a) A researcher wants to find out what are the factors which determine the number of installs (I) of an application (app) from a famous app store. Size in Mbs (S), Reviews in *000s (Re), Ratings (0 to 5) (Ra), Price in 'Rs (P). She ran the following regressions: log I = 1.329 + 0.2356 S +0.4320 log(Ra) – 0.2678 P + 1.928 log(Re) Se = (0.63) R = 0.734 (0.242) (1.29) (0.001) (0.156) df = 156 %3D

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Q3 a) A researcher wants to find out what are the factors which determine the number of
installs (I) of an application (app) from a famous app store. Size in Mbs (S), Reviews in
*000s (Re), Ratings (0 to 5) (Ra), Price in 'Rs (P). She ran the following regressions:
log I = 1.329 + 0.2356 S+0.4320 log(Ra) – 0.2678 P + 1.928 log(Re)
Se = (0.63)
R? = 0.734
(0.242)
(1.29)
(0.001)
(0.156)
df = 156
i. Interpret the regression above.
ii. Test for statistical significance of Price in the model. Depending on the result do you
suggest that price is a significant factor affecting app installation?
iii. Suppose the regression is re-estimated where number of installs (I) varies only with
respect to price (P). Average I in sample is 5 and average P is Rs 8.9. Following
regression was estimated:
I = 52.351 + 3.139 -
se = (37.39) (0.0187)
df = 156, R? = 0.806
How would you interpret this model? Explain the shape of the curve.
iv. What would be the slope and elasticity of number of installs with reference to the
equation given in iii) above?
v. How would the equation in iii) change if we suggest that number of app installations
varies with respect to the kind of cellular phone used by the customer, that is android or
ios phones?
b) Will a dummy variable trap always exist if the number of dummies taken for a variable
is same as the number of categories of that variable?
c) Show that the coefficient of determination, R2, can also be obtained as the squared
correlation between actual Y values and the Y values estimated from the regression model
where Y is the dependent variable.
Transcribed Image Text:Q3 a) A researcher wants to find out what are the factors which determine the number of installs (I) of an application (app) from a famous app store. Size in Mbs (S), Reviews in *000s (Re), Ratings (0 to 5) (Ra), Price in 'Rs (P). She ran the following regressions: log I = 1.329 + 0.2356 S+0.4320 log(Ra) – 0.2678 P + 1.928 log(Re) Se = (0.63) R? = 0.734 (0.242) (1.29) (0.001) (0.156) df = 156 i. Interpret the regression above. ii. Test for statistical significance of Price in the model. Depending on the result do you suggest that price is a significant factor affecting app installation? iii. Suppose the regression is re-estimated where number of installs (I) varies only with respect to price (P). Average I in sample is 5 and average P is Rs 8.9. Following regression was estimated: I = 52.351 + 3.139 - se = (37.39) (0.0187) df = 156, R? = 0.806 How would you interpret this model? Explain the shape of the curve. iv. What would be the slope and elasticity of number of installs with reference to the equation given in iii) above? v. How would the equation in iii) change if we suggest that number of app installations varies with respect to the kind of cellular phone used by the customer, that is android or ios phones? b) Will a dummy variable trap always exist if the number of dummies taken for a variable is same as the number of categories of that variable? c) Show that the coefficient of determination, R2, can also be obtained as the squared correlation between actual Y values and the Y values estimated from the regression model where Y is the dependent variable.
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