Answer:
MD: 242 364 7480
Ps: TYSON all can come for talking about frieenship
Answer:
126 minutes
Step-by-step explanation:
The answer is B. “Using her last move”
He drank more than half of his drink... So we need to look for fractions greater than 1/2.
First, lets find equivalents of 1/2
If all of these fractions are equal to 1/2, then by adding to the numerator of these fractions, we'l have fractions greater than 1/2.
2 +1 3
__= ___
4 4
3/4 is greater than 1/2
Mike could have drunken 3/4 of the juice
4/6>1/2 Mike could have drunken 4/6 of the juice
5/8 is greater then 1/2 Mike could have drank 5/8 of the drink.
Answer=3/4, 4/6, 5/8 and many more possible fractions
Answer:
A. True
Step-by-step explanation:
Linear regression is "an analysis that assesses whether one or more predictor variables explain the dependent (criterion) variable. The regression has five key assumptions:
1) Linear relationship
: We need to check if the dependnet variable present a linear relationshipThe linearity assumption can best be tested with scatter plots in order to check if we have outliers in the data.
2) Multivariate normality
: "The linear regression analysis requires all variables to be multivariate normal". And we can check this with a histogram or a Q-Q-Plot, usually Normality can be checked with a goodness of fit test like the Kolmogorov-Smirnov test or Shapiro Wilks test.
3) No or little multicollinearity
: "Multicollinearity occurs when the independent variables are too highly correlated with each other". And we can check this with a correlation matrix, variance inflation factor (VIF's), correlation index and other statistics.
4) No auto-correlation
: The "Autocorrelation happens when the residuals are not independent from each other in the data". And usually we can test this with the Durbin-Watson test.
5) Homoscedasticity: MEans that we need constant variance for the linear model. The scatter plot is good way to check whether the data are homoscedastic. And we can interpret this condition as "that variance in the response variable is reasonably consistent across the range of an explanatory factor (otherwise known as homoscedasticity)"
So then the statement is TRUE.
