Answer:
substitution and income effects will counteract each other totally
Explanation:
A labor supply curve is an economic analysis tool that shows the number or workers that are available to work or that can work at various wage rates.
The labor supply curve can either be bending backwards or sloping downwards or upward curving but it shows the relationship between labour and wage rates.
A labor supply curve can be affected by factors such as population, changes in social behaviour, opportunities in other markets, among other things.
From the above question, it is seen that a change in wage rate for Anthony from $25 to $29 does not affect his work hours positively of negatively. His work hours is the same despite the increase in hourly wage.
The effect of the Anthony sticking to 40 hours of work despite an increase in wage, which could have served as some motivation for him to put in more hours is his labor curve remains same. An increase in wage has done noting to affect the number of hours he works and as such his income vs work rate counters each other.
Cheers.
Answer:
Saver's credit = $0
Explanation:
He is not entitled to any saver's credit, as he is not married and his AGI is greater than $32,000. Therefore the Saver's credit is equal to zero. Is also important to consider that Desmond is a head of a household and his AIG is between 31,126 and 48,000.
Smaller: -3, -4, -5, -6, -7.
bigger: -1, 0, 1, 2, 3
Answer:
Answer is the one which produces values which compare well with actual values based on a standard measure of error.
Explanation:
Exponential smoothing is one means of preparing short-term sales forecasts on a routine basis. To use exponential smoothing, however, one must decide the proper values for the smoothing constants in the forecasting model. One method for selecting the smoothing constants involves conducting a grid search to evaluate a wide range of possible values.
Exponential smoothing forecasting methods use constants that assign weights to current demand and previous forecasts to arrive at new forecasts. Their values influence the responsiveness of forecasts to actual demand and hence influence forecast error. Considerable effort has focused on finding the appropriate values to use.
One approach is to use smoothing constants that minimize some function of forecast error. Thus, in order to select the right constants for forecasting, different values are tried out on past time series, and the ones that minimize an error function like Mean Absolute Deviation (MAD) or Mean Squared Error (MSE) are the ones used for forecasting
