F1FMPM2

Using following reports analyse statements of Will, Jack and Smith
Equation R2 R2 Adjusted n
Sale X = 25 + 0.25 NB 58% 57% 100
Sale Y = 5+0.35 MG + 0.75 PCI 62% 52% 60

Based on given data
1) Will says correlation of Sale X and NB is 0.75, Sale y and MG or PCI cannot be determined
2) Jack says Correlation of X and NB is 0.25, Y and MG is 0.35 and Y ad PCI is 0.75
3) Smith Says correlation of X and NB is 0.76, Y and MG is 0.787 and Y and PCI is 0.72.
Who is making a correct statement?For linear regression with one variable correlation between dependent and independent can
be determined by Root of R2 i.e. the root of 0.58. Same cannot be done for multiple regression.

Consider the regression equation
Sale Y = 5 + 0.35 MG + 0.75 PCI
R a 2 = 47%
R 2 = 52%

Find out correlation between dependent and independent variable.

Find out a coefficient of determination based on given data.
Total Variation = 653
Unexplained variation = 196
Sum of squared residual = 625

Which one of the following is not assumption multiple linear regression.
1 A linear relationship exists between the dependent and independent variables
2 Independent variables are random
3. There is an exact linear relationship between any two or more independent variable.
4. Variance of the error term is zero for all observations

Analyse the following statements which are or are incorrect
1 Multicollinearity is a condition when independent variables or a linear combination of it is highly
correlated
2 Perfect multicollinearity exhibited by the model when two independent variables are perfectly
correlated like the correlation of +
3 Imperfect multicollinearity exhibited by the model when two independent variables are negative
correlated i.e. or less than
4 Dummy variable trap is when all dummy variables are included in the linear regression equation.

Arav working as an associated in an investment firm recently joined FRM curriculum and new
to regression analysis, wants to apply in his daily work. He learned R 2 is the coefficient of
determination is explanatory power of variables combined. He decides to add on variables to
increase the explanatory power of the equation. At what point Arav Should stop adding variables in
a given structure.
Variable           R2             R2 adjusted
X1              32%                 24%
X2              38%                20%
X3              55%                33%
X4              95%                91%
X5              95%                89%

Relationship of stock ABC Ltd listed in NASDAQ is defined by Y=B 0 + B 1 X 1 where Y is stock price
and X is a market index in the beginning of 2018 NASDAQ was at 7000 points and ABC Ltd was
trading at $190. By the year-end NASDAQ is out of 7600 and ABC is trading at $215. What is B1
regression line?

Using the same case mentioned in question 1 what is the intercept.

Which one of the following statement correctly states the meaning of intercept
A Intercept coefficient is change in independent variable per unit change in dependent variable
B Intercept is expected value when the slope is 0
C Intercept is expected value setting a dependent variable to 0
D Intercept has expected value assuming explanatory variable = 0

Which one of the following statement is correct regarding error term

Omitting relevant variable in OLS regression can produce biased results. Variable bias is present
when few conditions are not met, which one of the following condition/s is/are not relav3ent for
omitted variable bias.
Omitted variable bias is
1 Correlated with the movement of the independent variable in the regression model
2 Correlated with the movement of the dependent variable in the regression model
3 determinants of the dependent variable
4 Determinants of an independent variable

Which one of the following statement/s are correct.
Consider equation Y i = B 0 + B 1 X 1 + B 2 X 2 + e i

1. Intercept is the value of the dependent variable when all coefficient of the independent
variable is equal to zero
2. Intercept is the value of the dependent variable when all independent variable is set to zero
3. Slopes (B1, B2) indicate estimated changes in dependent variable for one unit change in
independent variable setting other variables to zero
4. Slopes (B1,B2) indicates estimated changes in dependent variable for one unit change in the
independent variable holding other variable constant

Using the following information find out the standard deviation of the predicted value of
Ŷ
– Three Variables in a regression equation with 350 SSR and R2 = 52% analyst made 50
telephonic calls to gather data, which is in the form of 10 questions questioner.
(Note: Question may seem to be incomplete but enough information is given to calculate
standard deviation. Space for speculation is intentionally left so you can also understand how to
handle speculation in an exam)