient is- (Round to three decimal places as needed.) Print Done
A regression analysis was performed and the summary output is shown below. Regression Statistics Multiple R 0.7802268560.780226856 R Square 0.6087539470.608753947 Adjusted R Square 0.5870180550.587018055 Standard Error 6.7217061336.721706133 Observations 20 ANOVA dfdf SSSS MSMS F� Significance F� Regression 11 1265.3871265.387 1265.3871265.387 28.006928.0069 4.9549E-054.9549E-05 Residual 1818 813.264813.264 45.18145.181 Total 1919 2078.6512078.651 Step 2 of 2: Which measure is appropriate for determining the proportion of variation in the dependent variable explained by the set of independent variable(s) in this model?
Shown below is a portion of a computer output for regression analysis relating y (dependent variable) and x (independent variable). ANOVA df SS Regression 1 24.061 Residual 10 67.979 Coefficients Standard Error Intercept 11.064 2.049 x −0.566 0.301 (a) What has been the sample size for the above regression analysis? (b) Perform a t-test and determine whether or not x and y are related. Let ? = 0.05. State the null and alternative hypotheses. (Enter != for ≠ as needed.) H0: Ha: Find the value of the test statistic. (Round your answer to three decimal places.) Find the p-value. (Round your answer to four decimal places.) p-value = What is your conclusion? .

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Independent variable 2.1794 ANOVA df S MS F Regression 1 12,323.90 12,323.90 90.0481 Residual 1,604.502 136.8550 Total 13,928.4 What is the correlation coefficient?