Q3 a) A researcher wants to find out what are the factors which determine the number ofinstalls (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 = 156i. Interpret the regression above.ii. Test for statistical significance of Price in the model. Depending on the result do yousuggest that price is a significant factor affecting app installation?iii. Suppose the regression is re-estimated where number of installs (I) varies only withrespect to price (P). Average I in sample is 5 and average P is Rs 8.9. Followingregression was estimated:Î = 52.351 + 3.139 -1se = (37.39) (0.0187)df = 156, R? = 0.806How 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 theequation given in iii) above?v. How would the equation in iii) change if we suggest that number of app installationsvaries with respect to the kind of cellular phone used by the customer, that is android orios phones?

Given information: The regression output for the data is given.

OA 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 ir *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

OA 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 ir *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

College Algebra

1st Edition

ISBN:9781938168383

Author:Jay Abramson

Publisher:Jay Abramson

Chapter6: Exponential And Logarithmic Functions

Section6.8: Fitting Exponential Models To Data

Problem 3TI: Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to...

See similar textbooks

Related questions

Q: Based on the regression output, if a printer can print 4.8 pages per minute and copies in color,…

A: It is given that The reqression equation is Price = 238.3 + 26.8 Speed + 57.7 Color

Q: You were asked to help with analysis of birth weights (BW) of 10,000 infants born in NYC during a…

A: This question explores the relationship between the birth weights (BW) of 10,000 infants born in New…

Q: A study used logistic regression to determine characteristics associated with Y = whether a cancer…

A: Given: A study used logistic regression to determine characteristics associated with Y = whether a…

Q: Define Time-lagged regression analysis?

A: Introduction The time series data is consists of a sequence of data values indexed in time order.

Q: (a) How many bison will there be in 20 years? Round to the nearest whole number and include the…

Q: A virus, due to human eror, escaped a research facility in an isolated island. A day after the…

A: The regression equation is used to relate the response variable with one or more explanatory…

Q: Creating the app, they collected sample data and developed the following regression equation: where…

A: Given y = - 8.825 + 20.486x_{1} + 0.56x_{2} + 1.02x_{3}

Q: What does MS between reflects?

Q: According to this equation, what is the predicted value of the dependent variable when the…

Q: Interpret an R^2 value of 0.62 for a linear regression model where X is the independent variable and…

A: Coefficient of determination, R2 = 0.62

Q: make a regression analysis about any healthcare issue

A: Let's perform a simple regression analysis on a healthcare issue. We'll analyze the relationship…

Q: What is regression models?

A: To define Regression model:

Q: A regression was run to determine if there is a relationship between hours of TV watched per day (x)…

A: From the given information, the regression is y=20.094-0.759(x).

Q: In a regression model if you drop one insignificant variable then a. R ^ 2 will increase and SSE…

A: Coefficient of determination: It is the proportion of variation in the dependent variable which is…

Q: I am doing as you instructed, but I keep getting an error "regression - input range non-numeric…

A: Please find the explanation below.

Q: Suppose that real income per capita in New Jersey increases by 1% in the next year. Use the results…

A: Introduction: We need to predict the amount of change in the dependent variable, the number of…

Q: A well-balanced stock market portfolio will often experience an exponential growth. A particular…

A: Given , lnbalance=5.550+0.052t t=2 years=24 months

Q: Given that the systolic blood pressure in the right arm is 90 mm Hg, the best systolic blood…

Q: The regression analysis shows that r^2 = 0.4688. What would the correlation be? A. r = √0.4688 B. r…

A: Given , r^2 = 0.4688 Correlation coefficient (r)=?

Q: A regression was run to determine if there is a relationship between hours of study per week (X) and…

A: there is a relationship between hours of study per week (X) and the test scores (y ) a=5.931 b=20.1…

Q: Explain what a residual is and how this relates to the best-fit regression model.

A: A residual is the difference between the observed value of the dependent variable and the value…

Q: A frequent flyer was interested in the relationship between dollars spent on flying and the distance…

Q: Please define (Gauss Markov) MLR 4, state if it is likely to hold or not in this case.

A: The multiple linear regression equation is, y= β0 +β1 × x1 + β2 × x2 + β3 × x3 + .....+ βn × xn…

Concept explainers

Correlation

Correlation defines a relationship between two independent variables. It tells the degree to which variables move in relation to each other. When two sets of data are related to each other, there is a correlation between them.

Linear Correlation

A correlation is used to determine the relationships between numerical and categorical variables. In other words, it is an indicator of how things are connected to one another. The correlation analysis is the study of how variables are related.

Regression Analysis

Regression analysis is a statistical method in which it estimates the relationship between a dependent variable and one or more independent variable. In simple terms dependent variable is called as outcome variable and independent variable is called as predictors. Regression analysis is one of the methods to find the trends in data. The independent variable used in Regression analysis is named Predictor variable. It offers data of an associated dependent variable regarding a particular outcome.

