You are conducting a test of independence for the claim that there is an association between row variable and the column variable. x Y Z A 30 50 2 B 52 46 22 The expected observations for this table would be Y A В The resulting Pearson residuals are: Y А В What is the chi-square test-statistic for this data? x² =

You are conducting a test of independence for the claim that there is an association between row variable and the column variable. x Y Z A 30 50 2 B 52 46 22 The expected observations for this table would be Y A В The resulting Pearson residuals are: Y А В What is the chi-square test-statistic for this data? x² =

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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The following sample contains the scores of 6 students selected at random in Mathematics and English. Use the scores in English as the dependent variable Y. Mathematics score (X) 70 92 80 74 65 83 English score (Y) 74 84 63 87 78 90 ∑x=464, ∑y=476,∑x^2=36354,∑y^2=38254, ∑xy=36926. Estimate the regression parameters and also write the prediction equation.
Consider the following data on y = number of songs stored on an MP3 player and x = number of months the user has owned the MP3 player for a sample of 15 owners of MP3 players. x y 22 485 34 748 2 81 28 581 5 117 32 728 23 445 10 128 4 61 26 476 1 35 8 121 13 266 9 126 5 141 What is the equation of the estimated regression line? (Round your numerical values to four decimal places.) y = Is the simple linear regression model useful for describing the relationship between x and y? Test the relevant hypotheses using a significance level of 0.05. Calculate the test statistic. (Round your answer to two decimal places.) t = find the P-value for this test? (Round your answer to four decimal places.) P-value=
The following data give the family size (2) and number of pets per family (y) for a random sample of 10 families. 4. 2 6. 3. 0. 4. 9. 10 Create a scatterplot for the data. y 10 6. 7- 4 1 2 3 4 5 6 7 8 9 10 11 Clear All Draw: Determine the least squares regression line for the data. Round values to four decimal places, if necessary. Determine the correlation coefficent. Round the solution to four decimal places, if necessary. Use the model to predict the number of pets owned by a family with 13 members. Round the solution to the nearest whole number. 5. 5. 2. 3. 3.
Suppose a doctor measures the height, x, and head circumference, y, of 8 children and obtains the data below. The correlation coefficient is 0.858 and the least squares regression line is y = 0.228x +11.187. Complete parts (a) and (b) below. Height, x 27.5 25.75 26.5 25.5 27.25 26.25 25.75 27.25 27 27.25 27 Head Circumference, y 17.4 17.2 17.2 16.9 17.6 17.1 17.1 17.4 17.4 17.3 17.3 (a) Compute the coefficient of determination, R². R² =% (Round to one decimal place as needed.) (b) Interpret the coefficient of determination and comment on the adequacy of the linear model. Approximately % of the variation in (Round to one decimal place as needed.) is explained by the least-squares regression model. According to the residual plot, the linear model appears to be
Part A of question one asks for the variance (sigma squared), not the equation of regression line. Is the variance 2.79 as I listed on my answer sheet? Maybe my image didn't go through, but I already had the answer to subparts B and C to the question, which you list as #2 and #3. But I need help with parts D, E, and F. I have the following answers for D. d. Y hat (fitted value = 426.46), with residual of -1.62. e. r squared value = 1.00 g. 99% confidence interval = (-11.63, -1.05) Thanks thanks
Answer the Question 1. In multiple regression: a. the ANOVA table tells us whether the three main regression coefficients (R, R-square, adjusted R-square) are significantly different from 1. .50 2. 1.00 3. Zero 4. 1.50 b. the adjusted R-square can be interpreted as 1. Th percentage of variance accounted for in the dependent variable by the set of independent variables 2. The percentage of variance accounted for in the dependent variable by the set of independent variables minus an estimate penalty 3. The percentage of variance accounted for in the dependent variable by a single independent variable 4. The strength of a relationship between the dependent variable and the set of independent variables