The Linear Regression Equation. ... The equation has the form Y=a+bX, where Y is the dependent variable (that's the variable that goes on the Y axis), X is the independent variable (i.e. it is plotted on the X axis), b is the slope of the line and a is the y-intercept. (Sharpe, 2017)
The regression equation Y=a+bX. Where Y is (weeks), and X is (number of defective flashdrives). Slope is defined as the (intercept) on a Summary output. Y-intercept is in weeks under the coefficient title on the summary output. The equation will look like this with number to give the regression equation: Defective Flashdrive = 6.299+.047 (1)
Works Cited
Sharpe, D. (2017). Business Statistics. Person Education Inc.
a correlation of .303 shows a weak correlation. The correlation of determination R2 of is .092. . It is the total variation of defective flashdrives explained by the variation in weeks. Due to the poor linear relationship, only 9.2% of the variation in the defective flashdrives can be explained by the variation in weeks (Sharpe, 2015).
0.303 0.092 0.059 1.335 30 ANOVA Regression Residual Total Intercept Week Figure 2. df 1.000 28.000 29.000 Coefficient s 6.299 0.047 SS 5.047 49.920 54.967 Standard Error 0.500 0.028 MS 5.047 1.783 t Stat 12.597 1.682 F 2.831 P-value 0.000 0.104 Significanc eF 0.104 Lower 95% 5.275 -0.010 Flashdrives= 6.299+.047(week) (e) Report the values of the intercept coefficient and the slope coefficient. Interpret what these values indicate. Intercept coefficient= 6.299 Upper 95% 7.323 0.105 Slope coefficient=.047 The intercept value indicates the number of defective flash drives if the time frame were to be week 0 (Sharpe, 2015). For each week, the number of defective flash drives increases by the slope of .047. (e) Use the regression output in part d, and report the regression equation. Regression equationFlashdrives= 6.299+.047(week) (f) Use the regression output in part d, and report R2 value, interpret what this value indicates. R2=.091 R2 is known as the coefficient of determination. It is the total variation of defective flashdrives explained by the variation in weeks. Due to the poor linear relationship, only 9.2% of the variation in the defective flashdrives can be explained by the variation in weeks (Sharpe, 2015). (g) Please note that in a simple regression with one independent variable, the square of the correlation coefficient equals the R2 value.