Answer:
- If i could spend the perfect day I would go to the park and take a jog. I would also go to Mc donalds and get a vanilla flavored ice cream! I love the taste of vanilla, Next, i will watch my favorite movie on my phone. Then, i will go to the zoo with my friends and see my favorite animals! Lastly,i will go home and take a nap.
Step-by-step explanation:
okay
14 - 2x = 4a - 16
First, add 2a to both sides. / Your problem should look like:
Second, simplify 4a - 16 + 2a to get 6a - 16. / Your problem should look like:
Third, add 16 to both sides. / Your problem should look like:
Fourth, add 14 + 16 to get 30. / Your problem should look like:
Fifth, divide both sides by 6. / Your problem should look like:
Sixth, 6 goes into 5 to get 30, so simplify
to 5. / Your problem should look like:
Seventh, switch your sides. / Your problem should look like:
Answer:
a = 5
Step-by-step explanation:
y = 10°
is the correct answer .
plz mark my answer as brainlist.
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.
