Question

QUESTION 17

The process of creating a linear model of bivariate data.

	a.	Least Squares Regression
	b.	Variability
	c.	Extrapolation
	d.	Residual analysis

QUESTION 18

The "Portion of Variability" is also known as the

	a.	Correlation coefficient
	b.	Regression line
	c.	Fitted Value
	d.	Coefficient of determination

QUESTION 19

Linear regression models may not always acccurately reflect the pattern of data from which they are made

	a.	TRUE
	b.	FALSE

QUESTION 20

The following data relates the time a student spends on an online test with the final score: (8 points, 12 minutes), (22 points, 34 minutes), (33 points, 72 minutes), (40 points, 120 minutes). Use your TI83 to determine the linear regression equation, and choose the best response below

	a.	Points = 9(minutes) - 0.28
	b.	Points = 24(minutes) +3.2
	c.	Points = 0.28(minutes) + 9
	d.	Points = -3.2(minutes) +24

Answer 1

Answer 17

The process of creating a linear model of bivariate data.

a.

Least Squares Regression

Least square regression helps us model the bivariate data by helping in establishing & defining a linear relationship between the variables.

Answer 18

The "Portion of Variability" is also known as the

d.	Coefficient of determination

The coefficient of determination tells us about the portion of explained variability.

Answer 19

Linear regression models may not always accurately reflect the pattern of data from which they are made

a.

TRUE

If the variables show a non-linear relationship then linear regression models will not accurately reflect the data from which they are made.

Answer 20

c.	Points = 0.28(minutes) + 9

The independent variable is X, and the dependent variable is Y. In order to compute the regression coefficients, the following table needs to be used:

	X	Y	X*Y	X2	Y2
	12	8	96	144	64
	34	22	748	1156	484
	72	33	2376	5184	1089
	120	40	4800	14400	1600
Sum =	238	103	8020	20884	3237

Based on the above table, the following is calculated:

Therefore, based on the above calculations, the regression coefficients (the slope m, and the y-intercept n) are obtained as follows:

Therefore, we find that the regression equation is:

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