Question 2. 5. 4. 3. 2. 50 3. a. Sketch a scatter plot for the above data. b. Without using SPSS, compute the Pearson correlation. Test whether it is significantly different from zero at a = .05.- c. Find the regression equation (Y'= a + bX) for predicting Y from X. d. Computed the predicted Y' score for (i) X = 1 and (ii) X = 5. . e. Plot the regression line on the scatter plot you sketch in (a). *

Question 2. 5. 4. 3. 2. 50 3. a. Sketch a scatter plot for the above data. b. Without using SPSS, compute the Pearson correlation. Test whether it is significantly different from zero at a = .05.- c. Find the regression equation (Y'= a + bX) for predicting Y from X. d. Computed the predicted Y' score for (i) X = 1 and (ii) X = 5. . e. Plot the regression line on the scatter plot you sketch in (a). *

Functions and Change: A Modeling Approach to College Algebra (MindTap Course List)

6th Edition

ISBN:9781337111348

Author:Bruce Crauder, Benny Evans, Alan Noell

Publisher:Bruce Crauder, Benny Evans, Alan Noell

Chapter5: A Survey Of Other Common Functions

Section5.6: Higher-degree Polynomials And Rational Functions

Problem 1TU: The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t=...

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The following fictitious table shows kryptonite price, in dollar per gram, t years after 2006. t= Years since 2006 0 1 2 3 4 5 6 7 8 9 10 K= Price 56 51 50 55 58 52 45 43 44 48 51 Make a quartic model of these data. Round the regression parameters to two decimal places.
Olympic Pole Vault The graph in Figure 7 indicates that in recent years the winning Olympic men’s pole vault height has fallen below the value predicted by the regression line in Example 2. This might have occurred because when the pole vault was a new event there was much room for improvement in vaulters’ performances, whereas now even the best training can produce only incremental advances. Let’s see whether concentrating on more recent results gives a better predictor of future records. (a) Use the data in Table 2 (page 176) to complete the table of winning pole vault heights shown in the margin. (Note that we are using x=0 to correspond to the year 1972, where this restricted data set begins.) (b) Find the regression line for the data in part ‚(a). (c) Plot the data and the regression line on the same axes. Does the regression line seem to provide a good model for the data? (d) What does the regression line predict as the winning pole vault height for the 2012 Olympics? Compare this predicted value to the actual 2012 winning height of 5.97 m, as described on page 177. Has this new regression line provided a better prediction than the line in Example 2?
Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
Consider the following regression equation specied for 2-period panel data: where i = 1; 2; :::N and t = 1; 2. If you expect that β_1 is positive, but the correlation between Δx_i and Δu_i is negative, thenwhat is the bias in the OLS estimator of β_1 in the first-differenced equation?
2. The following data, adapted from Montgomery, Peck, and Vining (2001), present the number of certified mental defectives per 10,000 of estimated population in the United Kingdom ( y) and the number of radio receiver licenses issued (x) by the BBC (in millions) for the years 1924 through 1937. Fit a regression model relating y and x. Comment on the model. Specifically, does the existence of a strong correlation imply a cause-and-effect relationship?
Question 16: Run a regression analysis on the following bivariate set of data with y as the response variable. x y 48.6 77.8 85.7 25.8 33.9 -37.3 65.2 32.5 95.7 68.7 74.7 100.1 46.5 -34.3 43.8 -12.1 39 -64.1 77.7 36.8 69.1 43.6 Verify that the correlation is significant at an α=0.05α=0.05. If the correlation is indeed significant, predict what value (on average) for the explanatory variable will give you a value of -21.9 on the response variable.What is the predicted explanatory value?x = (Report answer accurate to one decimal place.)
Question 23: Run a regression analysis on the following bivariate set of data with y as the response variable. x y 103.5 90.7 58.3 62.9 90.6 70.6 65.6 63.1 71.3 63.3 72.9 66.5 49.2 48.6 69.7 70.9 94.1 88.4 67.4 60.2 72.4 72.8 72.8 68 Find the correlation coefficient and report it accurate to three decimal places.r = What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place. (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.)r² = %Based on the data, calculate the regression line (each value to three decimal places)y = x + Predict what value (on average) for the response variable will be obtained from a value of 96.4 as the explanatory variable. Use a significance level of α=0.05α=0.05 to assess the strength of the linear correlation.What is the predicted response value? (Report answer accurate to one decimal…
question 21 Run a regression analysis on the following bivariate set of data with y as the response variable. x y 43.8 54.1 41.3 51.2 35.3 60.1 48.1 44.9 42.8 51.8 44.7 50.8 39.4 56.6 38.2 56.6 40.7 53.5 45.3 51.1 45 46.7 41.1 52.2 A. Find the correlation coefficient and report it accurate to three decimal places. r = ________________B. What proportion of the variation in y can be explained by the variation in the values of x? Report answer as a percentage accurate to one decimal place. (If the answer is 0.84471, then it would be 84.5%...you would enter 84.5 without the percent symbol.)r² =_______________ %C. Based on the data, calculate the regression line (each value to three decimal places)y =___________ x + _______________
If a sample of 25 pairs of data yields a correlation coefficient, r, of 0.390 and the scatterplot displays a linear trend, can you use the regression equation to make predictions, assuming your x-values are within the domain of the data set? Choose your answer from the multiple choice answers below A.) Yes, because rcrit = 0.396 and the regression coefficient, r, is less than this value. B.) Yes, because rcrit = 0.381 and the regression coefficient, r, is greater than this value. C.) No, because rcrit = 0.381 and the regression coefficient, r, is greater than this value. D.) No, because rcrit = 0.396 and the regression coefficient, r, is less than this value.
Question 17: You run a regression analysis on a bivariate set of data (n=103n=103). With ¯x=24.3x¯=24.3 and ¯y=75.9y¯=75.9, you obtain the regression equationy=0.659x+59.886y=0.659x+59.886with a correlation coefficient of r=0.883r=0.883. You want to predict what value (on average) for the response variable will be obtained from a value of x=90x=90 as the explanatory variable.What is the predicted response value?y = (Report answer accurate to one decimal place.)
A sample of n = 15 pairs of X and Y scores produces a Pearson correlation of r = 0.45, SSY = 90. a) If the regression equation was found for these scores, how much of the Y variability would be predicted by the regression equation (SSregression) and how much would not be predicted (SSresidual)? b) Does the regression equation predict a significant portion of the variability for the Y scores? (Equivalently, is the Pearson correlation significant?)
Which of the following statements is NOT true? a.If the correlation coefficent, rr, is negative, then the slope of the regression line must always be negative. b.For variables x and y, the value of the correlation coefficent will not change c.if the units of x and y are changed, (i.e., if both x and y are changed from say inches to meters) d.If r≠0r≠0 , then a significant linear correlation will exist between the two variables e.The value of the correlation coefficient must be within the interval: −1≤r≤1