3. Please answer the following questions: a. What criterion in regression are we trying to minimize when we estimate our regression model? a. b. Write down the population regression model. a. c. What are the assumptions we make when we estimate a regression model?

Regression : Impact of y on x ; Regression Function : y = a + b(x)

Optimum Least square criteria, with assumptions - linear parameters, homoscedasticity, no autocorrelation, no multicollinearity.

Step-by-step explanation:

Regression is a statistical tool used to calculate the impact of independent/ explanatory variable (x) on dependent variable (y). Eg : Impact of temperature on ice cream sales.

Population regression function is linear function, establishing a hypothetical theoretical relationship between independent variable x & dependent variable y.

Regression Equation is : y = a + b(x)  ;

where : a is the autonomous value of dependent variable y ; b represents the coefficient of change in dependent variable y due to change in independent variable x.

Optimum Least Square method criterion is used while estimating regression model. The criteria minimises the squares of residuals, where residuals are the differences between observed values & estimated values. It is used as a measure of how well the estimated regression line fits the actual data.

Assumptions made while an OLS regression model are : Linear in parameters, Homoscedasticity (variables constant errors), No autocorrelation (errors correlation), No multicollinearity ( independent variables' correlation)

step-by-step explanation:

given perimeter: 28cm

length: 9cm

atp,

2(l+w)=p of the reactangle

2(9+x)=28

18+2x=28

2x=28-18

x=10/2

x=5

therefore width is 5 cm