Answer:
At the most basic level, economics attempts to explain how and why we make the purchasing choices we do.
Explanation:
this was a answer from my school
(price of stock)Oc. start increasing
<span>Retired people will be draining the U.S. economy of wealth. This is due to the quality of workers that are in the current generations to keep contributing to the Social Security System. Social security checks is given back to those who have paid in their contributions and have since retired. To keep funds available for more generations, those currently working need to contribute to the system as well. However, that is getting harder for the current generations to keep up with by either not working or not paying taxes. </span>
Question Completion:
On December 31, 2014, Renda's common stock sold for $35 per share. At that price, how much did investors say $1 of the company's net income was worth? Earnings per share = $1.50
Answer:
Renda Company
The value of $1 of the company's net income by investors was:
$23.33
Explanation:
a) Data and Calculations:
Market price of Renda's common stock = $35 per share
Earnings per share = $1.50
This means that investors' value on $1 = $35/$1.50 = $23.33
b) Investors in Renda's common stock place a value of $23.33 for each $1 of the company's net income. This is why they can afford to pay $35 per share in order to benefit from $1 of the company's earnings. This calculation is based on the price-earnings ratio, which relates the company's share price to the earnings per share.
Multiplying the dependent variable by 100 and the explanatory variable by 100,000 leaves the OLS estimate of the slope the same.
Explanation:
Its because, The OLS slope coefficient calculators are not based on the weight.
In statistics ordinary least square (OLS), an estimate of uncertain parameters in the linear regression model is a linear least-square form. OLS is the highest likelihood estimator on the basis that errors naturally are distributed.
The OLS estimator is compatible when the regressors are exogenous and efficient when the errors are homoscedastic and not strongly associated within the class of linear unbiased estimators.