Question

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:
Î = 52.351 + 3.139 -
1
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?

Expert Solution

This question has been solved!

Explore an expertly crafted, step-by-step solution for a thorough understanding of key concepts.

SEE SOLUTION Check out a sample Q&A here

Step 1

Step 2

Step 3

Step 4

Step by step

Solved in 4 steps with 1 images

SEE SOLUTION Check out a sample Q&A here

Knowledge Booster

$Correlation, Regression, and Association$

Learn more about

Need a deep-dive on the concept behind this application? Look no further. Learn more about this topic, statistics and related others by exploring similar questions and additional content below.

Similar questions

What does the y -intercept on the graph of a logistic equation correspond to for a population modeled by that equation?
Table 6 shows the population, in thousands, of harbor seals in the Wadden Sea over the years 1997 to 2012. a. Let x represent time in years starting with x=0 for the year 1997. Let y represent the number of seals in thousands. Use logistic regression to fit a model to these data. b. Use the model to predict the seal population for the year 2020. c. To the nearest whole number, what is the limiting value of this model?
Can the average rate of change of a function be constant?
Find the mean hourly cost when the cell phone described above is used for 240 minutes.
What is the y -intercept on the graph of the logistic model given in the previous exercise?
A popular television show recently released a video preview for the upcoming season on its website. As fans of the show discover the video, the number of views of the preview video has grown each day during the last 2 weeks. The number of days since the release of the video and the natural log of the number of video views is shown in the scatterplot. Based on the scatterplot and residual plot, what type of model would be appropriate for summarizing the relationship between days and number of views? An exponential model is appropriate because the residual plot does not show a clear pattern. A linear model is appropriate because the scatterplot shows a strong, positive linear relationship. A power model is appropriate because the scatterplot of days and natural log of views is strong and linear. An exponential model is appropriate because the relationship between days and the natural log of views is linear, and the residual plot does not show a clear pattern.
Imagine a regression with YEAR (as in 2015 or 2001) predicting SALES (in thousands of dollars) as so: SALES = 100 + 0.5*YEAR And YEAR is statistically significant. What should you predict SALES to be in 2020? (Express your answer as SALES does: in thousands of dollars.)
Why is the data lognormal
Question is in the photo attached.
You were asked to help with analysis of birth weights (BW) of 10,000 infants born in NYC during a certain period of time. The aim of the analysis is to see whether the birth weights of the infants are associated with mothers AGE at birth (continuous variable in years) and mothers smoking status (the maternal smoking status MSS contains 4 categories “Non-smoker”, “Past-smoker”, “Passive-smoker”, “Smoker”) and NYC boroughs (the BOROUGH variable contains 5 categories “Manhattan”, “Bronx”, “Brooklyn”, “Queens” and “State Island”). Write down the population model that assumes the effect of mother’s AGE at birth on infant weight BW is DIFFERENT for different levels of maternal smoking status (MSS) when adjusted for BOROUGH variable. Specify the name of each predictor in the model and/or what it means. Make sure that it is clear what each predictor means.
You were asked to help with analysis of birth weights (BW) of 10,000 infants born in NYC during a certain period of time. The aim of the analysis is to see whether the birth weights of the infants are associated with mothers AGE at birth (continuous variable in years) and mothers smoking status (the maternal smoking status MSS contains 4 categories “Non-smoker”, “Past-smoker”, “Passive-smoker”, “Smoker”) and NYC boroughs (the BOROUGH variable contains 5 categories “Manhattan”, “Bronx”, “Brooklyn”, “Queens” and “Staten Island”). Questions: 1.Write down the population model that estimates BW based on variables AGE and BOROUGH(with the dummy variables). Make sure that it is clear what each predictor means. 2.How many parallel lines are computed by model from population model you created above? 3.Write down the population model that estimates BW based on variables AGE, MSS(with their dummy variables) and BOROUGH(with their dummy variables). Also write down the number of parrallel lines.…
You were asked to help with analysis of birth weights (BW) of 10,000 infants born in NYC during a certain period of time. The aim of the analysis is to see whether the birth weights of the infants are associated with mothers AGE at birth (continuous variable in years) and mothers smoking status (the maternal smoking status MSS contains 4 categories “Non-smoker”, “Past-smoker”, “Passive-smoker”, “Smoker”) and NYC boroughs (the BOROUGH variable contains 5 categories “Manhattan”, “Bronx”, “Brooklyn”, “Queens” and “Staten Island”). Questions: 1) How many dummy variables do you need to create to analyze the effect of MSS? Select “Non-smokers” as a reference category. Write out all the dummy variables. 2)Write down the population model that estimates BW based on variables AGE and MSS. Make sure that it is clear what each predictor means. 3)How many parallel lines are computed by model from population model?