The “least square regression model” is based on the “best fit” line to the data. This...
- The “least square regression model” is based on the “best fit” line to the data. This will determine a line equation for LINEAR data that will minimize “residual” values (difference between actual and “predicted” )
True or False
- Correlation tells us if there is a relationship between two numeric variables and how strong that relationship is:
True or False
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The “least square regression model” is based on the “best fit”
line to the data. This...
Help with some data science questions Q.1 The linear regression model assumes multivariate normality, no or...
Help with some data science questions Q.1 The linear regression model assumes multivariate normality, no or little multicollinearity, no auto-correlation, and homoscedasticity? Which assumption is missing from this list? (no more than 10 words) Q.2 The coefficient of correlation measures the percent change in the feature variables explained by the target variables. a) True b) False Q.3 In a linear regression model, the coefficient measures the change in Y explained by one unit-change in X. a) True b) False Q4....
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...
Make predictions using a line of best fit Question The table shows data collected on the...
Make predictions using a line of best fit Question The table shows data collected on the relationship between the average number of minutes spent exercising per day and math test scores. The line of best fit for the data is 9 = 0.44x + 65.2. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Minutes Test Score 25 30 35 40 (a) According to the line of best fit, what would...
Which of the following statements regarding regression and correlation are true? (There may be more than...
Which of the following statements regarding regression and correlation are true? (There may be more than one correct answer.) a. If the linear correlation between two variables is 0, then there is no relationship between the two variables. b. When the slope of a linear regression equation is near 0, then the linear correlation between the two variables must also be near 0. c. The average error between the actual values and the predicted values of a least squares line...
The table shows data collected on the relationship between the average daily temperature and coffee sales (in hundreds of dollars) at a coffee shop. The line of best fit for the data is -0...
The table shows data collected on the relationship between the average daily temperature and coffee sales (in hundreds of dollars) at a coffee shop. The line of best fit for the data is -0.68z +85.1. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Temperature (Degrees) Coffee Sales (in hundreds of dollars) 30 40 50 60 65 58 50 45 According to the line of best fit, what would be the...
e Pas 2. If SSE is near zero in a regression, conclusion will be that the...
e Pas 2. If SSE is near zero in a regression, conclusion will be that the proposed model probably has a. too poor a fit to be useful b. near perfect fit c. a very strong positive linear relation d. a very strong negative linear relation 3. A residual/error shows the difference between Yactual and Yestimated on a standard scale. True False 4. In a simple regression, the F statistic is calculated by taking the ratio of MSR to the...
Name: Instructor Date: Section 6. The regression line equation for a set of data is given...
Name: Instructor Date: Section 6. The regression line equation for a set of data is given by y-23x+. -10. The value of for this same data set is 10.1. Use α .05 a. If the lincar correlation coefficient for this data is -521, what is the best y -value for x 57 b. If the linear correlation coefficient for this data is r 972, what is the best predicted y-value for x=5? size n 15 with a 01 7. What...
Which of the following statements regarding regression and correlation are true? (There may be more than...
Which of the following statements regarding regression and correlation are true? (There may be more than one correct answer.) a. A value of the linear correlation, r, near -1 means the data is tightly bundled around a line, and predictions within the scope of data are very reliable. b. When the slope of a linear regression equation is near 0, then the linear correlation between the two variables must also be near 0. c. The average error between the actual...
Make predictions using a line of best fit Question The table shows data collected on the relationship between the average number of minutes spent exercising per day and math test scores. The line of best fit for the data is 9 = 0.44x + 65.2. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Minutes Test Score 25 30 35 40 (a) According to the line of best fit, what would...
The table shows data collected on the relationship between the average daily temperature and coffee sales (in hundreds of dollars) at a coffee shop. The line of best fit for the data is -0.68z +85.1. Assume the line of best fit is significant and there is a strong linear relationship between the variables. Temperature (Degrees) Coffee Sales (in hundreds of dollars) 30 40 50 60 65 58 50 45 According to the line of best fit, what would be the...
e Pas 2. If SSE is near zero in a regression, conclusion will be that the proposed model probably has a. too poor a fit to be useful b. near perfect fit c. a very strong positive linear relation d. a very strong negative linear relation 3. A residual/error shows the difference between Yactual and Yestimated on a standard scale. True False 4. In a simple regression, the F statistic is calculated by taking the ratio of MSR to the...
Name: Instructor Date: Section 6. The regression line equation for a set of data is given by y-23x+. -10. The value of for this same data set is 10.1. Use α .05 a. If the lincar correlation coefficient for this data is -521, what is the best y -value for x 57 b. If the linear correlation coefficient for this data is r 972, what is the best predicted y-value for x=5? size n 15 with a 01 7. What...
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