Answer:
E. cannot be negative
Explanation:
It is an opposite and cannot be negative if it is positive.
b. remember you still love your parents
c. constructively deal with your feelings
d. be angry with other people, but not your parents
In the United States, what percentage of marriages ends in divorce?
a. 10%
b. 25%
c. 50%
d. 75%
Answer:
The correct answer is D) and C) 50 %
Explanation:
First question
During a divorce the couple's son or daughter may experience turbulent emotions, and one of the best ways to deal with this is to have emotional intelligence during this process. Being angry with others but not with your parents is not ideal behavior, as other people are not responsible for the choices and actions they have taken. Don't blame yourself for divorce, remember that you still love your parents, and deal constructively with your emotions are ideal examples of what to do.
Second question
The Divorce rates in america is about 50%. This may vary slightly between states but the major reason for these separations is related to: Age, education level, location, sexuality, religion and mental health. Getting married too early is related to the possible lack of emotional maturity.
Answer: c) a leader
Explanation: :)
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6.2% (2014)
FROM PLATO
The estimated price elasticity of demand (PED) for coffee is -0.1647, which means that a 1% increase in the relative price of coffee leads to a 0.1647% decrease in the quantity demanded of coffee. .
The estimated income elasticity of demand (YED) for coffee is 0.5115, which means that a 1% increase in per capita personal disposable income leads to a 0.5115% increase in the quantity demanded of coffee.
The estimated cross-price elasticity of demand (CED) between tea and coffee is -0.0089, which means that a 1% increase in the relative price of tea leads to a 0.0089% decrease in the quantity demanded of coffee. The coefficient is small and statistically insignificant, suggesting that the price of tea does not have a significant effect on the consumption of coffee.
It is possible that advertising expenditure is omitted from the equation because it may be difficult to measure accurately or may not have a significant effect on coffee consumption. Alternatively, advertising may be captured in the error term of the equation.
The trend factor is included in the equation to capture the effect of time on coffee consumption that is not captured by the other variables in the equation. The coefficient on the trend factor is positive, indicating that coffee consumption has been increasing over time during the sample period.
The seasonal pattern in coffee consumption in the USA is captured by the dummy variables D1, D2, and D3, which take the value of 1 in the first, second, and third quarters, respectively, and 0 otherwise. The estimated coefficients on these variables suggest that coffee consumption is highest in the first quarter, lower in the second quarter, and lowest in the third quarter.
The R-squared value of 0.80 suggests that the model explains 80% of the variation in coffee consumption during the sample period. This is a relatively high R-squared value, indicating that the model fits the data well.
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