Answer:
a = 27
b = 38
c = 19
Step-by-step explanation:
a + b + c = 84
b = 2c
a = 8+c
8+c +2c+c = 84
4c + 8 = 84
4c = 76
c = 19
b = 2(19) = 38
a = 8 + 19 = 27
Answer:
$8.28
Step-by-step explanation:
You need 24 Tablespoons to make 3 of the pies. And each lemon has 2 tablespoons. So you need 12 lemons to make them. Multiply 12 by 0.69 and you get your final answer.
Answer:
122
Step-by-step explanation:
To solve for x, undo the operations done to it.
__
5 = ∛(x +3) . . . . . given
125 = x +3 . . . . . cube both sides of the equation
122 = x . . . . . . . . subtract 3
Depending on your dependent/outcome variable, a negative value for your constant/intercept should not be a cause for concern. This simply means that the expected value on your dependent variable will be less than 0 when all independent/predictor variables are set to 0. For some dependent variables, this would be expected. For example, if the mean value of your dependent variable is negative, it would be no surprise whatsoever that the constant is negative; in fact, if you got a positive value for the constant in this situation, it might be cause for concern (depending on your independent variables).Even if your dependent variable is typically/always positive (i.e., has a positive mean value), it wouldn't necessarily be surprising to have a negative constant. For example, consider an independent variable that has a strongly positive relationship to a dependent variable. The values of the dependent variable are positive and have a range from 1-5, and the values of the independent variable are positive and have a range from 100-110. In this case, it would not be surprising if the regression line crossed the x-axis somewhere between x=0 and x=100 (i.e., from the first quadrant to the fourth quadrant), which would result in a negative value for the constant.The bottom line is that you need to have a good sense of your model and the variables within it, and a negative value on the constant should not generally be a cause for concern. Typically, it is the overall relationships between the variables that will be of the most importance in a linear regression model, not the value of the constant.
15.9876 you might think I'm wrong but if you divide you can get the same result as your true answer which is 15.7075. hope I helped!:3
