4. Residuals are...the difference between observedand valuesthe model.B. data collected from individuals that is not consistent with the rest of the group.C. none of theseD. possible models not explored by the researcher.E. variation in the data that is explained by the model.5. If the point in the upper right corner of this scatterplot is removed from the data set, thenwhat will happen to the slope of the line of best fit (b) and to the correlation (r)?A. b will increase, and r will decrease.b will decrease, and r will increase.C. both will decrease.D. both will increase.E. both will remain the same.6 An 8th grade class develops a linear model that predicts the number of cheerios (a smallround cereal) that fit on the circumference of a plate by using the diameter in inches. Theirmodel is: # cheerios = 0.56 +5.11(diameter).The slope of this model is best interpreted in context as...A. For every 5.11 inches of diameter, the circumference is about 1 cheerio bigger.B. For every 1 inch of diameter, the circumference holds about 0.56 more cheerios.C. It takes 5.11 cheerios to fill a plate' s circumference.For every 1 inch of diameter, the circumference holds about 5.11 more cheerios.E. A mistake, because л is about 3.14 and that should be the slope.

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Answered: 4. Residuals are... the difference…

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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OA. The scatterplot indicates there is a negative, strong, linear relationship between Median Salary and Annual Sales. OB. The scatterplot indicates there is a negative, weak, linear relationship between Median Salary and Annual Sales. OC. The scatterplot indicates there is a positive, strong, linear relationship between Median Salary and Annual Sales. OD. The scatterplot indicates there is a positive, weak, linear relationship between Median Salary and Annual Sales. OE. The scatterplot indicates there is no relationship between Median Salary and Annual Sales.
5. The National Center for Health Statistics published data on heights and weights. We obtained the following data from 10 randomly selected males 8-12 years of age. * y Height 69 Weight 151 72 154 70 160 200 Weight go IGO Regression Equation: Correlation r = 67 153 140 (20 O 75 201 66 126 a. If the researcher uses "Height" to predict "Weight" then the response variable is weight b. Use your calculator to create a scatter plot. Copy the scatter plot below (draw it). Make sure to label the horizontal axis and vertical axis, and provide the minimum and maximum values (displayed on your graph on your calculator) on each axis.. 70 174 71 185 60 70 Height 80 c. Is the association between the two variables positive or negative, or no association? (circle one) 68 143 g. Identify the slope of the regression line: and mention both men's heights and weights.)" d. Use your calculator to find the regression line (or line of best fit) and the correlation r. (Make sure something has a hat on it.) 65…
1. Correlation analysis is used to determine the equation of the regression line b. a specific value of the dependent variable for a given value of the independent variable the strength of the relationship between the dependent and the independent variables d. a. с. None of these alternatives is correct.
How would you interpret these results? 1. The effect of gender caused different levels of Stroop tas performance at different ages. 2. The effect of age caused different levels of Stroop task performance in men than women. 3. The interaction between gender and age did not affect Stroop task performance. 4. a and b 5. None of the above.
A statistic teacher recorded the Math and Science test scores of twelve students from one section. Using 0.05 as the level of significance, is there a significant relationship between the test scores in Mathematics and Science? Student number Math Scores (X) Science Scores (Y) 1 18 20 2 16 18 3 11 12 4 15 17 5 15 15 6 11 14 7 11 12 8 13 14 9 8 10 10 9 13 11 13 12 12 7 9
Does "correlation imply causation"? Give one example in which a variable X ispositively correlated with another variable Y, but we cannot deduce that X causes Y.
Two separate tests are designed to measure a student's ability to solve problems. Several students are randomly selected to take both tests and the results are shown below. Test A (x) 43 65 73 34 99 78 65 Test B (y) 39 60 62 20 85 70 54 1. Calculate r, the linear correlation coefficient. Identify the values for each part below. Part a.   The value of n   Part b.   The sum of the x-values Part c.   The sum of the y-values Part d.    The sum of the x-squared values   Part e.    The sum of the y-squared values   Part f.    The sum of the (xy) values   Part g.    The final value of r rounded top three decimal places
Q2.      A fast-food chain decided to carry out an experiment to assess the influence of advertising expenditure on sales. Different relative changes in advertising expenditure, compared to the previous year, were made in eight regions of the country, and resulting changes in sales levels were observed the accompanying table shows the results. Increase in advertising expenditure (%) 0 5 15 20 25 30 35 40 Increase in sales (%) 5 10 18 25 35 50 60 65 Determine the value of ∑X, ∑X2, ∑Y, ∑Y2, ∑XY. Where X represent independent variable and Y for dependent variable.                                                                 [2] Determine and interpret the coefficient of correlation between the two variables.     [1] Determine the value of regressions coefficients and write down the simple linear regression
A statistic teacher recorded the Math and Science test scores of twelve students from one section. Using 0.05 as the level of significance, is there a significant relationship between the test scores in Mathematics and Science? Student number Math Scores (X)   Science Scores (Y) 1 18 20 2 16 18 3 11 12 4 15 17 5 15 15 6 11 14 7 11 12 8 13 14 9 8 10 10 9 13 11 13 12 12 7 9 What does the value of r means? a) moderate positive correlation b) perfect positive correlation c) low positive correlation d) very high positive correlation e) moderate negative correlation

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