Homework Answers
33. ans-> B) False
34. ans-> A) Yes, we can infer causality.
35. ans-> B) Finding the perfect linear relationship
36. ans-> B) b
37. ans-> C) A Pearson-r near 0.0
38. ans-> A) True
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2 pts. when regression line has no slope, we can still predict Y from X because...
True or false?: 1) If X and Y are standardized, then fit a linear regression line...
True or false?: 1) If X and Y are standardized, then fit a linear regression line of standardized Y on standardized X, correlation between X and Y equals the slope of regression line. 2) If one calculates r for a set of numbers and then adds a constant to each value of one of the variables, the correlation will change. 3) The easiest way to determine if a relationship is linear is to calculate the regression line. 4) If the...
True or false: 1) If X and Y are standardized, then fit a linear regression line...
True or false: 1) If X and Y are standardized, then fit a linear regression line of standardized Y on standardized X, correlation between X and Y equals the slope of regression line. 2) If one calculates r for a set of numbers and then adds a constant to each value of one of the variables, the correlation will change. 3) The easiest way to determine if a relationship is linear is to calculate the regression line. 4) If the...
A regression line can be used to determine the strength of a relationship. determine if there...
Question 6A regression line can be used to determine the strength of a relationship. determine if there is a cause and effect relationship. predict Y for any X value. establish if a relationship is linear. Question 7 If the correlation coefficient R between two variables is ,it is expected that the slope of the regression line will be positive; positive positive; large negative; small positive; negative Question 8 If the slope of the simple regression line is .12, then the Pearson correlation coefficient r is expected to be positive negative small large
If you were to develop a linear regression equation that uses autonomy at work to predict...
If you were to develop a linear regression equation that uses autonomy at work to predict job satisfaction, what would be the Y-Intercept (a) of the regression equation? Use the slope (b) obtained from the previous question to answer this question. Autonomy Job satisfaction (Y) n 8 8 Mean My=4 My 7 Variability SSX-28 SSY - 156 SP-56 Pearson r.85 -10 1 2 pts Question 43 Correlation' is to 'regression' as significance to non significant 'samples are to populations relationship...
5. So far in our linear modeling, we have assumed that Ylz ~ NA,+Az,σ2); that is, there is a normal distribution of common variance around the regression line. Here, we change this up! Suppose that X...
5. So far in our linear modeling, we have assumed that Ylz ~ NA,+Az,σ2); that is, there is a normal distribution of common variance around the regression line. Here, we change this up! Suppose that X~Unif(0, 1) and that for a given r, we know YlN(,22). (Here, the regression lne is 01z and the variance around the regression grows as r grows.) a. In R, figure out how to generate 1000 data points that follow this model and plot them....
There are greater errors in predicting Y from X when A. None of the above. B....
There are greater errors in predicting Y from X when A. None of the above. B. All of the above. C. The slope of the regression line is closer to 0. D. The correlation coefficient is closer to 0. E. There is greater spread of Y scores around the regression line.
5. So far in our linear modeling, we have assumed that Ylx ~ N(Ao +Ax, σ2); that is, there is a normal distribution of common variance around the regression line. Here, we change this up! Suppose tha...
5. So far in our linear modeling, we have assumed that Ylx ~ N(Ao +Ax, σ2); that is, there is a normal distribution of common variance around the regression line. Here, we change this up! Suppose that X~Unif (0, 1) and that for a given a, we know Y~N(x, a2). (Here, the regression line is 0 1r and the variance around the regression grows as a grows.) a. In R, figure out how to generate 1000 data points that follow...
13 The scatter plot for the data set X and Y shows the data points clustered...
13 The scatter plot for the data set X and Y shows the data points clustered in a nearly perfect circle. For these data, what is the most likely value for the Pearson r? a) near 0 b) r near +1 c) r near - 1 d) r between - 5 and 7.5 e) none of the above.
here is the data Y X 34.38 22.06 30.38 19.88 26.13 18.83 31.85 22.09 26.77 17.19...
here is the data
Y X
34.38 22.06
30.38 19.88
26.13 18.83
31.85 22.09
26.77 17.19
29.00 20.72
28.92 18.10
26.30 18.01
29.49 18.69
31.36 18.05
27.07 17.75
31.17 19.96
27.74 17.87
30.01 20.20
29.61 20.65
31.78 20.32
32.93 21.37
30.29 17.31
28.57 23.50
29.80 22.02
this is q1
2. Using a computer program, a data set with 20 observations on and was generated (GENERATEDATA.csv). Again, use the same instructions as above (in #1) to upload and import this dataset...
Question 6A regression line can be used to determine the strength of a relationship. determine if there is a cause and effect relationship. predict Y for any X value. establish if a relationship is linear. Question 7 If the correlation coefficient R between two variables is ,it is expected that the slope of the regression line will be positive; positive positive; large negative; small positive; negative Question 8 If the slope of the simple regression line is .12, then the Pearson correlation coefficient r is expected to be positive negative small large
If you were to develop a linear regression equation that uses autonomy at work to predict job satisfaction, what would be the Y-Intercept (a) of the regression equation? Use the slope (b) obtained from the previous question to answer this question. Autonomy Job satisfaction (Y) n 8 8 Mean My=4 My 7 Variability SSX-28 SSY - 156 SP-56 Pearson r.85 -10 1 2 pts Question 43 Correlation' is to 'regression' as significance to non significant 'samples are to populations relationship...
5. So far in our linear modeling, we have assumed that Ylz ~ NA,+Az,σ2); that is, there is a normal distribution of common variance around the regression line. Here, we change this up! Suppose that X~Unif(0, 1) and that for a given r, we know YlN(,22). (Here, the regression lne is 01z and the variance around the regression grows as r grows.) a. In R, figure out how to generate 1000 data points that follow this model and plot them....
5. So far in our linear modeling, we have assumed that Ylx ~ N(Ao +Ax, σ2); that is, there is a normal distribution of common variance around the regression line. Here, we change this up! Suppose that X~Unif (0, 1) and that for a given a, we know Y~N(x, a2). (Here, the regression line is 0 1r and the variance around the regression grows as a grows.) a. In R, figure out how to generate 1000 data points that follow...
13 The scatter plot for the data set X and Y shows the data points clustered in a nearly perfect circle. For these data, what is the most likely value for the Pearson r? a) near 0 b) r near +1 c) r near - 1 d) r between - 5 and 7.5 e) none of the above.
here is the data
Y X
34.38 22.06
30.38 19.88
26.13 18.83
31.85 22.09
26.77 17.19
29.00 20.72
28.92 18.10
26.30 18.01
29.49 18.69
31.36 18.05
27.07 17.75
31.17 19.96
27.74 17.87
30.01 20.20
29.61 20.65
31.78 20.32
32.93 21.37
30.29 17.31
28.57 23.50
29.80 22.02
this is q1
2. Using a computer program, a data set with 20 observations on and was generated (GENERATEDATA.csv). Again, use the same instructions as above (in #1) to upload and import this dataset...
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